Academic Model Answers Library — Megaminds (UK Postgraduate)

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Global Supply Chain Management 1,800 words

Suez Canal Blockage: A Case Study of Global Supply Chain Disruption

This individual report examines the challenges faced by global supply chains as a result of the Suez Canal blockage and critically evaluates the factors that affected supply chain performance during the disruption. The assignment uses the Suez Canal blockage as a case study to explore the wider principles and practices of global supply chain management, with particular attention to supply chain disruption, risk management, resilience, logistics, supplier relationships and integrated supply chain performance. The report requires students to conduct preliminary research into the Suez Canal blockage and use relevant academic and practical sources to identify the major challenges created by the disruption. The case study focuses on the six-day stranding of the Ever Given and the resulting disruption to international supply chains and global trade. The assessment considers how a disruption at a strategically important global trade route can create wider consequences for organisations, logistics networks and supply chain operations. The analysis should identify the key factors that influenced supply chain performance and critically evaluate the effectiveness of the solutions implemented to resolve the disruption. Relevant global supply chain management theories, models and concepts should be clearly defined and applied to the case rather than simply described. The report should demonstrate an understanding of fundamental supply chain principles and consider how evolving trends and global issues can affect supply chain operations. The assignment also encourages consideration of important supply chain management areas covered within the unit, including risk management, resilience, outsourcing, supplier planning and selection, relationship management, supply chain integration, demand management, order management and customer service. These concepts should be connected to the Suez Canal blockage to provide a critical assessment of the challenges and possible approaches to improving supply chain resilience. The report must be presented in a formal and logically coherent report format and should provide evidence of critical reasoning through a wide range of relevant academic and practical sources. The conclusion should draw together the main findings, while recommendations should propose realistic approaches for addressing the supply chain issues identified in the case. The assessment is worth 30% of the overall unit mark and requires an individual report of 1,800 words, with a permitted variation of ±10%. The report is assessed across four main areas: use of theory and frameworks (25%), analysis and evaluation (30%), conclusions and recommendations (20%), and structure, presentation and referencing (25%). The marking criteria emphasise the application of relevant theoretical concepts, critical evaluation of academic and practical sources, a clear connection between theory and practice, realistic recommendations, logical structure, and appropriate in-text citations and referencing.

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Network Systems and Administration 2,500 words

Automated Container Deployment and Administration in the Cloud

This assessment focuses on the practical application of network systems administration, cloud infrastructure and automation technologies. The assignment requires students to design and implement an automated deployment process for a server running a Docker container on a cloud platform such as AWS, Azure or GCP. The assessment develops practical understanding of infrastructure automation, configuration management, containerisation and continuous integration and continuous deployment (CI/CD). The project requires the integration of at least two automation tools from technologies such as Terraform, AWS CloudFormation, Ansible, Azure DevOps, GitHub and GitHub Actions. The first stage involves provisioning the required cloud infrastructure, including a server instance, networking components and appropriate security configurations. Students must provide Terraform scripts, CloudFormation templates or equivalent infrastructure-as-code configurations together with an architecture diagram showing the deployed resources. The second stage focuses on configuration management. Ansible or an equivalent automation tool is used to configure the server environment, including installing and configuring Docker and ensuring that the required services start automatically. Students must provide the relevant playbooks or configuration scripts and a README explaining the overall automation flow. The assessment then requires students to containerise a sample web application using a Dockerfile and automate the deployment of the resulting Docker container to the provisioned cloud server. A CI/CD pipeline must also be implemented using Azure DevOps or another suitable CI/CD platform. The pipeline should automatically build and deploy the Docker container whenever changes are pushed to a version-control repository such as GitHub. The final stage requires comprehensive technical documentation and reflection on the complete automation process. Students must document the different phases of deployment, explain the rationale for the selected tools, discuss alternative solutions and identify the challenges encountered and how they were addressed. The final report must include a detailed architecture diagram, a GitHub repository containing the required scripts and configurations, and a working link to a demonstration video of the end-to-end deployment. The technical report should be approximately 2,000–2,500 words and include a title page, summary, introduction, main content, conclusions, references and appropriate appendices. The report should use Times New Roman 12-point font with 1.5 line spacing, clear headings, diagrams or flowcharts where appropriate, and Harvard referencing. The assessment is evaluated through the demonstration, report quality and reusable deployment artifacts, with the marking scheme allocating 40% to the demo, 50% to report quality and 10% to artifacts.

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International Human Resource Management 800 words

A Comparative Analysis of Cultural Factors Shaping Human Resource Staffing Strategies in International Enterprises

This assignment presents a comparative analysis of the cultural and institutional factors that influence human resource staffing and recruitment strategies in international enterprises. The case study focuses on VetDopomoga, a Ukraine-based international veterinary clinic chain that is considering international expansion into either the United Kingdom or India. The assignment requires an assessment of the potential benefits and challenges associated with operating in each market and the development of evidence-based recommendations for the organisation's international expansion. The poster begins by comparing the cultural and institutional environments of Ukraine, the United Kingdom and India. The analysis considers factors such as communication styles, hierarchy and power distance, individualism and collectivism, time orientation, labour-market characteristics and the regulatory and employment context. These factors are considered in relation to their potential influence on human resource practices, particularly recruitment. The assignment also requires the application of an international HR staffing strategy approach. Students must consider ethnocentric, polycentric, regiocentric and geocentric approaches and use their analysis to recommend which of the two potential markets VetDopomoga should enter first and which should be considered as the second market. The recommendation must be supported by evidence and clear justification. A cultural theory must also be selected and applied to analyse two relevant cultural dimensions. The available theoretical approaches include the Cultural Intelligence framework, Erin Meyer's Culture Map, the GLOBE Project and Hofstede's Cultural Dimensions Theory. The selected theory is used to examine the benefits and challenges of recruitment practices in Ukraine, the United Kingdom and India. The assignment further requires consideration of one major recent cultural, economic or political event affecting each country and an evaluation of how these developments may influence recruitment and international HR practices. Students must research best-practice recruitment examples from international veterinary enterprises operating in the UK and India and identify practices that VetDopomoga could adopt or adapt. Finally, the poster must provide a clear recommendation regarding the order of market expansion and identify three supporting recommendations. Visual elements such as diagrams, icons and charts should be used to communicate the findings clearly and concisely. The final poster should be no more than two pages and approximately 800 words, excluding illustrations, with appropriate Harvard in-text citations and a full reference list.

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Advanced Artificial Intelligence

CW1: Applying advanced AI methods for analysing text documents

This coursework is an individual practical assessment for the Advanced Artificial Intelligence module, focusing on the application of advanced artificial intelligence, natural language processing (NLP), natural language understanding (NLU), and neural network techniques to analyse text documents. The coursework uses a provided social-media dataset containing more than 89,000 posts associated with 947 news headlines. Each social media post is treated as an individual text document and is labelled as either real or fake according to the relationship between the linked news headline, its ground-truth status, and majority annotator agreement. The assessment consists of two main tasks. The first task focuses on the identification of fake text documents. Students are required to perform descriptive data analysis and NLP preprocessing before transforming text into numerical feature representations suitable for neural network classification. Students must design and implement both a Multi-Layer Perceptron (MLP) network and a deep learning neural network to classify documents as real or fake. The assessment requires justification of the selected architectures, including inputs, layers, neurons, activation functions and outputs. Students must also experiment with hyperparameters, evaluate model performance using appropriate metrics, compare different models, select suitable models, and save trained models for later demonstration. The second task focuses on topic discovery using natural language understanding techniques. Students are required to apply text-processing methods such as tokenisation, stop-word removal, lemmatisation or stemming and experiment with at least two different text representation strategies. Possible approaches include Bag of Words, TF-IDF, Latent Dirichlet Allocation, word vectors and word embeddings. The discovered topics must be analysed and interpreted in relation to the document content, associated news headlines, and class labels. The assessment is supported by a practical bench demonstration and a maximum of seven PowerPoint slides covering the design, model improvement process, performance evaluation and discussion of results. Students must submit their own Python code and presentation through Blackboard and demonstrate their saved models without retraining them during the demonstration. The marking scheme allocates 45% to fake text document identification, 35% to topic discovery using NLU, and 20% to the structure, organisation, professionalism and question-and-answer performance of the demonstration.

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Security of Connected Systems 4,500 words

CW: Security Evaluation

This assignment is an individual technical report focused on the security evaluation and strategic development of a rapidly expanding Internet of Things (IoT) systems developer based in Coventry. The client specialises in innovative IoT devices and systems for residential and commercial applications, particularly smart energy monitoring, and is planning to scale its operations and expand into new markets. The report is designed to provide practical and strategic guidance on how the organisation can achieve this growth while maintaining strong cybersecurity. The assignment requires students to act as an external IoT Security and Scaling Strategist and critically evaluate the organisation’s current and future security needs. The report covers four principal areas: organisational change strategy, secure design and development strategy, security audit strategy, and security recommendations. The organisational change section considers internal and external factors influencing growth, the development and implementation of an organisational strategy, monitoring against objectives, and approaches to leading and managing strategic change using relevant change management theories and models. The secure design and development section requires an overview and evaluation of possible secure design processes for IoT systems, including their strengths and weaknesses, followed by a recommendation for an appropriate process. The security audit section examines methods such as PTES and OWASP and develops a guide for conducting a security audit, including the testing methodology, rationale for each stage, and evaluation of different approaches. The security recommendations section requires a case study of an IoT security vulnerability, examination of where the security flaw was introduced, assessment of weaknesses in the secure design or audit process, consideration of the resulting security impact, and recommendations for improvement. The report should be written for a technical audience, particularly the client’s software development team, and should use appropriate structure, technical language, diagrams where useful, and APA referencing. The organisational strategy and strategic change proposal should be included in appendices and referenced within the main report. The assessment is worth 30 credits and has a maximum word count of 4,500 words, with the main sections weighted across organisational change, secure design, security auditing, security recommendations, and report structure.

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International Business Strategy 250 words

Individual Reflection on Case Study Discussion – International Business Strategy

This assignment is an individual reflective assessment for the International Business Strategy module. It focuses on developing students’ ability to critically reflect on their learning experiences and identify how learning from classroom discussions can improve their future academic and professional performance. The assessment is based on a Case Study Discussion undertaken during one of the teaching weeks from Week 5 to Week 8. Students are required to select one of the case studies discussed during the relevant seminar sessions and reflect on their learning from the discussion. The case-study discussions provide an opportunity to explore important issues in the business world, apply concepts introduced through Canvas preparation and face-to-face teaching, and exchange ideas with tutors and peers. The reflection therefore connects theoretical knowledge with practical business situations and encourages students to consider how strategic management concepts can be applied to real-world organisations. The reflective task requires students to explain the topic and case study discussed during their selected week, identify what they learned from the discussion, and consider how the experience helped them develop their ability to apply theories to their chosen company. Students should also reflect on their key takeaways, what went well during the discussion, challenges they encountered, and what they would change if they were to repeat the experience. The reflection should be between 150 and 250 words and should use an appropriate reflective framework, such as Gibbs' Reflective Cycle or Kolb’s Experiential Learning Cycle, to structure the discussion. Harvard referencing should be used where applicable. The assessment develops critical reflection, strategic thinking, international business understanding and the ability to connect academic concepts with practical business cases. The assessment is worth 15% of the module and is submitted through Canvas. The marking criteria consider the quality of reflection, critical analysis, personal learning insights, presentation and structure, intellectual curiosity, referencing, content and discussion.

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Time Series Modelling

Time Series Modelling Case Study – Oil Price Forecasting

This individual Time Series Modelling Case Study focuses on analysing and forecasting oil price data using established time-series techniques and an alternative modelling approach. The assessment requires students to work with daily oil price information covering the period from 2024 to 2026 and investigate the underlying patterns, stationarity and forecasting behaviour of the data. The first part of the assignment involves exploratory data analysis and time-series modelling using an ARMA-based approach. Students are required to create appropriate visualisations of the data, perform exploratory analysis and conduct tests for non-stationarity, including relevant stationarity diagnostics such as ADF, ACF and PACF analysis and differencing where required. An ARMA model must then be defined, with suitable model parameters identified using the AIC likelihood approach. The assessment requires the student to examine possible combinations of model parameters, assess model residuals, evaluate model performance using appropriate metrics such as RMSE, and produce forecasts extending 24 months into the future. Confidence intervals must also be included with the forecasts. The second part requires students to investigate an alternative modelling solution for the same oil-price time-series data. Possible approaches discussed in the assignment include models such as LSTM and Prophet. Students are expected to conduct a literature review relating to the selected alternative model, build and apply the model, tune relevant hyperparameters where appropriate, generate 24-month forecasts, create suitable visualisations and calculate appropriate evaluation metrics. The final part of the assessment requires a 6–8 page report describing the modelling process, forecasts, analysis and inferences. The report should explain the reasoning behind the analytical and modelling choices rather than simply presenting numerical results. Students are expected to critically discuss why particular approaches were selected, how the modelling decisions may have influenced the results, how forecasts compare with subsequently collected real data where available, and what improvements could be made in future work. The assessment evaluates both the technical implementation and the quality of the written analysis. The code component assesses completion of the modelling and forecasting tasks, stationarity testing, the alternative solution, code quality and annotation. The report component assesses discussion of the analysis and inferences, comparison of the modelling approaches, clarity of interpretation, report structure, appropriate use of figures and suitable academic references. The submission consists of a report in PDF or Word format, with the code submitted separately or through an appropriate repository.

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Computational Algorithms and Paradigms 1,000 words

Computational Algorithm Analysis – Research Paper Algorithm

This individual coursework for the Computational Algorithms and Paradigms module requires students to thoroughly analyse a computational algorithm proposed in a research paper selected from the list of research papers provided on Canvas. The purpose of the assessment is to develop students' ability to understand, explain and critically evaluate computational algorithms presented in academic research. Students must first identify and describe the computational problem addressed by the selected research paper and clearly state the research questions investigated by the authors. They must then extract the main computational algorithm proposed in the paper and present it in pseudocode. The assessment also requires students to clearly identify the inputs required by the algorithm and the outputs produced by it. The coursework consists of six main analytical sections. The first section focuses on the computational problem and research questions addressed in the selected paper. Students are expected to provide an accurate description of the problem and explain the research questions that the proposed algorithm attempts to address. The second section requires the proposed computational algorithm to be represented using suitable pseudocode. The third section identifies and explains the algorithm's inputs and outputs. The fourth section requires students to explain the proposed algorithm using simple and understandable language. The explanation should demonstrate a clear understanding of how the algorithm operates rather than simply reproducing the description provided in the research paper. The fifth section focuses on analysing the time complexity of the proposed algorithm. Students should evaluate the computational cost of the algorithm and explain its time complexity appropriately. The final section requires a critical evaluation of the algorithm's strengths and weaknesses. Students should identify the advantages and limitations of the proposed approach and discuss potential improvements where appropriate. This section should demonstrate critical thinking about the effectiveness, efficiency and practical applicability of the algorithm. The coursework has a total word count requirement of 800–1,000 words. The inputs and outputs, pseudocode and time-complexity analysis sections are excluded from this word-count limit. The template requires approximately 250 words for the computational problem and research questions, approximately 250 words for the simple explanation of the algorithm, and approximately 300 words for the strengths and weaknesses evaluation. Students must report the word count for sections 1, 4 and 6 after completing the assignment. The submitted work must be original and is subject to plagiarism and collusion checks through Turnitin. The assessment brief also states that generative AI tools may be used for proofreading but are not permitted for creating the coursework content. No figures or images are permitted, and the coursework must be submitted using the provided Word template in DOC or DOCX format.

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Computing Science

Writing a Literature Review in Computing Science

This individual assessment requires students to write a concise literature review on a selected topic within computing science. The review should be no more than six pages in length and should be written in a style appropriate for a general computing science audience. The assignment is designed to demonstrate technical knowledge, independent learning, effective written communication and professionalism in producing a concise technical document. Students must select a research topic from one of five permitted areas: algorithmic bias and fairness, a data science application, quantum computing, the Internet of Things (IoT), or the use of artificial intelligence in cybersecurity. Possible topics include how algorithms can discriminate and techniques for detecting and correcting algorithmic bias, applications of data science in areas such as agriculture or healthcare, quantum computing algorithms and hardware, technical IoT problems and potential solutions, and the use of AI for cybersecurity threat detection and prevention. The literature review must contain several required components. The first page should contain only the title, student number, abstract and statement of AI usage. The abstract must provide a concise overview of the review and must not exceed 200 words. The report should also include an introduction that provides broad background information before narrowing the discussion to the selected research topic. The introduction should explain why the topic is important and provide relevant context and examples of applications. Students are expected to review a range of relevant literature, including theories, methods, techniques, ethical concerns or tools where appropriate. The selected literature should not simply be described individually; instead, students must synthesise the sources to identify important themes, findings and areas of interest and provide a critical review of the literature. The conclusion should summarise the main findings, identify open issues and discuss possible future directions. The assessment must include a bibliography with accurate and up-to-date references formatted using Harvard style. The final document may be prepared in LaTeX or Word but must use one of the provided templates and be submitted as a PDF through Blackboard. The complete review, including figures and bibliography, must not exceed six pages. The assessment is marked according to structure, sources and their description, synthesis and critical review, and presentation.

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Calculus and Optimisation: Python Implementation of Gradient Descent, Derivatives and Polynomial Regression

This Calculus and Optimisation technical exercise demonstrates the implementation of mathematical and machine-learning concepts using Python. The document is organised as a set of code fragments that must logically work together to perform symbolic calculus, numerical optimisation, physical modelling and polynomial regression. The material uses libraries including NumPy, SymPy, Matplotlib and Scikit-learn, linking mathematical theory with practical computational implementation. Jigsaw_Puzzle_Calculus_Original… A central component is the implementation of gradient descent for linear regression. The supplied function calculates predictions, evaluates prediction errors, computes gradients and updates model parameters iteratively using a configurable learning rate. Mean squared error is recorded during optimisation, and a tolerance-based stopping condition is used to terminate the algorithm when successive cost values change by only a very small amount. Jigsaw_Puzzle_Calculus_Original… Synthetic linear-regression data is generated using a fixed NumPy random seed to support reproducible experimentation. A bias column is added to the feature matrix, initial parameter values are randomly generated, and the custom gradient-descent function is then executed to estimate the intercept and slope of the relationship. Jigsaw_Puzzle_Calculus_Original… The document also demonstrates symbolic differentiation and optimisation using SymPy. A cubic polynomial is defined and differentiated to obtain both its first and second derivatives. Critical points are identified by solving where the first derivative equals zero, while the second derivative is evaluated at each critical point to determine whether the point represents a local minimum, local maximum or saddle point. Jigsaw_Puzzle_Calculus_Original… Jigsaw_Puzzle_Calculus_Original… A further section applies mathematical formulas to projectile motion. Using a specified initial velocity, launch angle and gravitational acceleration, the code calculates both maximum projectile height and horizontal range. This component illustrates how calculus-related mathematical relationships can be translated directly into executable computational models. Jigsaw_Puzzle_Calculus_Original… The final major element explores polynomial regression. Synthetic nonlinear data is generated from a cosine-based function with added random noise. Scikit-learn pipelines are then used to compare polynomial models of degrees 1, 4 and 15. The models are fitted to the synthetic dataset and visualised against the underlying true function, allowing comparison of model complexity and illustrating concepts such as underfitting and overfitting. Jigsaw_Puzzle_Calculus_Original… Jigsaw_Puzzle_Calculus_Original… Overall, the exercise integrates calculus, optimisation, numerical methods and machine-learning modelling through Python. It provides practical experience with differentiation, critical-point analysis, iterative optimisation, mathematical simulation, regression modelling and visualisation. Important: because this file does not identify a university, assessment weighting, academic level, reference style or required word count, those fields should remain Not specified / Not applicable rather than being invented.

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Critical Analysis of Computational Algorithms: Research Paper Evaluation and Complexity Analysis

This postgraduate Computer Science coursework requires students to undertake a critical technical analysis of a computational algorithm presented in a prescribed academic research paper. Students select one paper from the available options and demonstrate that they understand both the research problem addressed by the authors and the algorithmic solution proposed. The assessment contributes 30% of the overall module grade and is completed individually. assignment The available research papers cover several algorithmic topics, including an improved Dijkstra shortest-path algorithm for sparse networks, a modified merge-sort approach for large-scale datasets, parallel merge sort with load balancing, and a Prim-based algorithm for hierarchical clustering. Students must extract the principal algorithm from their selected paper and explain its purpose, inputs, outputs and operating procedure. A major component involves identifying the research question and computational problem addressed by the selected study. Students then reproduce or extract the proposed algorithm in pseudocode form and clearly identify the information supplied to the algorithm and the outputs it generates. The algorithm must also be explained step by step using straightforward language so that its operation can be understood without relying exclusively on formal notation. The coursework further requires a detailed time-complexity analysis, demonstrating understanding of how computational requirements grow with input size and how the proposed technique compares with alternative or conventional approaches. Students must critically evaluate the algorithm’s strengths, weaknesses, performance characteristics and limitations, and suggest potential improvements where appropriate. The marking rubric gives substantial emphasis to five areas: identifying the computational problem and research questions, extracting the proposed algorithm, identifying inputs and outputs, explaining the algorithm clearly, analysing its time complexity, and critically evaluating its strengths and weaknesses. assignment The written submission must be 800–1,000 words, although the inputs/outputs, pseudocode and time-complexity sections are excluded from that limit. Figures and images are not permitted, and the work must be submitted using the prescribed coursework template in DOC/DOCX format. Overview word count: approximately 340 words. AI-use note: the guideline permits generative AI only for proofreading. AI tools are explicitly not permitted to create the assessed work itself. assignment

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Artificial Intelligence / Natural Language Processing / Deep Learning

Applying Advanced AI Methods for Analysing Text Documents

This Advanced Artificial Intelligence coursework requires students to implement and evaluate natural language processing, natural language understanding and neural-network techniques for analysing text documents. The assessment uses a supplied social-media dataset containing more than 89,000 posts linked to 947 news headlines, with each post labelled as real or fake through a combination of headline ground truth and majority-vote annotation. Each social-media post is treated as an individual text document for classification and topic-analysis purposes. CMP_6059B_7059B_2025_26_CW1-pre… The first major task focuses on identifying fake text documents. Students must preprocess the text using appropriate NLP techniques, transform documents into numerical feature representations and experiment with alternative preprocessing approaches to determine which performs best. A separate unseen test set must be reserved to evaluate generalisation, while the remaining data is used for training both shallow and deep neural-network classifiers. Students are expected to explain and justify how the data is split. CMP_6059B_7059B_2025_26_CW1-pre… Students first design a multi-layer perceptron (MLP) capable of predicting whether documents are real or fake. The architecture must be justified in terms of input dimensions, number of layers, neuron counts, activation functions and outputs. A second deep-learning neural network must then be developed for the same classification problem, with justification of the chosen network structure, layer types, activations and other configuration decisions. CMP_6059B_7059B_2025_26_CW1-pre… For both networks, students train baseline models and select three hyperparameters considered most important for improving performance. These hyperparameters must be tuned systematically, with visuals prepared to show the experimentation process and resulting performance changes. Appropriate evaluation metrics are then used to compare the trained models. The strongest MLP and deep-learning models must be saved so that they can be loaded and tested on unseen data during the final demonstration without retraining. CMP_6059B_7059B_2025_26_CW1-pre… The second task focuses on topic discovery using NLP and NLU techniques. Students perform syntactic preprocessing such as tokenisation, stop-word removal and lemmatisation or stemming, and experiment with at least two different text-representation approaches. Suggested methods include Bag of Words, TF-IDF, LDA, word vectors and word embeddings. Students must interpret the discovered topics and explain how those topics relate to document content, linked news headlines and class labels. The best topic-discovery model or models must also be saved for live analysis during the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… The assessment is completed through a bench demonstration, supported by a maximum of seven PowerPoint slides. The slides should document the system design, model-improvement process, performance evaluation and discussion of results for both fake-document classification and topic discovery. Students also submit a ZIP file containing only their Python source files. The demonstration lasts up to 15 minutes, consisting of approximately 10 minutes for presentation and technical demonstration followed by 5 minutes for questions and transitions. CMP_6059B_7059B_2025_26_CW1-pre… The marking scheme allocates 45% to fake-document identification, including descriptive analysis, preprocessing, MLP design and deep-learning design; 35% to topic discovery, including preprocessing, model development and interpretation; and 20% to the structure, organisation, professionalism and Q&A quality of the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… Important for the public Reference Library: the brief explicitly states that the use of Large Language Models or generative AI to produce any part of the submission is strictly prohibited, including code, data processing, testing, writing or PowerPoint content. Therefore, this entry should remain only a high-level public description of the assessment and should not be presented as material intended for direct student submission. CMP_6059B_7059B_2025_26_CW1-pre…

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International Business / Strategic Management 200 words

Individual Reflection on Tencent’s Strategic Reorganisation and International Business Strategy

This International Business Strategy assessment is an individual reflective exercise based on a case-study discussion undertaken during Weeks 5–8 of the module. Its purpose is to develop students’ ability to critically reflect on learning experiences and use that reflection to improve future academic and professional performance. Students participate in approximately 30-minute seminar discussions in which theoretical concepts from the module are applied to real-world business cases with tutors and peers. IBS-0901-2025-+Reflection2-Ref For this entry, the selected case is Tencent: “third time lucky?”. The case examines how Tencent evolved from a Chinese instant-messaging company into a major technology group spanning messaging, gaming, entertainment, investment and cloud services. It focuses particularly on three major organisational reorganisations undertaken in 2005, 2012 and 2018 as the company attempted to address increasing organisational complexity, coordination problems, changing technologies and new strategic priorities. The 2005 restructuring responded to Tencent’s growing scale by dividing the organisation into major business and platform-development groups. By 2012, further growth and the success of products such as WeChat had created new organisational complexity, leading to a structure organised around seven distinct business groups. The case highlights that this arrangement subsequently contributed to a “silo culture”, creating difficulties in sharing ideas, data, code and customers across business units. The 2018 reorganisation responded to a strategic shift toward the “industrial internet”, including greater emphasis on cloud services, business-to-business solutions and digital transformation. Tencent reorganised around six major groups, including Cloud and Smart Industries, Platforms and Content, Interactive Entertainment, Corporate Development, WeChat, and Technology and Engineering, while also establishing a technical committee intended to improve internal coordination and data sharing. The case asks students to consider both the strategic rationale for each restructuring and whether the 2018 changes adequately addressed Tencent’s organisational challenges. The reflection itself must be between 150 and 250 words. Students should explain what the week’s topic and case study were about, what they learned from the discussion, how the conversation helped them apply theory to their final report, and what key insights they gained. They should also reflect on what went well, what challenges were encountered and what they would change if repeating the session. At least one reflective framework, such as Gibbs’ Reflective Cycle or Kolb’s Experiential Learning Cycle, must be applied. IBS-0901-2025-+Reflection2-Ref The wider learning outcomes emphasise strategic management theory, internationalisation, internal and external environmental influences, critical analysis of international organisations and the use of analytical tools to evaluate strategic options. The task therefore combines strategic thinking, international business analysis and reflective professional development. IBS-0901-2025-+Reflection2-Ref The accompanying Tencent case specifically asks students to consider why each major reorganisation occurred, whether the 2018 restructuring adequately addressed Tencent’s challenges, and what additional “hard” and “soft” implementation measures might have been necessary. Case+study+7

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Network Systems and Administration / Cloud Computing / DevOps 2,500 words

Automated Container Deployment and Administration in the Cloud

This practical Network Systems and Administration assessment requires students to design and implement an automated cloud deployment workflow using modern DevOps and infrastructure automation technologies. The overall objective is to deploy a cloud-hosted server running a Docker container while integrating at least two automation tools such as Terraform, AWS CloudFormation, Ansible, Azure DevOps, GitHub Actions or GitHub. Students may use a cloud platform such as AWS, Microsoft Azure or Google Cloud Platform. B9IS121 Network Systems and Adm… The first stage focuses on cloud infrastructure provisioning. Students automate the creation of a server instance, such as an AWS EC2 instance or Azure virtual machine, and configure supporting infrastructure including networking and security groups. Infrastructure must be provisioned through tools such as Terraform, GitHub Actions or AWS CloudFormation. The required outputs include reusable infrastructure scripts or templates and an architecture diagram showing the deployed cloud resources. B9IS121 Network Systems and Adm… The second stage addresses configuration management. Students use Ansible or an equivalent automation platform to configure the provisioned server, install Docker and ensure Docker starts automatically when the server boots. Deliverables include Ansible playbooks or equivalent configuration scripts together with a README file explaining the automation workflow. B9IS121 Network Systems and Adm… The third stage requires students to containerise and deploy a web application. A Dockerfile must be created for either an existing or newly developed sample application, and deployment of the resulting container must be automated on the provisioned server. The required artefacts include the Dockerfile, deployment scripts and application repository. B9IS121 Network Systems and Adm… Students must also create a CI/CD pipeline using Azure DevOps or another suitable CI/CD platform. The pipeline should automatically build and deploy the Docker container whenever new code is pushed to a version-control repository such as GitHub. Students must provide the relevant pipeline configuration, normally in YAML, and demonstrate the pipeline operating successfully. B9IS121 Network Systems and Adm… Documentation is an important part of the assessment. Each student produces an independent PDF report explaining every stage of the automation process, challenges encountered and how those challenges were resolved. A 10-minute walkthrough video must demonstrate the complete end-to-end deployment, and the report must contain a detailed architecture diagram. B9IS121 Network Systems and Adm… The technical report should include a title page, summary, introduction, main technical content, conclusion, references and appropriate appendices. Students are expected to justify the tools selected and critically discuss alternative solutions, advantages and limitations. The report is expected to contain approximately 2,000–2,500 words and should use clear headings, diagrams or flowcharts where relevant, and academic or professional sources such as journal articles, whitepapers, DevOps documentation and industry reports. B9IS121 Network Systems and Adm… B9IS121 Network Systems and Adm… The marking scheme allocates 40% to the demonstration, 50% to report quality and 10% to technical artefacts. Higher-performing submissions are expected to show a working deployment, clear justification of technical actions, strong understanding of automation tools, professional report structure, consistent referencing and reusable scripts that reproduce the same deployment results. B9IS121 Network Systems and Adm… The final submission includes a comprehensive PDF report of approximately 5–8 pages, a detailed README, and links to the relevant scripts, configurations and deployment evidence. B9IS121 Network Systems and Adm…

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International Human Resource Management / International Business 800 words

Comparative Analysis of Cultural Factors Shaping Human Resource Staffing Strategies in International Enterprises

This individual international human resource management assessment examines how cultural and institutional differences influence recruitment and staffing decisions in international enterprises. Students take the role of an HR representative for VetDopomoga, a Ukraine-based international veterinary clinic chain planning to expand into either the United Kingdom or India. The final output is a concise academic poster of up to two pages and approximately 800 words ±10%, supported by appropriate visual material. Assignment 1_The Comparative An… The first component requires comparison of the United Kingdom and India with Ukraine. Students examine relevant cultural characteristics, including communication style, hierarchy and power distance, individualism versus collectivism and approaches to time, alongside institutional factors such as labour-market conditions and employment or regulatory environments. This comparison provides the contextual basis for evaluating how recruitment practices may need to change across markets. Assignment 1_The Comparative An… Students must then apply an appropriate international HR staffing strategy, choosing from ethnocentric, polycentric, regiocentric or geocentric approaches. Based on their analysis, they must recommend which market VetDopomoga should enter first and which should follow, providing clear evidence-based justification. Assignment 1_The Comparative An… A central theoretical component requires application of one cultural framework, selected from the Cultural Intelligence framework, Erin Meyer’s Culture Map, the GLOBE Project or Hofstede’s Cultural Dimensions Theory. Students focus on two dimensions from their selected framework and use them to evaluate the benefits and challenges associated with recruitment practices in Ukraine, the United Kingdom and India. Assignment 1_The Comparative An… The poster must also examine a recent cultural, economic or political event affecting the three national contexts and explain its implications for international recruitment and staffing. Examples suggested in the brief include changing social attitudes following COVID-19, economic shifts affecting demand for specialist labour and post-Brexit employment or migration policies. Assignment 1_The Comparative An… Students additionally research best-practice recruitment examples from international veterinary enterprises operating in the UK and India and identify practices VetDopomoga could adopt or adapt in each market. The final recommendation must identify the preferred first expansion destination and provide three evidence-based reasons supporting the decision. Assignment 1_The Comparative An… Assessment criteria place strong emphasis on content and findings, theoretical application, critical analysis, source quality and Harvard referencing, as well as poster presentation and structure. High-quality work is expected to go beyond description by applying cultural theory directly to HRM practices and critically evaluating the implications of cultural and institutional differences for international recruitment decisions. Assignment 1_The Comparative An… Assignment 1_The Comparative An…

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Data Science / Time Series Analysis / Machine Learning

Time Series Modelling Case Study: Oil Price Forecasting with ARMA and Alternative Models

This Time Series Modelling Case Study requires students to analyse real-world oil-price time-series data and develop forecasting models capable of predicting future values. The assessment combines traditional statistical time-series techniques with an alternative forecasting approach, requiring students to demonstrate practical modelling skills, critical research engagement and evidence-based interpretation of forecasting results. The coursework is completed individually and contributes 40% of the assessment. assing,,, (1) The assessment is divided into three main parts. Part 1 focuses on developing an ARMA-based forecasting model using daily oil-price data covering approximately 2024 to 2026. Students begin with exploratory data analysis and initial visualisation before testing whether the time series is stationary. Where necessary, appropriate transformations or differencing must be applied to obtain stationarity. Students then define an ARMA model and identify suitable p, d and q parameters using an AIC-based model-selection procedure across the parameter ranges specified in the brief. assing,,, (1) Model adequacy must be assessed using diagnostic analysis. Students inspect residuals, generate additional ACF plots, examine residual distributions and evaluate prediction performance using appropriate metrics such as RMSE. The selected model is then used to forecast oil prices 24 months into the future, with appropriate confidence intervals added to communicate forecast uncertainty. assing,,, (1) Part 2 requires students to research and implement an alternative forecasting approach. Suggested examples include LSTM and Prophet, although another appropriate model may be proposed. Students conduct a literature review supporting the alternative method, build and where relevant hyperparameter-tune the model, generate another 24-month forecast, visualise predictions and confidence intervals, and calculate suitable evaluation metrics. This component is intended to demonstrate independent research and the ability to propose an alternative solution rather than relying only on the conventional ARMA approach. assing,,, (1) Part 3 consists of a 6–8 page technical report explaining the modelling process, forecasting results and resulting inferences. The report should provide a critical analysis rather than simply reproducing numerical outputs. Students are expected to explain why results occurred, justify modelling choices, evaluate how those choices influenced performance, compare forecasts with subsequently observed real data where possible, and construct a coherent narrative supported by plots, images, summary statistics and academic literature. Future improvements to the modelling approach should also be critically discussed. assing,,, (1) Submission consists of both the report and working code. The code may be submitted directly or through an accessible Colab or GitHub repository and must reproduce all models, figures and numerical results presented in the report. The assessment allocates 60% of the marks to code and 40% to the report. Within the coding component, modelling and forecasting completion accounts for 40 marks and code quality and annotation for 20 marks. The report is assessed on analysis and inference, methodological justification, comparison of the two modelling approaches, presentation quality, figures and use of appropriate references. assing,,, (1) Key technical expectations include appropriate testing for stationarity, use of methods such as ADF, ACF, PACF and differencing, systematic model selection, forecasting, evaluation and clear comparison between the traditional ARMA model and the chosen alternative approach. Higher-quality work is expected to interpret what the forecasts mean, identify potential improvements and demonstrate sound technical communication rather than merely reporting model outputs. assing,,, (1) Important for the public Reference Library: the brief explicitly states that students must not use generative AI to write the report, and the rubric indicates that AI text-generation use may result in zero marks for the whole assignment. Therefore, the public entry should remain a high-level description of the assessment rather than material intended for direct submission. assing,,, (1)

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Supply Chain Operations and Logistics Management 3,000 words

Critical Review of Global Logistics: Supply Chain Strategy, Resilience and Sustainability

This postgraduate logistics assessment requires students to conduct a critical review of the global logistics operations and strategies of a selected multinational manufacturing organisation. The chosen company must operate internationally and have sufficient publicly available information to support detailed academic and industry research. The report examines how global manufacturers structure, manage and improve their upstream and downstream logistics channels in increasingly complex and uncertain supply-chain environments. Component_2_NBS_7054A_25_Report… The first part analyses the organisation’s upstream and downstream logistics channels and evaluates the logistics strategies it has adopted. Particular attention is given to how the organisation responded to disruption caused by the COVID-19 pandemic. Students examine how supply-chain networks were reconfigured and how logistics strategies were adapted to maintain operations, while explicitly relating these responses to relevant performance objectives and supply-chain dimensions. Component_2_NBS_7054A_25_Report… The second part focuses on value creation within global logistics networks. Students evaluate how supply-chain intermediaries contribute to value co-creation while balancing operational costs and reducing waste or Muda. The analysis also considers how communication and information sharing support the objectives of the organisation’s marketing channel network and identifies lessons learned from pandemic-related disruption. Component_2_NBS_7054A_25_Report… The final section examines how the organisation can strengthen the sustainability of its logistics operations. Students assess the feasibility of aligning circular economy principles and Industry 4.0 technologies with existing inbound and outbound logistics activities. Relevant issues include reduced material use, reuse, repair, refurbishment, alternative transport modes, carbon-footprint reduction and environmental efficiency. Students must also recommend operational decisions that apply innovation-led lean approaches to sustainability initiatives. Component_2_NBS_7054A_25_Report… The marking criteria place 25% of the assessment on logistics theories and strategies, 35% on value co-creation, communication and resilient logistics networks, and 40% on circular economy, Industry 4.0 and sustainability recommendations. Marking_Criteria_NBS_7054A_25_R… Overall, the assessment integrates global logistics strategy, resilience, lean operations, digitalisation, sustainability and evidence-based supply-chain decision-making within a real organisational case study. Overview word count: approximately 340 words.

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Cyber Security / Digital Forensics 3,877 words

Digital Forensics Investigation: USB, Memory and Windows Registry Analysis

This Digital Forensics Investigation Report presents a multi-source forensic examination involving removable media, volatile memory and a Windows disk image. The work is undertaken within an MSc Cyber Security context and demonstrates the application of forensic procedures, specialist analysis tools and evidential reasoning to investigate suspected criminal and malicious activity. The report places particular emphasis on maintaining evidence integrity, reconstructing activity across different forensic sources and correlating artefacts to produce defensible investigative findings. DF_Report_Sathiyaprakash The first part investigates a FAT32 USB forensic image associated with suspected video piracy and other potentially criminal activity. The examination uses tools including FTK Imager, Autopsy, Sleuth Kit, ewfmount, cryptographic hashing utilities and VeraCrypt. The methodology includes pre- and post-examination hash verification, read-only mounting, filesystem enumeration, deleted-file analysis and low-level sector examination. The investigation identifies deleted artefacts, portable anti-forensic utilities, browser evidence and an encrypted VeraCrypt container concealed within unallocated disk space. DF_Report_Sathiyaprakash The USB investigation also demonstrates the importance of evidence integrity and chain of custody. MD5 and SHA-256 hashes are used to establish and later confirm the integrity of forensic copies, while analysis is conducted without modifying the original evidence. The report examines filesystem structures, deleted files, anti-forensic tooling and encrypted data and records evidence handling through a formal chain-of-custody process. DF_Report_Sathiyaprakash DF_Report_Sathiyaprakash The second part focuses on volatile-memory forensics using a Windows memory image. Volatility 3 is used to identify the operating-system profile, reconstruct process hierarchies, inspect process ownership and security identifiers, and extract suspicious process memory. Particular attention is given to AtomicService.exe, which is observed running with SYSTEM privileges and associated with the Atomic Red Team framework and MITRE ATT&CK technique T1543.003 – Windows Service. The investigation also considers PowerShell activity, process execution timelines and indicators of suspicious behaviour. DF_Report_Sathiyaprakash DF_Report_Sathiyaprakash The third part conducts Windows Registry forensic analysis on the WinRegEvidenceP3.vhd image. Registry artefacts are examined using RegRipper, with analysis covering SYSTEM, SOFTWARE, SAM and NTUSER.DAT hives. The investigation evaluates system configuration, user accounts, application execution and persistence evidence using artefacts such as Run keys, BAM, Prefetch and scheduled tasks. These findings are then correlated with evidence recovered from volatile memory to reconstruct the sequence of suspicious activity. DF_Report_Sathiyaprakash Across the report, evidence from disk, memory and the Windows Registry is combined to reconstruct malicious activity and identify persistence mechanisms, elevated processes, suspicious user accounts and adversary-simulation tools. The analysis maps relevant behaviour to the MITRE ATT&CK framework and considers both technical findings and their evidential significance. The report therefore demonstrates practical competence in forensic acquisition principles, artefact analysis, timeline reconstruction, malware and process investigation, evidence correlation and professional reporting. Important for the Reference Library: this upload contains an actual student name on the cover page and detailed case evidence. Since your Reference Library says there is no student record behind uploaded past work, I would use the generic title and overview above rather than copying the student-identifying cover-page information into the public metadata. DF_Report_Sathiyaprakash

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Computer Science / Parallel Computing

Parallel Merge Sort with Load Balancing

This technical research article investigates the performance limitations of conventional parallel merge sort and proposes a load-balanced alternative designed to improve processor utilisation in distributed-memory parallel computing systems. Traditional parallel merge sort progressively reduces the number of active processors during successive merge stages, causing many processors to remain idle and reducing the performance benefits of parallelisation. The proposed approach addresses this limitation by ensuring that all processors continue participating throughout the merging process. Parallel_Merge_Sort_with_Load_B… The paper first explains the conventional parallel merge-sort process, in which data are locally sorted before processors are paired for a series of merging operations. At every subsequent stage, the number of participating processors is halved until only one processor remains responsible for the final merged list. This results in poor processor utilisation and increasing workload concentration. Parallel_Merge_Sort_with_Load_B… The proposed load-balanced parallel merge sort distributes each partially sorted list across multiple processors so that every processor maintains approximately the same number of keys throughout execution. Processor groups use histograms and boundary values to determine how data should be redistributed during merging. Histogram-based partitioning reduces unnecessary data movement, while an index-swapping mechanism is introduced to avoid transferring large blocks of keys when logical processor reassignment can achieve the same result more efficiently. Parallel_Merge_Sort_with_Load_B… Parallel_Merge_Sort_with_Load_B… The algorithm was implemented in C using MPI and experimentally evaluated on a Cray T3E parallel computer and an eight-node PC cluster. Testing considered both uniform and Gaussian key distributions. Results show that performance improvements increase as processor count grows, although communication and histogram-management overhead can reduce benefits for small workloads. Parallel_Merge_Sort_with_Load_B… The proposed technique achieved a maximum merge-phase speedup of 9.6 on a 32-processor Cray T3E and 2.3 on an eight-node PC cluster when processing four million keys. The study concludes that distributing approximately equal workloads across processors can substantially improve parallel merge performance and may also be applicable to related parallel sorting algorithms. Parallel_Merge_Sort_with_Load_B… Overview word count: approximately 330 words. For your portal, I would not label this as university coursework unless you also have the actual assessment brief that uses this paper. This PDF itself only establishes a published academic paper and the authors’ Korea University affiliation.

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Artificial Intelligence / International Business 3,000 words

AI Innovation Consultancy: Evaluating Artificial Intelligence Solutions for Business Problems

This individual consultancy assessment requires students to act as an AI Innovation Consultant and evaluate how artificial intelligence could address a significant real-world business problem. Students select one industry—such as healthcare, retail, FinTech, manufacturing or agriculture—and concentrate on a single clearly defined organisational challenge rather than comparing multiple sectors. Potential issues include long waiting times, high operating costs, fraud and risk, poor customer experience or inefficient supply chains. The report develops a practical AI solution by identifying suitable technologies such as machine learning, natural language processing or computer vision and explaining how they would operate within the chosen organisational context. Students are not required to build an AI system; instead, the emphasis is on demonstrating business-level technical understanding, critical thinking and the ability to assess whether the proposed technology can realistically integrate with existing organisational processes. The analysis considers the capabilities and limitations of AI, technical feasibility, integration requirements and the skills or organisational capabilities required for implementation. Students must also critically examine ethical, legal and social implications, including issues such as algorithmic bias, transparency, accountability, privacy and regulatory obligations such as UK GDPR. Appropriate risk-mitigation measures should be proposed. A substantial element of the report develops the business case for AI adoption. Students evaluate implementation costs and expected benefits, estimate return on investment, identify assumptions and commercial risks, and assess the overall strategic value of the solution to the organisation. The report concludes with clear recommendations, implementation priorities and a final judgement on whether the proposed AI initiative is feasible and worthwhile. The assessment places strong emphasis on critical analysis, technical understanding, business acumen and professional communication. Students are expected to support arguments with credible academic, industry and government evidence and include at least two professional visualisations such as frameworks, diagrams or tables. Harvard referencing is required throughout. Overview word count: approximately 330 words. The brief also allows authorised use of generative AI for idea generation, drafting/structuring and proofreading, provided the student verifies accuracy, references appropriately and submits the required GenAI declaration.

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Cyber Security / Applied Cryptography / Secure Systems Design 2,500 words

Secure Property Contract Exchange: Cryptographic Protocol Design, Threat Modelling and Post-Quantum Readiness

This Applied Theory of Cyber Security and Secure Design coursework places students in the role of a cyber security consultant engaged by Hackit & Run LLP, a legal firm specialising in UK and international property transactions. The firm wishes to implement a secure digital system for handling, exchanging and legally signing property contracts. Students must design and evaluate a secure communication protocol supporting interactions between the buyer’s solicitor, the seller’s solicitor and the buyer while addressing both first-time communications and previously established secure relationships. 11b17febb9eeb4570f76f6ca95a831a… Section A – Cryptographic Protocol Design, worth 45%, requires a complete secure communication protocol. Students must explain how trust is initially established, how later communications can be simplified without weakening confidentiality, integrity or availability, and how the buyer can digitally sign a contract in a manner enforceable under UK law. The design must justify specific cryptographic algorithms for functions such as key exchange, bulk encryption, digital signatures and hashing. The protocol must be presented through both a sequence diagram showing message flows and cryptographic operations and pseudocode explaining the key algorithmic stages. 11b17febb9eeb4570f76f6ca95a831a… Section B – Threat Modelling, worth 20%, requires a focused analysis using the STRIDE methodology. Students identify three realistic threats from different STRIDE categories and analyse the attack vector, asset at risk and potential effect on the legal transaction. Each threat must then be connected back to specific protocol defences, with residual risks acknowledged where controls cannot provide complete mitigation. The guidance encourages consideration of issues such as social engineering, insider threats, key-management failures and availability risks in addition to purely cryptographic attacks. 11b17febb9eeb4570f76f6ca95a831a… Section C – Security Evaluation Against Standards, worth 15%, requires students to evaluate the proposed system against a recognised cybersecurity standard or framework. Options include ISO/IEC 27001:2022, Common Criteria (ISO/IEC 15408) and the OWASP Application Security Verification Standard. Students select three or four directly relevant controls or requirements, assess whether the proposed design satisfies them, identify gaps and recommend specific improvements. 11b17febb9eeb4570f76f6ca95a831a… Section D – Post-Quantum Readiness and Critical Reflection, worth 15%, examines how a future quantum-capable adversary could affect the protocol. Students identify vulnerable cryptographic components, discuss the NIST Post-Quantum Cryptography standardisation programme, and examine replacement algorithms such as ML-KEM for key establishment and ML-DSA for digital signatures. They must also evaluate a hybrid migration strategy combining classical and post-quantum algorithms, considering performance overhead, backward compatibility and the legal admissibility of post-quantum digital signatures. 11b17febb9eeb4570f76f6ca95a831a… The remaining 5% evaluates professional report quality, logical structure, technical language, integration of diagrams and consistent CUHarvard referencing. Higher-quality work is expected to demonstrate a sophisticated trust model, clear traceability between threats and controls, precise standards mapping, practical security recommendations and well-evidenced analysis of post-quantum migration. 11b17febb9eeb4570f76f6ca95a831a… Important for the public Reference Library: the brief states that the assessment document is intended only for Coventry University Group students and must not be passed to third parties or posted on any website. Therefore, publish only an original high-level description such as the overview above; do not upload or reproduce the original assignment brief publicly. 11b17febb9eeb4570f76f6ca95a831a…

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Computer Science / Research Methods / Specialist Research 2,800 words

Research Specialism Report: Critical Review of Open Research Questions in Computer Science

This Advanced Research Topics in Computer Science assessment requires students to critically examine a research paper associated with their chosen MSc specialism and demonstrate an understanding of how established research techniques are used to create and extend knowledge in computer science. Eligible specialisms include Artificial Intelligence, Networking, Cyber Security, Software Engineering and Data Science. The assessment is intended to prepare students for deeper independent research as part of their Master's project. 7COM1084+Research+specialism+re… Students begin by providing a clear introduction to their selected research specialism and explaining the broader research area in a way that is accessible to readers with general computer-science knowledge. The report then identifies the open research question presented in the relevant 7COM1084 specialist lecture paper, explains the problem in detail and evaluates why it is scientifically significant or relevant to a real-world application. 7COM1084+Research+specialism+re… A substantial literature-review section requires students to examine existing and related research beyond the specialist lecture paper. The aim is to identify what previous work has achieved, explain why existing approaches do not fully solve the research problem and identify further unresolved questions. 7COM1084+Research+specialism+re… The research-methods section focuses on the approaches used in the selected specialist paper. Students are expected to describe and critically evaluate those methods, considering both their strengths and limitations. They must then propose an alternative or extended research approach that could build on the published work and investigate related open problems, drawing on principles of experimental design and theoretical or practical research. 7COM1084+Research+specialism+re… The final reflective component asks students to explain their personal investment in the research area, including why the selected question interests them and how their own strengths and prior experience would support future research in that domain. 7COM1084+Research+specialism+re… The report must not exceed 2,800 words ±10%, must use the Harvard referencing system, and must include at least 20 references, one of which must be the relevant 7COM1084 specialist paper. 7COM1084+Research+specialism+re… 7COM1084+Research+specialism+re… 7COM1084+Research+specialism+re… Overall, the assessment integrates research specialism knowledge, literature review, open-problem identification, methodological critique, research design, future-work development and scholarly communication within a Level 7 computer-science research context.

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Data Science / Artificial Intelligence / Generative Modelling

Generative Modelling Case Study: GANs for Medical Imaging, Cybersecurity and Creative AI

This Generative Modelling Case Study requires students to design, implement and evaluate Generative Adversarial Networks (GANs) across a range of synthetic and real-world applications. The coursework develops both theoretical understanding and practical deep-learning skills, with emphasis on building models, evaluating generated data and critically interpreting model performance. The assessment addresses research understanding, originality, future development and scholarly communication in data science. Generative modelling case study… Part 1 – Building and Understanding GANs from Scratch focuses on fundamental GAN concepts using synthetic two-dimensional data. Students first reproduce a sine-wave GAN from the tutorial and then create a second synthetic distribution using either a 2D spiral, a mixture of Gaussians or a noisy parametric curve. They must modify aspects of the GAN architecture, such as activation functions or network depth, and visually compare generated samples with the original data distribution. Generative modelling case study… Part 2 – Real-World GAN Applications extends the work across three application domains. The first application uses the BloodMNIST subset of MedMNIST to train a DCGAN that generates synthetic blood-cell microscope images. Students explore the dataset, analyse class distributions, train the model, monitor generator and discriminator losses and compare real and generated images using both visual inspection and quantitative measures such as the Fréchet Inception Distance (FID). An optional extension involves implementing a class-conditioned GAN capable of generating images from specific categories. Generative modelling case study… The second application addresses cybersecurity using the CICIDS 2017 intrusion-detection dataset. Students construct a GAN that generates synthetic network-traffic feature vectors rather than images. The model is trained using benign and denial-of-service traffic, and generated samples are compared with real traffic using dimensionality-reduction techniques such as PCA or t-SNE. Students must evaluate how closely the synthetic traffic reflects the distribution of genuine network data. An extension allows analysis of the full CICIDS dataset and evaluation across different attack types. Generative modelling case study… The third application explores Creative AI using the Google QuickDraw pizza category. Students implement another DCGAN to generate artificial pizza sketches, track training behaviour across epochs, and compare generated sketches with genuine examples using both visual inspection and quantitative metrics such as FID. Extension work may examine additional QuickDraw categories and investigate how model performance changes with class and sketch complexity. Generative modelling case study… Submission consists of a 6–8 page report together with working code. The report should explain the analysis undertaken, justify modelling decisions, describe the network architectures, interpret the results and incorporate suitable figures, evaluation metrics and references. The accompanying code must reproduce the figures, models and numerical results reported and must execute successfully when tested. Generative modelling case study… The marking scheme places 60% of the marks on code and 40% on the report. Within the coding component, 40 marks relate to completing the GAN modelling tasks and 20 marks assess code quality, modularity and annotation. The report is assessed on discussion and interpretation of the analysis, justification of architectural choices, results presentation, document quality, figures and appropriate academic references. Generative modelling case study… Important for the public Reference Library: the brief explicitly states that students must not use generative AI to write the report, and the rubric states that AI-generated report text can result in zero marks for the whole assignment. Therefore, use this entry only as a high-level public description of the assessment and do not present generated report content as something students can submit directly. Generative modelling case study… Generative modelling case study…

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Software Engineering / Software Measurement and Process Models 2,000 words

Software Measurement, Goal–Question–Metric Analysis and Process Model Evaluation for DETO

This Measures and Models in Software Engineering coursework uses the Doolan Executive Taxi Service Online (DETO) case study to assess the application of software measurement techniques, Goal–Question–Metric analysis and software process modelling. The assignment requires students to connect quantitative software-engineering measures with practical organisational objectives and to critically evaluate suitable development-process approaches for a safety- and reliability-sensitive software system. 7COM1024_MMSE_ Def_CW_Brief 202… Task 1 focuses on software measurement using Unadjusted Function Points. Students analyse part of the DETO web-based email attachment system and identify relevant functional elements, including user inputs, reports to users, prompts and responses, interfaces to external systems or files, and internal logical files. Appropriate weights must then be selected to calculate the unadjusted functional-point value. 7COM1024_MMSE_ Def_CW_Brief 202… Task 2 requires application of the Goal–Question–Metric (GQM) approach to evaluate the effectiveness of the DETO website. Students identify the object under measurement and define the purpose, quality focus, viewpoint and context. They then develop two measurable organisational goals, formulate relevant questions for each goal and identify metrics that could be collected to evaluate website performance and support the company's priority of attracting new customers. 7COM1024_MMSE_ Def_CW_Brief 202… Task 3 moves from software measurement to software process models. DETO is considering a sophisticated on-board navigation system for its next generation of vehicles, where fast development, high quality, reliability and integration with the existing website are strategically important. Students select two software process models and critically evaluate their suitability for the project. The discussion should compare their distinguishing characteristics against alternative approaches, identify development challenges, evaluate project risks and pitfalls, and propose appropriate risk-mitigation strategies. 7COM1024_MMSE_ Def_CW_Brief 202… The main discussion for Task 3 is limited to 2,000 words ±10% and must use the Harvard referencing system, with at least 10 sources expected. The brief specifically requires original prose and prohibits bullet points, lecture materials and diagrams for this section. 7COM1024_MMSE_ Def_CW_Brief 202… Overall, the assessment integrates functional software measurement, GQM-based metrics, process-model evaluation, software quality, reliability, systems integration and project risk management within a realistic software-engineering business scenario.

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Cyber Security / Ethical Hacking / Penetration Testing 4,000 words

Ethical Hacking and Penetration Testing: Vulnerability Exploitation, Privilege Escalation and Mitigation

This Ethical Hacking and Penetration Testing coursework requires students to conduct a practical CTF-style penetration test against a set of authorised target machines and produce a professional technical report documenting the compromise of one selected target. The assessment evaluates practical exploitation skills alongside the ability to analyse risk, explain attack vectors and recommend effective security controls. 8598ba8d8fff5a38af8d427a4ce8f84… The practical element requires students to identify vulnerabilities in multiple target systems, exploit those weaknesses to gain low-privileged access and then perform privilege escalation to obtain root-level access. Successful completion of each stage produces flags, with separate user and root flags contributing directly to the practical marks. Brief descriptions of the attack vectors and payloads used must also be recorded. 8598ba8d8fff5a38af8d427a4ce8f84… The written report focuses in detail on one compromised machine. Students must explain the reconnaissance and vulnerability-identification process, including the techniques used to discover services, web content and potential attack surfaces. The marking criteria specifically recognise appropriate reconnaissance tools such as Nmap and FFUF and reward clear justification of methods and links between reconnaissance results and identified threats. 8598ba8d8fff5a38af8d427a4ce8f84… 8598ba8d8fff5a38af8d427a4ce8f84… A further component requires a formal risk rating for the discovered vulnerabilities. Students should use a recognised risk-classification approach, such as OWASP or SANS, justify the assigned severity and discuss relevant social, legal and ethical considerations. Higher-performing work is expected to connect those considerations directly to the specific vulnerabilities identified. 8598ba8d8fff5a38af8d427a4ce8f84… The exploit section should explain the technical cause of the vulnerability, describe the exploitation process and present relevant example payloads. Mitigation recommendations must then be linked directly to the vulnerabilities discovered, with clear explanations of where the weakness occurs and how it can be remediated. 8598ba8d8fff5a38af8d427a4ce8f84… Overall, the coursework integrates reconnaissance, vulnerability analysis, exploitation, privilege escalation, risk assessment, ethical and legal considerations, technical reporting and defensive mitigation within an authorised penetration-testing environment. Important: this brief states that it is for Coventry University Group students' own use and must not be passed to third parties or posted publicly. 8598ba8d8fff5a38af8d427a4ce8f84… So for your public Reference Library, use an original summary like the one above rather than uploading the assessment brief itself.

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Digital Forensics / Cyber Security 3,000 words

Digital Forensics Investigation of a USB Device: Evidence Acquisition, Analysis and Reporting

This Digital Forensics assessment requires students to undertake a simulated professional investigation of a USB storage device while acting as a digital forensic trainee for a fictional forensic-services organisation. The nature of the suspected wrongdoing is initially unknown, requiring the investigator to apply appropriate forensic methodologies, tools and analytical techniques to identify suspicious activity, recover relevant artefacts and determine whether evidence of malicious or unauthorised behaviour exists. 7070SCN_Assessment Brief_2526MA… The investigation begins with authorisation, evidence handling and chain of custody. Students must demonstrate secure receipt and management of the USB device, operate within the authorised scope of the investigation and maintain records covering each stage of the evidence lifecycle. Forensic acquisition requires use and validation of a software write blocker, creation of bit-for-bit forensic images and cryptographic hashing to demonstrate that working and evidential copies maintain integrity. 7070SCN_Assessment Brief_2526MA… The analytical stage involves examination of the working forensic image using appropriate digital-forensic tools. Students investigate file-signature mismatches, password-protected files, registry artefacts, USB history, user activity, metadata and timeline relationships. Where suspicious executable files are discovered, static and/or dynamic malware analysis may be undertaken. The investigation can therefore span documents, images, applications, executables and registry hives. 7070SCN_Assessment Brief_2526MA… Students must maintain objective and chronological forensic case notes documenting evidence acquisition, preservation, analysis and interpretation. These records should support reproducibility and potential evidential admissibility. The final forensic report summarises findings, explains and justifies the tools and methodologies used, records integrity-verification results, discusses relevant legal, ethical and professional principles and provides appropriate recommendations for further action. 7070SCN_Assessment Brief_2526MA… The assessment also encourages advanced forensic analysis where relevant, including OSINT, password recovery or decryption, data carving, regular-expression searching, advanced registry analysis and static or dynamic malware investigation. Professional practice is assessed through adherence to recognised forensic methodologies, industry best practice, legal and ethical obligations, chain-of-custody records and evidence-handling procedures. 7070SCN_Assessment Brief_2526MA… Overall, the coursework integrates forensic acquisition, evidence preservation, artefact analysis, advanced investigation techniques, professional documentation and legally defensible reporting within a realistic digital-forensics case-study environment. Important for your public Reference Library: this brief explicitly states that it is for Coventry University Group students' own use and must not be passed to third parties or posted on any website. 7070SCN_Assessment Brief_2526MA… So use the metadata and an original high-level overview like the one above, but do not upload or publicly reproduce the assessment brief itself.

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Software Engineering / Object-Oriented Programming / Java Development

Java Group Exercise Booking and Management System for Furzefield Leisure Centre

This software-development coursework requires students to design and implement a Java-based booking and management system for Furzefield Leisure Centre (FLC). The proposed system manages member bookings for group exercise lessons delivered on Saturdays and Sundays. Exercise types can include Yoga, Zumba, Aquacise, Box Fit and Body Blitz, with each lesson accommodating a maximum of four members and carrying an exercise-specific price. 7COM1025 Coursework Explanation… The application must enable members to search the lesson timetable either by day or exercise type and make bookings subject to capacity constraints. Members may book multiple lessons, but duplicate bookings for the same lesson are prohibited. Existing bookings can be changed when capacity is available in the replacement lesson or cancelled before attendance. Successful changes must retain the existing booking ID while releasing the place held in the original lesson. 7COM1025 Coursework Explanation… 7COM1025 Coursework Explanation… After attending a lesson, members can provide a written review and a numerical rating from 1 to 5. Booking statuses should distinguish booked, changed, attended and cancelled reservations. The system must also generate management reports showing attendance and average lesson ratings and identify the exercise type producing the highest income, while listing income generated by each exercise category. 7COM1025 Coursework Explanation… 7COM1025 Coursework Explanation… Students must design at least eight weekends of timetable data, equivalent to 48 lessons, covering at least four exercise types. The program should be self-contained and does not require an external database or security protocol. 7COM1025 Coursework Explanation… The software-engineering component requires a UML class diagram, Java implementation, JUnit testing, version-control evidence and consideration of design principles, design patterns and refactoring. Students submit a PDF report containing the UML diagram and repository evidence, a ZIP containing source code, tests and an executable JAR, and a screen-recording demonstrating the final system. 7COM1025 Coursework Explanation… 7COM1025 Coursework Explanation… Overall, the coursework assesses practical object-oriented software development through system modelling, implementation, validation and reflective documentation. The marking allocates 40 marks to system functionality, 30 to design and implementation, 10 to UML, 10 to JUnit testing, 5 to version control and 5 to report quality. 7COM1025 Coursework Explanation… Note: the uploaded material identifies the code 7COM1025 but does not state the formal module title. I would therefore keep Module name = Not specified rather than inventing one.

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Cybersecurity / Security Operations

Catnip Games International: SOC Automation and Incident Response Platform

This cybersecurity project presents the design and implementation of a prototype Security Operations Centre (SOC) automation and incident-response platform for Catnip Games International. The scenario addresses security challenges affecting a gaming organisation operating more than 300 Linux servers across two data centres, including credential-stuffing bot attacks, compromised player accounts, phishing campaigns and delayed coordination during security incidents. Catnip_Games_SOC_Complete Catnip_Games_SOC_Complete The proposed solution integrates TheHive 5, Cortex 3, Elasticsearch, Cassandra and Python-based automation, with MISP explored for threat-intelligence integration. TheHive functions as the central incident and case-management platform, Cortex provides automated observable analysis, Elasticsearch supports search and log storage, Cassandra provides persistent case and alert storage, and Python scripts automate alert ingestion and workflow activities through REST APIs. Catnip_Games_SOC_Complete Catnip_Games_SOC_Complete Implementation includes environment configuration, Docker deployment, API integration, automated alert generation, incident-response playbooks, KPI monitoring and backup procedures. Three attack scenarios are modelled: bot attacks, account takeover and phishing. Alerts are automatically ingested into TheHive, converted into cases and processed through analyst triage, investigation and resolution workflows. Catnip_Games_SOC_Complete Catnip_Games_SOC_Complete The project also develops structured response playbooks covering triage, containment, investigation, recovery and post-incident actions for each security scenario. Operational metrics are visualised through a KPI dashboard measuring alert volumes, response times, Mean Time to Detect (MTTD), Mean Time to Respond/Resolve (MTTR) and platform availability. Catnip_Games_SOC_Complete Catnip_Games_SOC_Complete Overall, the work demonstrates practical application of SOC architecture, security automation, incident management, threat analysis, containerised infrastructure, API-based integration, operational metrics and cyber-response procedures within a realistic gaming-industry security scenario. Catnip_Games_SOC_Complete Overview word count: approximately 330 words. Important before putting the presentation on a public Reference Library: redact any API keys/authentication tokens and other live credentials shown in the technical slides. The presentation includes API-key material in the Cortex and Python automation sections, so those credentials should also be revoked/rotated if they were ever active. Catnip_Games_SOC_Complete Catnip_Games_SOC_Complete

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Artificial Intelligence / Machine Vision / Computer Vision

Artificial Intelligence and Machine Vision: Neural Network-Based Image Processing Application

This postgraduate Artificial Intelligence and Machine Vision coursework requires students to design, implement and critically evaluate a neural network-based image-processing application addressing a real-world problem. Students may select an application such as medical imaging, plant or fruit classification, skin cancer detection, object detection or image segmentation and must demonstrate an appropriate end-to-end machine vision workflow. CN7023 Coursework T2 25-26 (1) The project begins with a clear definition of the selected real-world problem, the objectives of the proposed solution and its potential practical impact. Students are expected to demonstrate creativity in selecting and designing their approach, explain the neural-network or image-processing methods adopted and justify why the selected techniques are suitable for the chosen dataset and application. CN7023 Coursework T2 25-26 (1) A substantial component focuses on simulation and implementation. Students describe the dataset, including its source, size, classes and representative images, before explaining how image data were encoded and preprocessed for use within the chosen neural network. The report must then document the selected network architecture, learning algorithm and procedures used for training, validation and testing. CN7023 Coursework T2 25-26 (1) Model performance must be communicated using quantitative and visual evidence. Required outputs include test-set accuracy, accuracy curves across training, validation and testing, and a confusion matrix supported by appropriate explanation. Students must also critically analyse the results, identify factors affecting model performance and discuss alternative methods or simulation changes that could improve the solution. CN7023 Coursework T2 25-26 (1) The coursework permits several technical routes, including combining image processing with artificial neural networks, deep learning or computer vision, or focusing on one of these approaches independently. Development may be completed using MATLAB or Python. The wider module covers artificial neural networks, CNNs, digital image processing, image restoration, compression, segmentation, classification and ethical, legal, privacy and social issues associated with AI systems. CN7023 Coursework T2 25-26 (1) Module handbook 2526-B (1) Overview word count: approximately 335 words. Important note: the coursework cover page labels the assignment as “Individual Assignment 100%,” but the module handbook clarifies that the coursework report itself contributes 50% of the module, with the remaining marks allocated to MATLAB course completion (20%), lab tasks (15%) and presentation (15%). For the Reference Library, I would use the handbook’s 50% report weighting if you need to record the assessment contribution.

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Statistical Programming / Data Science / Business Analytics

Statistical Programming with R: Data Analysis, Probability, Regression and Business Decision-Making

This Statistical Programming assessment evaluates students' ability to apply statistical techniques and R programming to practical data-science and business decision-making problems. The individual assessment combines descriptive statistics, data preparation, visualisation, probability, regression, correlation and sampling, requiring students to demonstrate both conceptual statistical understanding and practical implementation in RStudio. The module learning outcomes emphasise the application of statistical methods to large and real-world datasets, critical evaluation of analytical techniques and awareness of legal, cultural and ethical issues associated with data analysis and dissemination. KL7012 - Statistical Programmin… The early tasks examine fundamental statistical reasoning. Students interpret weight-loss data comparing exercise classes with gym-only workouts using sample size, mean, mode and standard deviation, and explain an appropriate method for dealing with missing data, including its advantages and disadvantages. KL7012 - Statistical Programmin… A substantial practical component uses a cystic fibrosis dataset containing variables such as age, sex, height, weight, body-mass-related measurements, forced expiratory volume, residual volume, functional residual capacity, total lung capacity and maximum expiratory pressure. Students import the data into an R data frame, generate descriptive summaries and interpret the results. They then use scatterplots to investigate relationships between variables and sex-stratified boxplots to identify possible outliers. KL7012 - Statistical Programmin… The assessment also covers major probability models. Students apply probability concepts to healthcare survival, helpdesk email arrivals and fuel-demand scenarios, while also discussing how changing assumptions or real-world conditions can affect interpretation. These exercises assess understanding of statistical distributions and their application to operational and managerial decision-making. KL7012 - Statistical Programmin… Further analytical tasks examine linear regression and correlation. Students analyse the relationship between temperature and converted sugar in a chemical process, use a regression model to estimate the expected response at a specified temperature, and interpret relevant summary statistics. They also calculate and evaluate the suitability of a correlation coefficient for examining the relationship between advertising activity and product purchases. KL7012 - Statistical Programmin… The final and most substantial task involves a real-world M1 traffic-speed investigation for a manufacturing organisation. Students must design an appropriate sampling strategy, collect data from the specified Traffic England source, conduct statistical analysis in RStudio and develop evidence-based conclusions. The statistical report for this task is limited to 1,500 words and should include sampling methodology, collected data, statistical analysis, results, conclusions and relevant background research, supported by appropriate graphs, tables and charts. Raw data and RStudio calculations must be included in an appendix. KL7012 - Statistical Programmin… Overall, the assessment integrates statistical theory with R-based practical analysis, covering descriptive statistics, probability, visualisation, missing-data treatment, regression, correlation, sampling and critical interpretation of results in healthcare, operational and business contexts.

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Global Business Strategy 2,500 words

Global Business Strategy – Strategic Analysis of a Global Company

This assessment is an individual portfolio for the Global Business Strategy module. The assignment requires students to conduct a comprehensive strategic analysis of a named global company that is facing a significant business problem and to develop strategic options that can support the organisation's sustainable growth and future success. The selected company can be from any industry and may be a large or small global organisation. Students must identify a company that has experienced significant challenges during the past five years or is currently facing strategic difficulties. These may include loss of market position, inability to gain or regain market share, operational challenges, or declining revenues and profits. The assessment requires students to investigate the company's strategic purpose, vision, mission and objectives before examining the internal and external global business environment that may have contributed to its challenges. Relevant strategic theories and models should be applied to support the analysis. The portfolio is developed progressively across the semester. The first stage focuses on selecting the global company, identifying its key problem and analysing the internal and external business environment. The second stage examines sustainable business strategies, diversification and competitive advantage through a resource audit, together with an appropriate model for supporting global business growth. Students must also propose an appropriate mode and strategy for entering an international market in which the selected company does not currently operate. The third stage focuses on strategic change, leadership and governance. Students are expected to demonstrate an understanding of effective leadership styles, the role of leadership in strategic change, good governance and the importance of ethics in international business. Practical examples should be used to demonstrate how leadership, governance and ethical considerations can contribute to business growth. The fourth stage addresses good strategy execution and evaluation. Students must justify how the proposed strategy can be effectively implemented using available organisational resources and process management tools that support continuous improvement. The analysis should lead to strategic recommendations and an evaluation of how the proposed strategies could affect the company's future performance and competitiveness in the global market. After completing the individual portfolio tasks, students must prepare a summary report of approximately 1,000–1,500 words. This report should synthesise the key learning and insights gained from each task and demonstrate how the different strategic concepts have been applied to the selected organisation. The final e-portfolio is compiled as a single academic report in PebblePad and submitted as a PDF through Turnitin. The overall report should be approximately 2,500 words ±10%, excluding references and appendices, and sources should be cited using the ULBS Harvard referencing style.

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Business Ethics 2,500 words

Success Through Business Ethics – Ethical Analysis of an FMCG Brand

This assessment is an individual written report for the Success Through Business Ethics module. The assignment requires students to critically examine ethical issues and challenges within a Fast Moving Consumer Goods (FMCG) brand and evaluate how ethical theories, decision-making approaches and leadership practices can be applied to understand unethical business behaviour. The report has a total word limit of 2,500 words and accounts for 100% of the assessment. Students are required to select an FMCG brand that has been involved in unethical practices between 2000 and 2025. The selected brand may originate or operate anywhere in the world and may still be operating or may have ceased operations. Students must ensure that sufficient information is available about the organisation and its external environment before selecting the company. The report begins with an introduction explaining the nature of business ethics and introducing the selected FMCG brand and the unethical business practices associated with it. The main analysis requires students to select three normative ethical theories, explain their key principles and critically evaluate whether the company's behaviour adhered to or violated those principles. Students must use this analysis to explain why the identified business practices can be considered unethical. A second analytical section requires students to choose either ethical decision-making or social accounting. The selected concept must be explained and then critically applied to the unethical practices of the chosen FMCG brand. A further analysis focuses on the leadership style of the company's leadership team. Students must explain the principles of the leadership style, consider its positive and negative aspects, and analyse how the leaders responded to the unethical business practices. The report concludes by summarising the key findings from the analysis and providing two important recommendations for the selected brand. The recommendations should be directly relevant to the unethical practices identified and should be justified using evidence and findings from the report. Where a selected company has ceased operating, recommendations should still be provided on the assumption that the brand is operating. The assessment develops students' ability to apply ethical frameworks to business decision-making, understand ethical decision-making and corporate social responsibility, align ethics and values with business contexts, analyse ethical challenges in business strategies and operations, and evaluate moral dilemmas using economic, legal and ethical considerations. The report must use Harvard referencing for in-text citations and the reference list, with the reference list organised alphabetically.

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Engineering and Environment Advanced Practice 3,000 words

AP Research Project – Reflective Individual Report and Poster Presentation

This assessment is the Advanced Practice (AP) Research Project for the Engineering and Environment Advanced Practice London Campus Research Project module. It is designed for postgraduate students undertaking an independent research project with supervisory guidance. The assessment provides an opportunity to reflect critically on the research process, knowledge gained, professional development and challenges encountered during the project. Students are expected to demonstrate independent learning, research skills, critical thinking and the ability to communicate complex ideas in a professional context. The main assessment is a 3,000-word Reflective Individual Report, which contributes 80% of the module assessment. The report is structured around four key areas. The first section, Finding a Research Topic, requires students to explain how and why they selected their research topic, the research tools and search strategies used, the library collections explored, their research aims and objectives, and relevant discussions or recommendations received from their supervisor. The second section, Professional Activities, focuses on reflection on research development activities, scheduling, the independent researcher role, challenges such as time management, and communication with the supervisor. The third section, Literature Review and Research Work, requires students to critically review relevant literature, discuss their research method, demonstrate major research activities and consider the feasibility of the selected method, including challenges and recommendations. The final section, Reflection of Research Project, requires critical reflection on personal strengths and weaknesses, continuous self-development and the relevance of research activities to the student's programme of study and future career. Areas such as decision making, problem solving, communication and influencing, technical skills, collaboration, innovation and proactivity should be considered. The second component is a 10-minute Poster Presentation worth 20% of the assessment. The poster should provide a balanced combination of visuals and text and present the research objectives and significance, a summary of the literature review and research method, key research findings, and conclusions and recommendations. The assessment also requires appropriate academic presentation, including a cover page, table of contents, page numbers, figure and table captions, numbered headings and consistent formatting. Harvard or APA referencing may be used. The report is submitted through Turnitin and is subject to anonymous marking. The assessment is a Pass/Fail module, with students required to achieve at least 50% to pass.

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Applied Data Science 500 words

Critique of a Data Science Book and Selected Chapter

This assignment requires students to critically evaluate a selected data science book and a specific chapter from that book. Students must choose one book from the list provided in the assignment, read the preface or introduction to understand the intended audience, and then select a chapter that is relevant to their interests, existing knowledge and learning objectives. The available books cover a range of data science and analytical subjects, including practical time series analysis, machine learning with Python, Python-based data science, technical analysis, Bayesian statistics and artificial intelligence applications. The main purpose of the assignment is to develop the student's ability to engage critically with technical literature rather than simply summarising its content. The critique should identify the selected book and its intended audience, clearly state the chosen chapter and explain the reasons for selecting it. Students are expected to consider what they hoped to learn from the selected material and then critically assess whether the chapter achieved these objectives. The assessment should consider the clarity, usefulness and accessibility of the material, as well as the extent to which it contributes to the student's understanding of data science concepts. Students should also discuss additional knowledge they would like to gain from the book and identify particular aspects that were either helpful or less useful. This may include the quality of explanations, examples, technical depth, practical applications, organisation of material and relevance to the student's existing knowledge. The critique should demonstrate engagement with the selected chapter and provide reasoned observations rather than simply describing what the author has written. The final submission is a 500-word critique with a permitted variation of plus or minus 10 percent, meaning the expected range is approximately 450–550 words. The text must be written as a continuous narrative and should not use subheadings for the individual assessment points. The headline should follow the format “Critique of <book title> by <book author>”, with the student's name and student ID as the subtitle. The assignment assesses both technical presentation and content, including grammar, writing style, word count, completeness, breadth and depth of the book assessment, critical analysis and evidence of engagement with the selected material. Students must submit text that can be processed by Turnitin.

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Computing

Independent Critical Analysis 30

Independent Critical Analysis 30 is an individual assessment that develops students' ability to critically analyse ethical, social, legal and professional issues arising from a computing-related scenario. The assessment is worth 30% of the overall module assessment and requires students to respond to three questions relating to the ethical and professional issues presented in the scenario. Students are expected to apply the specific analytical techniques or approaches identified in the assessment instructions when developing their responses. The assessment focuses on the critical examination of ethical issues and professional responsibilities within contemporary computing contexts. Students are expected to identify relevant ethical concerns and provide a reasoned justification for their analysis. The marking criteria emphasise the identification of a single ethical issue or ethical risk, followed by a structured analysis using the EHAZOP Framework. Higher-level responses are expected to provide clear justification for the ethical issue selected, examine the associated consequences, and demonstrate a systematic and critical approach to the scenario. A second component focuses on ethical risk analysis using the Ethical OS Toolkit. Students are expected to identify an ethical risk associated with the scenario and justify their selection through a structured analysis. The assessment therefore requires students to consider the potential consequences of computing-related decisions and to demonstrate an understanding of how ethical risks can be identified and evaluated. The final component addresses professional issues. Students are expected to identify relevant professional principles and apply an appropriate professional code of conduct, such as the ACM or BCS Code of Conduct, to the scenario. The analysis should explain which professional principles are relevant and provide a reasoned justification for their application. The marking criteria place emphasis on systematic analysis, appropriate justification and understanding of responsible computing practice. The assessment therefore connects ethical decision-making with professional responsibilities and standards. The assignment also assesses broader module learning outcomes relating to ethical standards, contemporary computing contexts, legal and professional issues, and the ability to critically analyse high-profile cases or case studies. Students are required to submit the provided answer sheet containing the scenario and three questions. The assessment brief states that submissions are subject to Turnitin and that students must follow the specified submission requirements.

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Computer Science 2,800 words

Specialism Research Report

The Specialism Research Report is an individual assessment for the Advanced Research Topics in Computer Science module. The assignment provides students with an opportunity to develop a deeper understanding of a selected computer science research specialism and to investigate an open research question within that area. The available research specialisms are Artificial Intelligence, Networking, Cyber Security, Software Engineering and Data Science. Students are expected to examine the research specialism associated with their degree route and develop their understanding of current research challenges, established techniques and potential future research directions. The report requires students to provide an overview of the research paper presented in the relevant 7COM1084 Research Specialism Lecture. The report must identify and explain the research question addressed by the specialist paper, consider why the research problem is scientifically interesting or relevant to a real-world application, and investigate possible approaches for addressing the question. Students must also identify further research that could build upon the existing work and consider their own personal strengths, interests and experience in relation to the proposed research. The report is structured into five main sections. The first section, Introduction of Research Specialism, provides a general overview of the selected research area and is intended to be accessible to readers with broad computer science knowledge. The second section, Open Research Question, identifies and explains the research problem discussed in the specialist lecture paper and considers its scientific or practical significance. The third section, Existing and Related Work, provides a literature review of research beyond the specialist paper, identifies limitations in existing approaches and discusses related open research problems. The fourth section, Research Approach, examines the methods used in the specialist paper, evaluates their strengths and weaknesses, and proposes an approach for extending the research. The fifth section, Personal Investment, explains the student's interest in the research question and evaluates their personal strengths and experience relevant to conducting the proposed research. Students are expected to use module reading materials and secondary research to support their discussion. The report must use Harvard referencing and include at least 20 references, including the 7COM1084 specialist paper. The references are excluded from the word count. The submission must not exceed 2,800 words, with a permitted range of ±10%. The assessment develops students' ability to understand established research techniques, identify research problems from relevant literature, propose alternative solutions, critically evaluate research literature, develop approaches for future research and communicate research knowledge effectively in a scholarly manner.

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Environmental Sustainable Engineering 3,000 words

Environmental Sustainable Construction & Logistic Sources – EG7037

This coursework assesses Environmental Sustainable Engineering and Logistic Sources through a large-scale construction project scenario. The student is appointed as the project manager for a £320 million design-and-build project involving the development of a new shopping centre in central London. The project is planned over a 48-month period and is presented as a flagship initiative focused on environmentally friendly and sustainable construction practices, with government support for its sustainability commitments. The project manager is responsible for protecting the client's interests while ensuring that project standards, schedules, budgets, procurement requirements and quality expectations are achieved. The assignment is structured around environmental sustainability, construction logistics, procurement, renewable energy, health and safety, and sustainable design. Part A requires investigation of the environmental, economic, historical and cultural factors that could contribute to significant project costs and delays. It also requires discussion of four renewable or sustainable energy sources that could be used within the project, together with justification for each selected energy source. These activities require consideration of the project's location, sustainability objectives and practical construction requirements. Part B focuses on supply chain management and health and safety. Students must outline the supply chain management and procurement process for the material resources required for the construction project. This includes consideration of how appropriate materials and suppliers can be sourced and managed. The section also requires identification of key health and safety considerations during the production phase and discussion of the associated risks. Part C focuses on sustainable design systems. Students must discuss the need for implementing passive or active systems during the design stage and identify appropriate tools or techniques that can support their application. The potential environmental impacts of these systems must also be considered. The assignment therefore requires students to connect sustainable design decisions with environmental performance and the wider objectives of the construction project. The completed coursework should be approximately 3,000 words, excluding appendices and labelled diagrams or sketches. Students are expected to provide a well-researched and clearly structured written account using appropriate textbooks and other relevant sources. Images, photographs and diagrams should include captions and be cross-referenced within the text. The submission should include a conclusion and a properly cited bibliography or reference list. References must follow the Cite Them Right requirements. The coursework is submitted electronically through the designated Turnitin link.

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Digital Marketing

Three Weekly Digital Marketing Tutorial Challenges

This assessment consists of three weekly digital marketing tutorial challenges designed to develop practical skills in digital marketing, consumer insights, content creation, inclusive communication, ethical storytelling and campaign design. The assessment accounts for 45% of the module and requires students to apply digital marketing theory and consumer insight techniques in real-world marketing contexts. The activities are completed through tutorial-based learning, where students participate in discussions, receive tutor guidance and develop their work before submitting it through Canvas. The assessment focuses on applying consumer insights, data analytics tools and digital marketing strategies to create meaningful and responsible customer experiences. Challenge 1, titled “Are You Being Inclusive?” – Brand Audit & Persona for Ethical Reach, focuses on ethical digital auditing and inclusive persona design. Students select a brand from their home country and evaluate the inclusiveness and accessibility of its digital marketing across its website, social media and paid advertising. They then develop two to three detailed customer personas representing underserved or marginalised customer groups. Students also use AI analytics tools such as Google Trends, SparkToro or simulated AI insights to identify audience behaviours and preferences. The challenge is linked to SDG 10 – Reduced Inequalities and includes consideration of the ethics of using AI in digital marketing and its effect on the credibility of consumer insights. Challenge 2, titled “Don't Just Capture Attention – Respect It” – Campaign Idea Pitch, focuses on sustainable branding and growth. Students research the client's existing branding, examine opportunities to expand or sustain its audience and use consumer insight tools such as Google Trends, reviews and surveys to understand public perceptions. Students then create a visual mini-campaign promoting sustainable and transparent brand messaging, including social media content and AI-generated visuals. The campaign is connected to SDG 16 – Peace, Justice and Strong Institutions. Challenge 3, titled “Create, Educate, Disseminate & Evaluate”, develops digital social marketing and consumer engagement. Students extend the campaign developed in Challenge 2 into an interactive digital event that promotes positive behavioural change and educates audiences about a social issue. The activity must consider audience interests, increase interactivity and engagement, and provide evidence of positive engagement. This challenge is linked to SDG 4 – Quality Education. Overall, the assessment develops students' ability to combine digital marketing strategy, consumer insights, ethical considerations, sustainability and creative campaign design in practical marketing activities.

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Management 5,000 words

Principles of Management – Tesco Management Report

This assessment requires students to produce a 5,000-word management report analysing the current management practices of Tesco. The report is written from the perspective of a business consultant and is intended for Tesco’s senior management team. Students are required to apply management theories, concepts and practices covered in the module, supported by appropriate academic literature, case study materials and independent research. The assessment also requires students to develop a personal Skills Development Plan and a 500-word Reflective Statement focused on their own management competencies and future career objectives. The report addresses four main tasks. Task 1 focuses on key management theories, concepts and practices and requires students to explain how management contributes to Tesco’s success and adds value. Students should discuss the four philosophies of the Competing Values Framework and identify one additional relevant management theory that supports the analysis of Tesco’s management approach. Task 2 evaluates management as a value-adding universal activity by examining the dynamics of the global business environment and how Tesco adapts its strategies and methods at global, national and local levels. Students may apply frameworks such as LoNG-PEST or Porter’s Five Forces and should identify one global opportunity and one global threat. Task 3 critically analyses the key principles and functions of management, including planning, organising, leading and controlling. Students must identify one internal challenge faced by Tesco and evaluate how management functions can be applied to address it, supported by an appropriate academic model such as Value Chain analysis, VRIO or Mendelow’s Matrix. Task 4 focuses on critical reflection and skills development. Students are required to complete a Personal SWOT, prepare a Skills Development Plan addressing their future career aims and objectives, and provide a 500-word Reflective Statement using an appropriate reflection model such as Borton, Kolb’s Cycle, Maslow’s or GROW. The reflection should focus on professional skills including self-management, problem-solving and decision-making. The report should conclude with recommended changes that Tesco should implement to improve its success. The assignment requires academic writing, critical analysis, evidence-based arguments and Harvard referencing. The main body consists of an introduction, four assessment tasks and a conclusion, while the cover page, contents, references and appendices are excluded from the 5,000-word limit. :contentReference[oaicite:2]{index=2} :contentReference[oaicite:3]{index=3}

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Leading Through Digital Change 2,500 words

Leading Through Digital Change (CW1)

The Leading Through Digital Change (CW1) assessment is a summative coursework assignment for MSc Management programmes at BPP University Business School. The assessment accounts for 100% of the module marks, with a minimum of 50% required to pass. Students must submit the assessment through Turnitin and follow the Harvard Referencing System. The coursework consists of a 1,500-word business report together with an A4-size poster. The assessment requires the student to act as a Digital Transformation Consultant responsible for coordinating technology expertise within an organisation. The Chief Information Officer (CIO) requires a Digital Transformation Report and Poster that evaluate and recommend changes designed to maintain competitive advantage and create business value. Students must select one organisation from the options provided in the assessment brief: Tata Motors, Meezan Bank Limited, Hulu, or Puma SE. Part 1 addresses the Digital Transformation Strategic Framework and is linked to Learning Outcome 1. The student must critically review and propose one appropriate digital transformation framework for the selected organisation. The brief specifies that one framework should be used from McKinsey 4Ds, BCG 3 Stages, Gartner's 6 Steps or Cognizant's 4 Pillars. The section should also include digital transformation objectives supporting areas such as operations, ICT and marketing. Part 2 addresses Future Digital Trends and Learning Outcome 2. Students must design a poster evaluating two disruptive technologies or techniques likely to affect the smartphone industry, employment and the labour market over the next five years. Suggested technologies include Artificial Intelligence and Machine Learning, 5G Connectivity, Internet of Things, Robotics, Drone Delivery, Blockchain, and Augmented or Virtual Reality. Academic literature and real-life examples should support the discussion. Part 3 addresses Digital Leadership Recommendations and Learning Outcome 3. Students must analyse and propose two appropriate digital leadership styles that the selected organisation should develop to effectively manage and support digital transformation. The brief identifies possible approaches including hyperaware agile leadership, ethical-tech leadership, people-oriented leadership, agile leadership and Goleman's six leadership styles, while allowing other relevant approaches. The required report structure includes the BPP University administration cover sheet, table of contents, list of abbreviations where appropriate, introduction, Part 1, Part 2 (Poster), Part 3, conclusion, references and appendix where required. The main business report is limited to 1,500 words, while the poster has no separate word limit but must fit on A4 paper. The report must be written in the third person, use professional formatting, include page numbers, correctly label tables and figures, and use Harvard in-text citations and references. The marking criteria emphasise critical evaluation rather than simple description. Higher performance requires evidence of extensive personal research, critical analysis of digital transformation frameworks, emerging technologies and leadership approaches, supported by academic literature and real-world examples. The rubric also expects strong links between the organisation's technological challenges, transformation objectives, disruptive technologies and proposed leadership approaches.

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Engineering and Environment Advanced Practice London Campus Research Project 3,000 words

LD7152 – AP Research Project

The LD7152 AP Research Project is a postgraduate research and reflective assessment for students studying within the MSc Computing Framework or MSc International Project Management programme. The module focuses on developing students as independent researchers and requires them to reflect critically on their research strategy, activities, learning and personal development. The assessment is designed to encourage students to recognise their achievements, identify challenges encountered during the research process and evaluate how their research experience has contributed to their academic and professional development. The assessment consists of two components: a 3,000-word Reflective Individual Report worth 80% and a 10-minute Poster Presentation worth 20%. Students are expected to work independently while receiving supervisory guidance. The reflective report requires students to demonstrate critical engagement with knowledge discovery and reflect on their educational development in relation to the challenges experienced during their research project. The report is structured around four main areas. The first section, Finding a Research Topic, is approximately 500 words and requires students to explain their chosen topic, why it was selected, the research tools and search techniques used, the Library collections explored, the research aims and objectives, and relevant discussions or recommendations from their supervisor. The second section, Professional Activities, is approximately 500 words and focuses on research development tasks, scheduling, the independent researcher role, challenges such as time management, and communication with the supervisor. The third section, Literature Review and Research Work, is approximately 1,500 words. It requires a critical review of relevant literature with appropriate citation, discussion of the research method and major research activities, and consideration of the feasibility, challenges and recommendations associated with the selected method. The fourth section, Reflection of Research Project, is approximately 500 words and focuses on achievements, contributions, strengths and weaknesses, continuous self-development and employability. Students are expected to reflect on areas including decision making, problem solving, communication and influencing, technical skills, collaboration, innovation and proactivity. The second assessment component is a 10-minute poster presentation. The poster should provide an overview of the research activities and communicate what the student learned during the research process. It should balance visual and textual information and include the research objectives and significance, a summary of the literature review and research method, key research findings, and conclusions and recommendations. The report must include a cover page, table of contents, page numbers and captions for figures and tables. The required formatting includes Times New Roman, 12-point font, numbered headings and approximately 1.2–1.3 line spacing. The assessment brief permits Harvard or APA referencing. The report is submitted electronically through Turnitin on Blackboard. The module is assessed on a Pass/Fail basis, with students required to achieve 50% or above to pass. The assessment also evaluates critical reflection, application of knowledge, independent learning, communication of complex ideas, personal development and reflection on technical leadership.

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Team Research and Development

Team Research and Development – Research Question, Hypothesis and Statistical Data Analysis

This group assessment for 7COM1079 – Team Research and Development requires students to collaboratively select a dataset and conduct a structured research investigation using data analysis. The assessment is worth 40% of the module mark and requires group members to work together to develop and submit a final research report. All group members are expected to contribute equally to the work, with one designated group member submitting the final assessment on behalf of the group. The main objective of the assignment is to develop a meaningful research question and corresponding hypothesis based on a selected dataset. The research question defines a specific claim or issue that the group intends to investigate and provides the starting point for constructing and testing an evidence-based argument. The hypothesis provides a proposed explanation or prediction that can be examined through analysis of the available data. Together, the research question and hypothesis establish what evidence is required, how that evidence will be evaluated and how effectively the findings can support or challenge a particular position. The assignment permits three types of research questions. The first involves establishing a difference in means between two groups. The second involves establishing a difference in proportion between two groups. The third involves establishing a correlation between two measures. The selected research question should therefore be appropriate for the characteristics of the chosen dataset and should allow the group to perform meaningful statistical analysis. Following the selection of the dataset and formulation of the research question and hypothesis, students are expected to produce appropriate data visualisations and statistical analysis. The analysis should provide evidence relevant to the research question and allow the proposed hypothesis to be examined systematically. The resulting findings should be interpreted in relation to the original research question and hypothesis rather than simply presenting numerical results. The assignment provides a Microsoft Word final report template containing the required table of contents, chapter and subchapter names and explanations of the expected content. Students are instructed to download and use this template when preparing their final report. Work produced by individual students during their first assignment may be incorporated where the student examined the same dataset allocated to the group, provided the material is appropriately incorporated into the group submission. All italicised instructional text in the template must be removed before submission. The completed amended template and the group's dataset file must be submitted through Canvas. Acceptable submission formats include PDF, DOC, DOCX, CSV and XLS. The assignment has a late-submission penalty, and the submission deadline is stated as being available in the assignment specification on Canvas. Assessment is based on the criteria provided in the module rubric. The rubric is used to assess the group's work, with group members initially receiving the same mark, although peer review may be taken into account. The assignment therefore combines collaborative research, statistical reasoning, data visualisation, analytical interpretation and academic report writing. The assignment instructions explicitly state that students should not use AI for this assessment. The module team also reserves the right to arrange a viva if academic misconduct is suspected. The final report should therefore represent the group's own research, analysis, interpretation and contribution.

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Data Mining

Data Mining – Classification, Model Optimisation and Evaluation

This individual Data Mining assignment requires students to apply the techniques covered in the module using the WEKA data mining platform. The assessment is worth 40% and focuses on practical application of machine learning and data mining methods, requiring students to configure algorithms, analyse datasets, optimise model parameters, evaluate classification performance and explain their technical choices and results. The assignment also assesses the ability to critically evaluate different algorithms and models of data mining. The assignment includes several tasks covering different stages of the data mining process. Students are required to work with supplied datasets and use appropriate preprocessing and classification techniques. The datasets include a balanced screenshot dataset containing processed screenshots classified into categories such as "Okay" and "Bad", where the objective is to train a model capable of identifying inappropriate content. The data has been processed using PCA to provide a smaller four-dimensional representation while protecting privacy and reducing the size of the data. Another supplied dataset concerns furniture reviews, containing positive ("pos") and negative ("neg") written feedback, with the objective of building a model that can determine whether a furniture review belongs to either class. The assessment evaluates students' ability to understand and describe the datasets, including the number of instances, number of columns, data types and relevant statistical information. For text-based data, students must apply appropriate vectorisation and describe the resulting dataset characteristics. Students must also consider class imbalance and apply an appropriate method where necessary, explaining how the chosen approach affects the distribution of instances between the classes. A significant component of the assignment involves classification algorithms and parameter optimisation. The assessment requires students to work with algorithms including Naive Bayes, LibSVM and J48. Students must investigate appropriate parameters and perform parameter searches or fine grid searches to identify suitable configurations. They must explain the selected parameters, their impact on the model and the reasoning behind the chosen values. Model performance must be evaluated using appropriate validation techniques, including cross-validation. Students are required to compare the algorithms using results such as overall accuracy and confusion matrices. The assignment expects students to identify an appropriate or best-performing algorithm in the context of the dataset and to provide a clear explanation of the comparison rather than simply reporting numerical results. The rubric places emphasis on accurate configuration, clear explanation of parameter choices, dataset analysis, class-balance treatment, parameter optimisation, cross-validation and critical comparison of algorithm strengths and weaknesses. High-quality work should explain both the technical process and the implications of the results, with results presented clearly through appropriate tables, confusion matrices and graphical outputs where required. The submission must be a single PDF document containing the report and must not exceed 10 pages. Students are instructed to include their student ID at the beginning of the report but not their name or other identifying details so that marking remains anonymous. Screenshots are specifically required to demonstrate use of the student's ID number as the random seed; other WEKA results should be presented in the student's own tables or result formats. The brief also states that no research beyond the material covered in the module is required and therefore no citations or reference list are required. The assignment explicitly prohibits the use of Generative AI tools for creating content and prohibits using GenAI tools or proofreading services for proofreading. Students are expected to complete the practical work themselves and explain their own technical choices and results.

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7COM1076 4,000 words

7COM1076 Network Design, Modelling and Evaluation Coursework

This 7COM1076 coursework requires students to design, model, emulate, test and evaluate networking environments for a new department building at the University of Hertfordshire. The assessment combines practical network modelling with theoretical analysis and evaluation, covering wireless and mobile networking, cloud networking, Software Defined Networking (SDN), network applications and contemporary networking technologies. The assignment is designed to develop practical knowledge through Python-based network emulation and critical understanding through academic research. Task 1 focuses on wireless and mobile networking. Students must use Python to emulate a building containing three WiFi access points and two user stations, identified as UE1 and UE2. The access points are connected using a physical linear topology, while the stations use Class C private IP addresses. The task requires configuration of SSIDs, passwords, channels, ranges and coordinates, with WPA2 encryption and standalone fail mode. The stations must also be configured with mobility, following specified movement sequences and speed ranges. Students must discuss the design and implementation, complete the configuration and mobility tables, provide the Python script used with the Mininet API, and include screenshots demonstrating mobility, access-point association and successful ping connectivity. Task 2 examines cloud services using Mininet and Render.com. Students must emulate a network containing three switches and two hosts and deploy a simple static website using Render.com. Host H1 must access the deployed webpage through the xterm environment. Deliverables include a discussion of the design and implementation, the Python script, evidence of configuring Render.com with the GitHub repository, commands used to connect H1 to the internet and access the webpage, screenshots of the webpage through xterm, and the HTML code of the website. Task 3 focuses on Software Defined Networking and connectivity. Students must emulate an environment containing ten hosts and three servers using a linear topology, with an ONOS controller enabled for control-plane programmability. The task requires configuration of assigned IP and MAC addresses, demonstration of connectivity between hosts and servers, and a UDP transmission using a duration of 600 seconds and bandwidth of 100 Mbps. Students must provide a design discussion, Python emulation script, ONOS GUI screenshot, full ping connectivity evidence and the required UDP transmission results. Task 4 is the analysis and evaluation component. Students must critically discuss three contemporary networking issues using academic sources. These include the challenges faced by a small-to-medium organisation providing services to the UK National Health Service when migrating from on-premises infrastructure to cloud computing; the opportunities and challenges of incorporating Software Defined Networking into future optical networks; and whether WiFi and 5G should coexist to provide ubiquitous services to users. The final report must use 12-point font, normal margins and Harvard referencing in accordance with University of Hertfordshire guidelines. The required report length is 4,000 words with a permitted variation of plus or minus 10%, excluding the title page, contents page, references and appendix, with a maximum page limit of 25 pages. The recommended structure includes an introduction, discussions and results for Tasks 1–3, the three Task 4 analysis sections, conclusion, references and an appendix containing the Task 1, Task 2 and Task 3 code. The assessment covers wireless and mobile networking, cloud and SDN networking, network applications, analysis and evaluation, MCQ tests and overall report quality. The marking allocation includes WiFi networking, mobility and ICMP, cloud configuration, SDN networking, cloud web page, UDP, three analysis sections, two MCQ tests and quality of the report.

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Networking and Security Practice

Networking and Security Practice – Recorded Demonstration

This assessment for the MSc Cyber Security module Networking and Security Practice is a practical recorded-demonstration coursework designed to assess students' ability to configure, secure, troubleshoot and monitor a virtualised sandbox network. The assessment contributes 60% of the module mark and consists of four recorded demonstration videos, with each video limited to a maximum of five minutes. The completed videos are submitted through a Moodle quiz as video files or accessible links. The practical environment uses virtual machines running Ubuntu Server and Ubuntu Desktop on VirtualBox or UTM. Students deploy a four-machine architecture consisting of a Gateway, Webserver, Workstation and Zabbix-Server. The Gateway acts as the router, firewall and NAT gateway; the Webserver hosts an Nginx web server; the Workstation is used for administration and testing; and the Zabbix-Server provides network monitoring. The architecture requires appropriate network interfaces, IP addresses, routing and communication between the different internal networks. The coursework develops practical networking and system administration skills through several phases. Students install and configure operating systems, allocate virtual machine resources, configure static IP addresses, enable IP forwarding and NAT masquerading, and implement firewall rules using iptables. They also develop command-line proficiency using networking, DNS, security, remote-access and web tools including ping, traceroute, ss, nslookup, dig, nmap, Wireshark, netcat, SSH, SCP, rsync, curl and wget. Students must also configure secure remote access and deploy an Nginx web server. SSH must be hardened by disabling password authentication and root login, while key-based authentication is used for secure access. The Webserver must contain a customised landing page showing the student's name and student ID, which is accessed from the Workstation. The monitoring component requires installation and configuration of Zabbix, deployment of agents across the virtual machines, host monitoring, a customised dashboard containing at least five live-data widgets and configured alerts or triggers. Students must explain what their selected monitoring elements measure and why they are operationally useful. The traffic-analysis component uses Wireshark to capture and examine HTTP, ICMP and DNS traffic. Students apply appropriate filters, identify protocols using the Protocol Hierarchy and discuss the security implications of unencrypted HTTP traffic, including secure alternatives such as HTTPS and DNS over TLS. The security-evaluation component uses Nmap within the isolated virtualised sandbox to identify open ports and services, assess vulnerabilities and recommend mitigations. Students must also demonstrate iptables forwarding and NAT masquerading rules. The four videos cover Network Infrastructure, Network Monitoring, Traffic Analysis and Security Evaluation. The assessment is marked out of 100, with 35 marks allocated to Network Infrastructure, 25 to Network Monitoring, 20 to Traffic Analysis and 20 to Security Evaluation. Students are expected to provide clear voice narration, explain commands and outputs, demonstrate technical understanding and critically relate their work to network security. The assessment develops employability skills in Linux administration, remote system management, network troubleshooting, security hardening, packet analysis, port and service scanning, virtualisation and network monitoring. It also requires students to conduct security testing ethically within their own isolated virtualised environment and prohibits unauthorised scanning of university networks, public websites or other systems.

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Security of Emerging Connected Systems 1,500 words

CW1: Policy and Legal Aspects Report – IoT System

This 1,500-word report for the Security of Emerging Connected Systems module examines the legal and security implications of a proposed Internet of Things (IoT) system designed for consumers to monitor food intake and bodyweight. The coursework requires students to provide an initial investigation of the potential legal pitfalls associated with the proposed product and identify appropriate solutions or mitigation measures. The report is worth 5 credits and is assessed as an individual written report. The proposed IoT system consists of several connected components. A smartphone application allows users to scan barcodes of processed food to record calorie and nutritional information against their health record. A kitchen scale communicates with the phone application to record the weight of ingredients used in home-cooked meals. A bathroom scale records the user's weight and provides light and sound reminders to encourage regular measurements. A UK-based server stores the collected information and generates individual user reports. The main purpose of the report is to ensure that the company understands the UK and international laws that may apply to the proposed system. Students must identify potential legal issues and recommend appropriate mitigation through technology, organisational policy, licensing arrangements or user agreements. The system definition is deliberately broad, so students should not make unsupported assumptions about its design. Where several options have different legal implications, the report should compare the relevant alternatives and explain their implications rather than presenting only one solution. The initial product is intended for UK residents, while the company is considering future expansion into the United States. Consequently, the report should focus primarily on UK law but also include a short section discussing legal aspects that may need to be reconsidered when entering the US market. The report is intended for company executives and may subsequently be provided to the R&D department. Therefore, high-level outcomes should be communicated early, while useful links to technical information such as encryption schemes, protocols and frameworks may be provided without extensive technical explanations in the main report. The assessment places 50% of the marks on understanding and coverage of UK and US law, 40% on technical recommendations and 10% on report presentation. Strong submissions are expected to provide comprehensive coverage of relevant legislation, connect legal issues with the wider security context, analyse technical recommendations for both regions, identify differences between UK and US requirements and support arguments with appropriate citations and a wide range of sources. The assignment learning outcomes focus on critically evaluating the role of security policy in protecting information assets and proposing appropriate policies for internet-based technologies. They also require students to demonstrate an understanding of key legislation relating to information security and how legislation influences organisational security policy. The final report should therefore combine legal analysis with practical security recommendations, addressing the proposed IoT system from both UK and US perspectives while remaining suitable for both technical staff and non-technical management.

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Corporate Strategy 4,000 words

Strategic Case Study – Corporate Strategy

This Corporate Strategy assessment requires students to undertake a critical strategic analysis of a chosen organisation and develop evidence-based recommendations for its long-term growth and competitive position. The assignment is presented as a 4,000-word Strategic Case Study and focuses on analysing practical business situations through the application of corporate strategy theories, analytical tools and strategic frameworks. The assessment aims to develop students' ability to evaluate an organisation's competitive advantage, identify significant strategic challenges and opportunities, and propose practical strategies for sustainable growth. The report begins with an introduction explaining the purpose of the case study and identifying the selected company. The strategic positioning section then examines the organisation's current competitive advantage, differentiation from competitors, fundamental competencies and resources. Relevant theories and strategic frameworks should be applied to support the analysis. The external environment section requires a critical evaluation of industry attractiveness using Porter's Five Forces. Students must also examine the factors driving organisational change, compare the selected organisation with close rivals and provide strategic recommendations for addressing identified challenges. The international strategy section critically analyses the effectiveness of the company's existing international expansion strategy and requires students to propose a new expansion strategy for a geographical location in an international market. An appropriate internationalisation theory and market-entry mode should be applied to support the proposed strategy. The challenges and opportunities section examines major issues affecting the organisation, including areas such as market competition, technological disruption and regulatory change. Students must identify opportunities that could strengthen the company's competitive position and use an appropriate framework to consider the development, leadership and implementation of organisational change. The proposed leadership-driven strategy should also be reflected upon in relation to the company's strategic goals. The final main section provides two to three actionable strategic recommendations that the organisation should implement to sustain or enhance its competitive advantage. Students are also required to include an appendix of no more than 500 words containing concise explanations, models or frameworks demonstrating their understanding of theories and concepts relevant to the assessment. The appendix should support the main analysis and be clearly referenced within the report. The assessment develops several strategic management capabilities. The assessed learning outcomes include critically analysing a company's strategic position using analytical tools and theoretical frameworks; developing corporate-level strategies and considering their implementation and control; integrating innovative and sustainable practices into strategic planning; and using performance measurement instruments to assess strategic initiatives. The brief also aligns with CMI Level 7 Strategic Management and Leadership Practice units covering strategy development and strategic change. The submission is an individual written coursework assignment submitted through Turnitin. The written assignment must be submitted as a Microsoft Word document rather than PDF. Students must acknowledge and reference any AI tools used in developing the assignment and provide appropriate acknowledgement of all sources.

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Digital Security Risk and Audit 2,000 words

Digital Security Risk and Audit – Information Security Audit of ABC Air

This individual coursework for the Digital Security Risk and Audit module requires students to prepare a 2,000-word information security audit report based on a case study involving ABC Air, a small aircraft service company responsible for aircraft maintenance for civil operators. The company records information including aircraft flying hours, servicing time, engineers' man-hours and related maintenance activities. An external contractor has also provided a report outlining a possible solution for ABC Air. Students are required to assess the information security risks associated with the scenario, complete an information security audit and produce a professional report, clearly identifying any assumptions made during the analysis. The assessment requires students to examine risk assessment, identification and analysis as part of the audit process. A suitable auditing approach must be selected and justified, with students considering either a general risk-based approach or a specific control-based approach. The report should explain why the selected approach is appropriate for the ABC Air scenario and demonstrate how it can be applied to the organisation's information security environment. The coursework also requires consideration of potential cyber attacks and their use within an integrated fault event analysis. Students must identify relevant information security threats and examine how an attack could affect the organisation and its information assets. The report should further identify appropriate standards, best practices or guidelines that could be used to mitigate information security breaches. These should be critically evaluated, including discussion of their advantages and disadvantages rather than simply being listed. The assessment develops students' ability to apply information security governance and audit practices within legal, ethical and professional contexts. It also requires consideration of recognised industry frameworks such as COBIT and international standards including the ISO 27000 series. Students are expected to perform systematic risk assessment and analysis, critically evaluate information assurance reference models, and select appropriate information security audit strategies for complex real-world scenarios. The marking criteria place particular emphasis on the quality of the information security audit, identification and adoption of appropriate international standards and frameworks, and critical evaluation of the benefits and limitations of security audit frameworks. The assessment allocates 20% to identifying and applying an appropriate audit approach, 50% to completing the conceptual information security audit and assurance, and 30% to interpreting and critically evaluating information assurance reference models. The report is an individual assessment and must be submitted as a PDF through Aula/Turnitin. The brief states that APA referencing should be used for the work and that all sources and any AI tools used must be acknowledged.

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1,500 words

Network Security Evaluation and Monitoring – Reconnaissance, Incident Response and APTs

This coursework assesses the research and analytical abilities required to design and evaluate an effective network security evaluation and monitoring solution. The scenario places the student in the role of a network security evaluation specialist responsible for helping a client design and build a monitoring solution for a complex client network. The client operates in the defence and security sector, works with government departments, multinational organisations and foreign agencies, and handles sensitive information. The network includes several server farms, gateway nodes, hundreds of client nodes, internal application services, externally accessible services and wireless access points. The organisation is considered vulnerable to threats such as sabotage and intellectual property theft. The coursework requires all questions to be answered in the given order within a single report. An abstract is not required, and students are expected to use technical terminology precisely. Relevant and clearly labelled illustrations are encouraged. Where assumptions are required about security software, hardware or services already deployed on the network, these assumptions must be clearly identified in a dedicated “Assumptions” section at the beginning of the report. Question 1 focuses on detecting network reconnaissance originating from inside the organisation. Students must explain how an insider could collect and use reconnaissance information for malicious purposes, identify the types of data that should be collected and the appropriate network locations for collection, and justify the selection of monitoring data. The question also requires recommendations for suitable tools and configurations to detect reconnaissance activity, together with strategies for dealing with the scale and high traffic volume of the client network. This section carries 30 marks and has a suggested length of 500 words. Question 2 focuses on incident response following a confirmed security incident. The scenario involves suspicious out-of-hours activity and an external flash drive connected to a workstation at gateway 10, a large number of files being opened on a file server at gateway 9, and significant traffic between the workstation and a database server at gateway 5. Students must determine which previously collected data would be relevant, explain the evidence expected from that data, and recommend additional network and endpoint data that should be collected. The proposed approach must be forensically sound so that evidence can potentially be used in court. This section carries 50 marks and has a suggested length of 700 words. Question 3 addresses Advanced Persistent Threats (APTs) and evaluates the effectiveness of the proposed monitoring solution. Students must recommend appropriate testing to determine whether the monitoring system operates according to its specifications and objectives, explain the types, timing and location of testing, and identify suitable qualifications, certifications, knowledge and tool experience for security testers. The section also requires discussion of APT behaviour and how the proposed monitoring mechanisms could detect or prevent such activity. This section carries 20 marks and has a suggested length of 300 words. Overall, the coursework develops skills in network security monitoring, reconnaissance detection, incident response, digital forensics, security testing and APT detection. It requires students to connect technical monitoring strategies with practical security, legal and operational considerations within a complex organisational network environment.

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Machine Learning and Big Data 2,000 words

Machine Learning for Big Data

This assignment for the Machine Learning and Big Data module requires students to produce a 2,000-word individual report demonstrating their understanding and practical application of machine learning techniques to big data. The assessment is worth 15 credits and is structured around five interconnected areas: data in big data, machine learning architecture, model deployment, model evaluation, and the machine learning lifecycle. The first part focuses on identifying and evaluating suitable datasets for a selected big data topic and determining whether the datasets are appropriate for the intended machine learning application. Students are expected to examine the characteristics of their data and apply appropriate pre-processing approaches, including consideration of attribute selection and data preparation. The second part addresses machine learning modelling architecture. Students must develop an appropriate architecture for their big data application and may compare alternative architectural approaches. The report should explain the selected machine learning techniques and demonstrate how they operate as part of the proposed system. Practical considerations such as performance, scalability, fault tolerance, technology usage and reliability should also be considered. The third part requires students to implement and deploy the proposed data and machine learning model. This includes testing, visualising and evaluating the resulting outcomes. The fourth part requires critical evaluation of the dataset selection, modelling design, implementation and application, including assessment of whether the selected machine learning techniques are appropriate for the intended purpose. The final part focuses on the complete project lifecycle. Students are expected to critically reflect on the work undertaken, identify what they have learned, evaluate the development process and explain how the machine learning application could be improved in a future implementation. The assessment develops five learning outcomes covering big data sources and applications, machine learning techniques, practical application of machine learning tools, critical evaluation of techniques and tools, and the ability to follow a complete big data analysis lifecycle. The marking criteria allocate 20% to each of these five areas. The assignment is submitted as an individual written report. The brief states that Microsoft Word should be used rather than PDF and requires students to acknowledge sources and any AI tools used in accordance with the stated AI policy.

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Ethical Hacking 2,500 words

Ethical Hacking – Professional Penetration Testing Report

This resit coursework for the Ethical Hacking module at Coventry University requires students to conduct a professional penetration testing examination of a small office environment represented by a number of virtual machines. The purpose of the assessment is to evaluate the security of the target environment, identify vulnerabilities, demonstrate appropriate exploitation techniques within the authorised assessment environment, and produce professional recommendations for improving the security of the systems. The assignment carries 15 credits and requires a report of approximately 2,000 words, with a permitted variation of ±10%. The report should follow a structured penetration-testing approach. The first section covers reconnaissance and target analysis, requiring students to investigate the target environment and identify its structure, services and potential attack surfaces. The marking criteria emphasise the use of appropriate tools to identify network structure and services and the identification of vulnerabilities during the scanning process. Students are expected to analyse the results rather than simply reproduce the output of scanning tools. The second section focuses on exploitation. Students must describe in detail the steps taken and the tools used to exploit relevant vulnerabilities identified during the assessment. The marking criteria distinguish between compromising the desktop and gaining access to the server, with higher achievement involving multiple relevant vulnerabilities and successful access through more than one vulnerability. The report should provide appropriate screenshots and sample sessions to support the findings. The third section addresses post-exploitation activities. Students are required to document and analyse activities carried out after gaining access to the target systems. Examples identified in the marking criteria include dumping password hashes and creating a persistent backdoor. For server assessment, the criteria also consider activities such as obtaining root access or establishing a persistent connection. The report should explain the significance of the activities rather than merely listing technical actions. The fourth section provides recommendations for securing the target machines. Recommendations must address all vulnerabilities identified during the assessment, not only vulnerabilities that were successfully exploited. Security issues should be discussed using an established risk-rating approach such as OWASP, and proposed countermeasures should be relevant to the specific vulnerabilities discovered. The report should also analyse how vulnerabilities relate to one another and fit within the wider security context. The final section presents the conclusions, including an evaluation of the penetration-testing work and alternative approaches that could have been taken. The overall learning outcomes require students to critically discuss the legal, technical and ethical scope of ethical hacking, evaluate penetration-testing methodologies and security assessment tools, analyse vulnerabilities, and professionally report penetration-test outcomes with suitable countermeasures.

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Cryptography 2,500 words

Cryptography – Secure Land Transaction Contract Exchange Protocol

This 2,500-word Cryptography coursework for Coventry University examines the design of a secure communication protocol for the remote exchange and signing of legal property transaction contracts. The assignment is based on a scenario involving Hackit & Run LLP (H&R), a firm of solicitors specialising in property transactions in the UK and overseas. Because property transactions are increasingly conducted through remote communications, H&R intends to establish a comprehensive system for secure document handling, exchange and digital signing that complies with legal requirements and remains enforceable under UK law. The scenario concerns a land transaction between Mrs. Harvey, the buyer, and Mr L.M. Facey, the seller. Students must devise a communication protocol involving three parties: H&R, the seller’s solicitor and Mrs. Harvey. H&R communicates with the seller through the seller’s solicitor rather than directly with the seller. The seller’s solicitor sends the contract to H&R, H&R forwards it to Mrs. Harvey, Mrs. Harvey digitally signs the contract and returns it to H&R, and H&R then sends the signed contract to the seller’s solicitor. The assignment requires students to consider two communication scenarios between H&R and the seller’s solicitor: a situation where the two parties have previously communicated securely and a situation where they are communicating securely for the first time. Students must identify suitable encryption algorithms for the different stages of the contract exchange protocol and justify their algorithm choices. The work should demonstrate an understanding of appropriate cryptographic approaches for maintaining confidentiality, integrity and availability during secure communication. Students must clearly illustrate their proposed protocol using suitable graphics and pseudocode. A full functioning implementation using a programming language may be provided as a higher-level approach. The report must identify the strengths and limitations of the proposed protocol and discuss the findings. This requires students to connect cryptographic theory with a practical security protocol designed for a real-world legal transaction. The coursework assesses knowledge of modern cryptography, including symmetric-key cryptography, key exchange, asymmetric cryptography, digital signatures, digital certificates and authentication. Students are also expected to model, test and assess the suitability of cryptographic protocols and algorithms for different practical requirements and critically evaluate current research and technological developments in cryptography and its applications. The final submission is a written report of 2,500 words, excluding appendices and tables, with properly formatted references. The assignment is categorised as a report and is a normal coursework attempt. The brief does not specify a particular referencing style or academic level, so these fields should not be guessed when entering the assignment into the Reference Library.

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Entrepreneurship / Entrepreneurial Marketing 1,200 words

Reflective Analysis of Entrepreneurial Capabilities and Development: The GymBuddy Venture

This reflective Entrepreneurial Marketing assessment evaluates the development of the student's entrepreneurial capabilities through the GymBuddy business concept, a proposed fitness application designed to improve motivation and accountability among beginner gym users. The reflection examines how entrepreneurial learning, customer research and creativity frameworks changed the student's original approach from developing a highly complex product toward a more focused and testable business proposition. Entrepreneurial_Marketing_Refle… A central theme of the report is entrepreneurial self-assessment. Opportunity recognition is identified as an important strength, supported by interviews with beginner gym users and competitor analysis involving platforms such as Strava, MyFitnessPal and Nike Training Club. At the same time, the reflection identifies areas requiring development, particularly financial understanding, customer acquisition cost, lifetime value, conversion-rate analysis and negotiation capability. These strengths and weaknesses are used to establish priorities for future entrepreneurial development. Entrepreneurial_Marketing_Refle… The report also evaluates the evolution of the GymBuddy product using SCAMPER, Design Thinking and Lean Startup principles. Rather than pursuing a feature-heavy application, customer feedback leads to greater emphasis on simplicity, peer accountability, social competition and iterative validation. SCAMPER is applied to eliminate unnecessary features, substitute complex recommendations with simpler motivational mechanisms, combine useful functionality and adapt gamification to the needs of beginner users. Entrepreneurial_Marketing_Refle… Another major area of reflection concerns teams, networks and collaborative innovation. The student considers how group creativity exercises challenged the assumption that a venture should be developed independently and highlights the importance of mentors, developers, industry professionals and potential investors. The report links entrepreneurial networks with access to resources, opportunity identification, legitimacy and emotional support and proposes deliberate networking activities as part of future venture development. Entrepreneurial_Marketing_Refle… The visual development framework on page 3 summarises the entrepreneurial journey in five stages: GymBuddy concept and market research, capability assessment, product development and validation, teams and networks, and a time-based action plan. The reflection is structured using the Gibbs Reflective Cycle, linking experience with evaluation, analysis and future action. Entrepreneurial_Marketing_Refle… Overall, the assessment demonstrates reflective learning through the application of entrepreneurial theory to a practical start-up concept. It shows a shift from perfectionism toward rapid validation, customer-centred development, collaborative working and concrete action planning, while identifying financial management, negotiation and network development as important areas for continued improvement. Entrepreneurial_Marketing_Refle… Note: this file is a completed reflective submission rather than the original university assessment guideline, so the university, academic level and academic year should remain Not specified unless you also upload the official brief.

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Cryptography 2,500 words

Cryptography – Secure Contract Exchange Protocol

This 2,500-word Cryptography assignment for Coventry University examines the design of a secure communication protocol for the digital exchange and signing of property contracts. The scenario is based on Lauren Order & Cashgrab LLP (LO&C), a UK and overseas property law firm seeking to establish a comprehensive document handling, exchange and signing system that supports remote property transactions while remaining consistent with legal requirements and enforceable under UK law. The assignment requires students to consider the exchange of contracts using the extended CIA model and to devise a secure communication protocol involving three parties: LO&C, the buyer's solicitor Hackit & Run (H&R), and the seller. The scenario specifies that LO&C communicates with the buyer through H&R, that LO&C and the seller collaborate on initial contract drafts, and that LO&C prepares and sends the final contract to the seller for approval and digital signing before forwarding the signed contract to H&R for the buyer's signature. LO&C and H&R have an existing secure communication relationship. A central requirement is the identification and justification of suitable encryption algorithms for the different stages of the contract exchange protocol. Students may select algorithms covered in lectures or undertake additional research to identify alternative algorithms. The report must explain why particular algorithms are appropriate at different stages of the protocol and demonstrate how the selected cryptographic techniques address the practical security requirements of the scenario. The protocol must be clearly illustrated using suitable graphics and pseudocode, with functioning code being an optional higher-level approach. Students are required to identify the strengths and limitations of their proposed protocol and discuss their findings. The assignment therefore combines theoretical knowledge of modern cryptography with practical protocol design and evaluation. Generative AI may be used to create suitable code where permitted, but students must demonstrate their understanding of the code. The assessed learning outcomes cover modern cryptographic concepts and techniques, including symmetric-key cryptography, key exchange, asymmetric cryptography, digital signatures, digital certificates and authentication. Students are also expected to model, test and assess the suitability of cryptographic protocols and algorithms for practical requirements and critically evaluate current research and technological developments in cryptography and its applications.

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International Marketing and Brand Management 6,000 words

International Marketing and Brand Management – International Brand Expansion Project

This individual project report for the International Marketing and Brand Management module focuses on analysing a selected brand and developing a strategy for its expansion into a new international market. Students must select a brand that is currently available in a limited number of countries or within a particular region and that has a physical market offering. The completed case study must be between 4,000 and 6,000 words, with 6,000 words specified as the maximum. The first part of the assignment focuses on Brand Analysis. Students are required to introduce the selected brand and examine its brand portfolio and geographical availability. The analysis must also consider the brand’s elements and evaluate its brand equity using Keller’s Customer-Based Brand Equity (CBBE) model. Students must additionally identify and analyse an appropriate brand mantra. These elements are intended to provide a comprehensive understanding of the brand’s existing market position and identity. The second part examines potential international markets for the selected brand. Students must conduct SLEPT analyses of three culturally distinct markets that the brand could potentially enter. Each market must also be assessed through competitor analysis. This section requires students to consider the wider market environment and competitive conditions when evaluating potential international expansion opportunities. The third part develops the Strategy for Market Entry. Students must select and justify one new international market for the brand to enter and recommend an appropriate market entry strategy. The report must also provide recommendations regarding standardisation or adaptation using a marketing mix framework. In addition, students must identify the intended Points of Parity (POP) and Points of Differentiation (POD) that would support the brand’s positioning in the selected international market. The assignment requires students to integrate relevant academic models and theories into the brand case rather than simply describing them. Sources used to support models, theories, claims and arguments must be appropriately referenced using Harvard referencing, both through in-text citations and in the final reference list. Students are also encouraged to use appropriately referenced images and figures to illustrate their case study. Assessment weighting is divided equally across the three substantive sections, with Brand Analysis accounting for 30%, International Market Analyses for 30%, and Strategy for Market Entry for 30%. The remaining 10% assesses presentation, including the range and quality of sources, Harvard referencing, clarity of writing and presentation, and quality of discussion.

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Leadership and Change 3,000 words

Leadership and Change – Reflective Learning Portfolio

This 3,000-word coursework is a reflective learning portfolio focused on leadership, organisational change and reflective practice. The assessment is worth 100% of the module mark and is structured into four individual components. It is designed to assess leadership capabilities such as strategic thinking, emotional intelligence, communication, adaptability, team building, ethical decision-making and change management, while encouraging students to connect leadership theories and self-assessment tools with their own development. Component 1 focuses on individual leadership style and requires students to provide the results of the Fastest MBTI Test and critically reflect on what the outcome means in relation to their preferred leadership style. Students are expected to connect their MBTI results with leadership approaches covered in the module, including the Situated Leadership model. This component represents 20% of the assessment and has a suggested length of 600 words. Component 2 examines team roles. Students must present evidence from their Team Roles Test, identify their preferred team role and explain why it suits them. They must then identify two other Belbin team roles that would be important when building a team and explain their relevance. Connections can also be made between the Team Roles Test, Belbin’s framework and the results of the Conflict Management questionnaire. This component contributes 20% and has a suggested length of 600 words. Component 3 requires students to develop an individual organisational change plan in response to a fictional case study in which ARU is considering the adoption of AI to mark student coursework assignments and examinations. The plan evaluates change enablers and barriers, stakeholder involvement, ethical considerations and resource constraints. Students then develop an implementation strategy using a suitable change-management framework such as Lewin’s Change Model or Kotter’s 8-Step Model, including stakeholder engagement, communication, resistance management and strategies for sustaining change. This component represents 30% and has a suggested length of 900 words. Component 4 is a 900-word reflective account of what students learned about leadership while working as part of a team to design the organisational change plan. The reflection considers how leadership emerged, team collaboration, missing or required roles, conflict management and personal development. Students may structure the reflection using Kolb’s Reflective Learning Cycle or Gibbs’ Reflective Cycle and support their discussion with theories such as transformational, situational and distributed leadership, as well as Belbin’s team-role framework. Overall, the learning outcomes require students to critically reflect on their leadership style, evaluate classical and contemporary leadership theories, understand organisational change processes and develop evidence-based change strategies. The assessment also emphasises reflective practice and personal leadership development through experiential learning in a group setting.

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Principles of Management 1,000 words

Management Practices and Value Creation at Tesco

This 1,000-word formative assessment for the MSc Management programme at BPP University examines the management functions, theories and practices of Tesco. The assignment requires a business consultant perspective, with the report addressed to Tesco’s senior management team. It focuses on two learning outcomes: applying key management theories, concepts and practices to contemporary organisations, and evaluating the dynamics of the global business environment and the role of effective management as a value-adding activity at global, national and local levels. The case study provides background on Tesco, including its position in the UK grocery market, employment scale, store network and historical development. Tesco was founded by Jack Cohen in 1919, opened its first store in 1929, introduced self-service stores in 1948 and expanded rapidly through acquisitions and diversification. Its activities developed beyond grocery retailing into areas including non-food products, fuel retailing, financial services through Tesco Bank, and mobile and home entertainment. Task 1 focuses on key management theories, concepts and practices. The assignment requires discussion of how Tesco’s management practices contribute to organisational success and create value. Students must consider the four philosophies of the Competing Values Framework and identify one additional relevant management theory that supports Tesco’s management approach. Suggested theories include Scientific Management Theory, Bureaucratic Theory, Administrative Theory, Contingency Theory and Total Quality Management, although the brief states that the list is not exhaustive. Task 2 examines management as a value-adding universal activity. Students are required to evaluate the global business environment and discuss how Tesco’s management has adapted its strategies and methods at global, national and local levels. The brief suggests using the LoNG-PEST framework or Porter’s Five Forces, identifying one global factor that can create an opportunity and one that can pose a threat to Tesco, and explaining where management can add value to the organisation. The report structure includes a BPP University administration cover sheet, table of contents, optional list of abbreviations, an optional 100-word introduction, Task 1 of approximately 500 words, Task 2 of approximately 500 words, references and optional appendices. The main body is limited to 1,000 words, excluding the cover page, contents, abbreviations, references and appendices. Harvard referencing is required throughout the report.

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Emerging Technology and Cloud Computing 5,000 words

Emerging Technology and Cloud Computing – SafeCloud Project

This MSc Management coursework for BPP University’s Emerging Technology and Cloud Computing module is a 5,000-word formal business report based on the SafeCloud project at AlwaysUp Ltd., a manufacturing company specialising in power-electric equipment for buildings and critical installations. The assignment examines how emerging technologies, Big Data and cloud computing can support AlwaysUp Ltd.’s international expansion, real-time equipment monitoring, personalised preventive maintenance, data-driven decision-making, and secure management of equipment and client information. The company is seeking to expand from the UK into European markets through distributor partnerships and ultimately achieve global reach. The report requires the identification and critical evaluation of two emerging technologies that can improve AlwaysUp Ltd.’s support and equipment-care services. Students must evaluate the benefits and limitations of each technology within a manufacturing context and use real-world examples to support the analysis. The assignment specifically assesses the ability to demonstrate a comprehensive understanding and critical evaluation of emerging technologies in business. The second task focuses on designing and evaluating a cloud-based Big Data architecture that integrates the two selected emerging technologies. The proposed architecture must include an architecture diagram and should be evaluated in terms of scalability, security, cost-effectiveness, real-time data processing and decision-making. The solution should address AlwaysUp Ltd.’s business requirements and enable the collection, storage and analysis of equipment-related data. The third task examines data protection, ethical considerations, project risks and resource requirements associated with implementing the proposed solution. This includes consideration of data protection and ethical issues arising from the selected technologies, implementation risks, and the human, technological and other resources required. The fourth task analyses the strengths and weaknesses of the combined emerging technologies and Big Data architecture and considers their application to AlwaysUp Ltd.’s business strategy. The report must conclude with a proposed route forward based on the findings. The required report structure consists of an introduction of approximately 500 words, four main tasks of approximately 1,000 words each, a conclusion of approximately 500 words, Harvard-style references and optional appendices.

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Organisational Strategy and Sustainability 2,500 words

Strategic Evaluation and Analysis of Hindustan Unilever Limited (HUL)

This 2,500-word coursework for the MSc Management module Organisational Strategy and Sustainability requires a strategic evaluation and analysis of Hindustan Unilever Limited (HUL). The assignment is written from the perspective of a management consultant at BPP Consulting Group and is addressed to HUL’s Board of Directors. It focuses on HUL’s strategic management, sustainability initiatives and responsible leadership within a global business context. The report begins by examining HUL’s internal capabilities and external business environment. Students are required to apply one strategic model to analyse the internal environment and one model to assess the external environment, such as VRIO, PESTEL or Porter’s Five Forces. The analysis should identify internal competencies, external opportunities and threats, sources of competitive advantage, and the influence of global and local market dynamics on HUL’s strategic choices. Particular consideration may be given to key regions such as the European Union, Asia and North America. The second section evaluates HUL’s ethical, sustainable and responsible business practices. The brief requires the use of one framework, such as the Triple Bottom Line or Sustainable Supply Chain Management, to assess one selected area of CSR, sustainability or ethical practice. The discussion should consider stakeholder engagement, regulatory influences such as the United Nations Sustainable Development Goals and European Union legislation, and one HUL sustainability initiative such as product lifecycle management, eco-design, renewable energy integration or supply chain transparency. The third section critically evaluates HUL’s strategic responses to current and future challenges, including climate change, artificial intelligence and digital disruption. One strategic framework, such as the Ansoff Matrix, Blue Ocean Strategy or VRIO, should be applied to evaluate strategic options and their implications for long-term sustainability, competitiveness and market relevance. The final section provides a personal leadership and sustainability reflection. Students select one leadership model, such as Goleman’s Emotional Intelligence, Hofstede’s Cultural Dimensions or Transformational Leadership, and reflect on their learning from the previous sections. The reflection covers personal development, ethical, cross-cultural or sustainable leadership, career aspirations, and values-driven leadership. The report concludes by summarising recommended changes to HUL’s organisational strategy and sustainability initiatives. The assessment requires Harvard referencing and a professional academic structure. The main body has a 2,500-word limit, with suggested allocations of approximately 50 words for the introduction, 600 words each for LO1, LO2, LO3 and LO4, and 50 words for the conclusion. The cover page, contents, references and appendices are excluded from the word count.

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Consultancy Project 1,464 words

Enhancing Sustainable Supply Chain Practices through Digital Transformation: A Case Study of Unilever Plc

This consultancy project proposal examines how digital transformation can enhance sustainable supply chain practices at Unilever Plc. Unilever is presented as a multinational fast-moving consumer goods organisation operating across more than 190 markets, with a complex global supply chain and sustainability commitments linked to the United Nations Sustainable Development Goals. The proposal focuses on the challenges associated with achieving supply chain transparency, managing fragmented sustainability data, and balancing sustainable sourcing and operational costs. The project investigates how emerging digital technologies, particularly blockchain, the Internet of Things (IoT) and Artificial Intelligence (AI), could support greater transparency, traceability, operational efficiency and sustainability within Unilever's supply chain. The organisational analysis identifies issues including the complexity of tracing raw materials across multi-tier supply chains, fragmented procurement and sustainability information systems, and the tension between sustainability investments and short-term operational costs. The proposal also considers infrastructure readiness, data security and employee upskilling as factors affecting digital transformation. The research aims to examine how digital transformation technologies could improve sustainable supply chain planning at Unilever while supporting long-term business and environmental goals. Three objectives are established: examining sustainability challenges affecting Unilever's global supply chain, assessing how blockchain, IoT and AI could improve transparency, traceability and efficiency, and proposing strategic recommendations for integrating digital innovation into Unilever's sustainability framework. The central research question examines how digital transformation can contribute to an ethical, transparent and sustainable supply chain while maintaining operational efficiency. A qualitative exploratory case-study research design is proposed. The main emphasis is on secondary data, including Unilever's annual reports, sustainability information, digital transformation publications, company news, industry articles and analyst reports. Where feasible, primary data may be collected through semi-structured expert interviews and a short qualitative questionnaire. The proposed analysis includes thematic analysis, document review and descriptive statistics using relevant secondary numerical data. Validity is supported through triangulation, while reliability is addressed through consistent coding and an audit trail. The proposal also addresses ethical considerations, including research integrity, transparency, confidentiality, informed consent, anonymity, GDPR requirements, plagiarism avoidance, Harvard referencing and potential corporate bias or greenwashing. Overall, the project explores the potential contribution of digital innovation to Unilever's sustainability objectives and the wider responsible digitalisation of the FMCG supply chain.

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Consultancy Project 1,500 words

Consultancy Project Proposal

This assessment requires students to develop a professional Consultancy Project Proposal addressing a current organisational issue or business challenge. The assessment consists of a 1,500-word individual project proposal worth 80% of the module grade and an A4 Consultancy Project Proposal Digital Poster worth 20%. The proposal should provide an in-depth analysis of a selected business or management issue and establish a comprehensive research methodology for investigating the identified challenge. The assignment requires students to select an existing organisation and critically examine between one and three current and ongoing organisational challenges. The discussion should be evidence-based and supported by relevant, recent and credible sources, such as academic journals, company reports and other appropriate research. Students are encouraged to establish links between the selected challenges, current affairs, news events and the United Nations Sustainable Development Goals where relevant. The proposal must establish a clear research aim and three research objectives connected to the selected organisational challenges. Students must demonstrate awareness and application of appropriate research methodologies, including consideration of qualitative and quantitative approaches. The assessment also encourages the development of one or two concise research questions and requires students to demonstrate how the selected research methods can be applied to investigate the identified challenges. Students must also develop a feasible research plan and discuss ethical considerations, particularly those relating to secondary data collection. The digital poster should communicate the research plan clearly and demonstrate data analysis and data presentation skills through appropriate graphs, charts and tables. The final proposal should critically organise and synthesise information, provide a clear conclusion, and maintain professional academic standards. The recommended structure consists of a cover page, table of contents, list of abbreviations where appropriate, introduction, critical discussion of challenges, research aim and objectives, application of research methodologies, ethical considerations, research plan, critical organisation and synthesis of information, conclusion, references, the Consultancy Project Proposal Digital Poster and appendices where required. The main proposal has a 1,500-word limit, while the digital poster should not exceed one A4 page. The assignment requires Harvard referencing and the report should be written in the third person.

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Managing Customer Experience 1,000 words

Customer Experience Strategy Report

This formative assessment for the Managing Customer Experience module requires students to prepare a 1,000-word business report analysing the customer experience (CX) strategy of one selected company in one specific country where the company operates. The report is written from the perspective of a customer experience consultant and is intended for the Board of Directors of the chosen organisation. Students must apply relevant customer experience concepts, academic literature and practical examples to evaluate how the organisation creates value for its customers and supports business success. Students must select one company from the organisations provided in the assessment brief: H&M, Chen One, or M&S. The selected company and the specific country being analysed must be clearly identified in the report. The first part of the report requires an explanation of the concept of customer experience and an evaluation of its importance to the success of the chosen brand. This includes considering the role of CX, mutual benefits for the organisation and customers, the impact of CX on business and financial performance, and examples of how the brand has used customer experience to achieve competitive advantage. The second part focuses on how the selected brand delivers a seamless omnichannel customer experience. Students must discuss the organisation's strategy for creating a seamless customer journey, including the role of customer personas and customer journey maps. A detailed customer persona should be developed based on research and data analysis, covering the profile, goals, pain-points and motivations of the selected customer. Students must also create a customer journey map covering stages, needs, activities, feelings, pain-points and opportunities for improvement. The report should critically evaluate how effectively the chosen organisation creates a seamless customer journey and identify potential areas for improvement. The assessment is aligned with learning outcomes concerning the importance of customer experience to business success and the evaluation of seamless omnichannel customer journeys. The report should use an academic style, avoid first-person writing, and appropriately reference figures, diagrams and independent research using the Harvard Referencing System.

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Effective Marketing Management 470 words

Bright Network Academy – Learn New Skills

This assignment, titled “Bright Network Academy – Learn New Skills,” is part of the Effective Marketing Management module and focuses on developing contemporary skills that support marketing effectiveness and future career development. The assignment introduces Bright Network Academy as a free e-learning platform providing expert advice and learning opportunities designed to help students prepare for the world of work and strengthen their graduate applications. Students are required to select and complete a minimum of two Bright Network Academy courses, provided that each selected course is at least 35–40 minutes long. Alternatively, students may choose shorter courses of approximately 15–20 or 20–30 minutes, in which case four courses must be completed. After completing the selected courses, students are required to download and submit the corresponding certificates as evidence of completion. The main written component is a one-page A4 statement of approximately 500 words. In this statement, students must explain their choice of the selected short courses and justify their relevance to the Effective Marketing Management module as well as their broader relevance to future career development in marketing. The assignment therefore requires students to reflect on how the skills and knowledge gained from the selected courses can contribute to their professional development. The available Bright Network Academy courses cover areas such as transferable skills, leadership skills, commercial awareness, and entering the marketing profession. Students are expected to consider how their chosen courses relate to contemporary marketing skills and their future professional goals. The final submission consists of a single file beginning with the approximately 500-word statement on the first page, followed by the completed course certificates on subsequent pages. The grading criteria allocate 25% to the first certificate, 25% to the second certificate, and 50% to the quality of the written statement. The assignment therefore combines evidence of course completion with reflective writing about the relevance of the selected learning activities to marketing and career development.

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Network Systems and Administration 3,500 words

Network Systems and Administration – Linux System and Network Administration Portfolio

This assessment is an individual portfolio for the Network Systems and Administration unit. It consists of two quizzes and a report based on a case study involving the development, implementation, configuration, testing, maintenance, and evaluation of a network solution using an industry-standard network operating system. The report requires students to justify their design and implementation decisions, provide a detailed testing strategy, develop a maintenance and disaster recovery plan, and critically evaluate the completed solution. The case study concerns Piranha, a small organisation in which the business owner has been maintaining a server containing information relating to finance, management, production, and sales. The student is required to undertake the role of Network Systems Administrator and configure an appropriate Linux-based network environment. The practical work includes creating a new user account with root access, confirming root privileges, verifying access to company files, and configuring group-based access controls for finance, management, production, and sales directories. Students must then establish a client-server network by installing a Linux distribution on a separate virtual machine in VirtualBox. SSH connections are required to verify user access and the configured file permissions. The assessment also requires students to use Wireshark to observe network traffic during SSH sessions, report on packet types and encryption, identify potential security vulnerabilities, and recommend improvements such as SSH keys and unique passwords. A maintenance schedule and a brief disaster recovery or backup strategy must also be proposed. The final report must document the practical work with clear explanations and screenshots showing commands, user accounts, and results. The report should be between 1,500 and 3,500 words. The assessment also includes Linux Essentials and Networking Essentials final grades. The marking criteria cover system and network administration, the maintenance plan, reflection and report writing, screenshots, references, and Harvard referencing.

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Information Organization 3,000 words

Information Organization System Case Study

This assignment requires students to gain a practical understanding of an information organization system by selecting a system of their choice and systematically studying how it works, how it developed, and how it may evolve in the future. The selected system may include a library online catalog or library management system, database, search engine, or another appropriate information system. The assignment begins with identifying an information organization system and providing a link to the system or an information page about it. Students then research the history of the selected system and describe important milestones in its development. The research should address why the system was built, what existed before the system, who its intended users are, whether it was developed from scratch or based on existing technology, and which companies or individuals were involved in its creation. Students are also asked to consider whether the system is an appropriate fit for the organization. A major component of the case study is an examination of the fields and metadata within the system. Students identify the types of information objects entered into the system and examine at least four to six fields, such as author, title, publication year, patient name, date of visit, or reason for visit, depending on the selected system. The assignment also asks students to discuss whether the fields are appropriate for the system and its users and whether additional fields could be useful. The assignment further requires an examination of how the system can be evaluated and what its future may look like. Students consider current evaluation methods, user involvement, potential integration of emerging technologies such as artificial intelligence, possible replacement by more efficient systems, areas of concern, and the qualifications and experience required to manage the system. The final submission must be written as an essay describing the selected system. It should include a title page, an introduction, complete sentences and paragraphs, and a length of approximately 2,000–3,000 words. At least four APA-style references are required, with at least two references coming from assigned course readings.

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Multimodal Sentiment Intelligence Platform for Dynamic Market Insights

This assignment presents the development of a Multimodal Sentiment Intelligence Platform for Dynamic Market Insights. The project addresses the need for real-time market sentiment analysis by combining text and visual data through an AI-driven multimodal approach. Traditional sentiment analysis methods may have limitations when dealing with diverse data modalities, and this project aims to address this gap by integrating multiple artificial intelligence and deep learning techniques. The primary objective is to develop an AI-driven platform capable of performing real-time sentiment analysis and supporting market trend prediction. The proposed system also aims to generate business intelligence insights that can support applications in marketing, finance, and customer service. The project incorporates deep learning, large language models, and multimodal fusion techniques to improve sentiment understanding. For text-based sentiment analysis, the assignment identifies BERT and GPT-2 for sentiment classification. For visual sentiment analysis, YOLOv8 is used for object detection while DeepFace is incorporated for facial emotion recognition. Feature-level and decision-level fusion strategies are applied to combine information from different modalities and improve the overall sentiment analysis process. Retrieval-Augmented Generation (RAG) is also incorporated to provide context-aware sentiment insights. The proposed platform uses Amazon reviews and IMDb reviews as text datasets. For image or video-based multimodal data, the assignment references the CMU-MOSEI dataset and an Amazon-Reddit merged reviews dataset. Overall, the work focuses on combining natural language processing, computer vision, deep learning, large language models, multimodal fusion, and retrieval-augmented generation to create a platform capable of producing dynamic sentiment and market insights.

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Digital Marketing / Marketing Analytics 2,483 words

Digital Marketing Analytics (BS783) — GA4 Performance Analysis and 90-Day Campaign Strategy for the Google Merchandise Store

This Level 7 report positions the writer as a digital marketing consultant engaged by the Google Merchandise Store, a live e-commerce operation. All quantitative work is drawn from the public GA4 Analytics Demo Account, so the analysis rests on real platform data rather than invented figures, and the report is structured in two halves that move from diagnosis to prescription. Part A is research and analysis. It opens by defining and evaluating the role analytics currently plays in shaping the store's digital marketing strategy — what is being measured, what that measurement is used for, and where the strategic gaps sit — grounded in marketing performance-measurement literature rather than description of the interface. The tactical analysis then assesses how effectively the store's digital channels and social content drove traffic and conversions across two defined quarterly reporting periods. Performance reporting follows, comparing the two periods in detail to identify the strongest customer segments, acquisition channels and products, and — more importantly — offering a reasoned explanation for why performance differed between them, distinguishing seasonality and campaign activity from genuine structural change. Part B turns to campaign strategy for a niche lifestyle market segment. A customer persona is developed and supported by the behavioural evidence visible in the GA4 data rather than asserted demographics, and a full journey map traces that persona from awareness through consideration, conversion and retention, identifying the friction points at each stage. Strategic recommendations then propose specific enhancements to channels and content aimed at that audience, each justified against the performance evidence from Part A and against the competitive landscape the store operates in. The final section builds the measurement architecture for the proposed campaign over the following ninety days. It sets goals and performance management parameters, selects the tools and metrics that will track them, defines the reporting cadence and the decision thresholds that trigger optimisation, and places particular weight on measuring return on marketing investment — connecting spend to sales, leads and customer satisfaction rather than to vanity engagement figures. Attribution limitations and the practical constraints of GA4 measurement are acknowledged where they affect confidence in the numbers. Throughout, screenshots and exported GA4 reports evidence the claims made, academic and practitioner sources support the analytical framing, and Harvard referencing is applied consistently. The submission is a single file through Turnitin.

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Data Visualisation / Business Intelligence 2,500 words

Data Visualisation (BS666) — Business Analyst Client Report: Dashboard Development, Tool Evaluation and Accessibility in Power BI and Tableau

This Level 7 resit assessment takes the form of a single client-facing report written from the position of a qualified business analyst. Rather than assembling semester activities, it asks for one sustained piece of analytical writing that carries a business case from raw open data through to a defended set of visualisations and the decisions they support. The report opens by identifying a data source drawn from an approved open repository and setting out the business problem the client faces. Provenance, structure, granularity and known limitations of the dataset are examined honestly at this stage, since every downstream claim rests on them, and all data is referenced in full — including data appearing inside charts, as the brief specifically requires. The main body works through three connected strands of critical evaluation. The first traces the development sequence of the visualisations themselves: how the data was prepared and cleaned, why particular chart types were selected over the alternatives available, how layout and interactivity were arranged for the intended audience, and how the design changed across iterations once weaknesses became visible. At least three completed visualisations are then evaluated individually and critically — what each one reveals, where each falls short, and what a reader could reasonably conclude from it. The second strand compares Power BI and Tableau as working environments for this specific dataset rather than in the abstract. Data connectivity, transformation and calculation capability, visual flexibility, publishing and sharing, licensing and governance are all weighed against what the client actually needs, with the practical friction encountered during the build reported rather than smoothed over. The third strand addresses accessibility and cognitive processing. It examines how each platform handles colour contrast and colour-vision deficiency, text alternatives, keyboard navigation and screen reader support, and then moves into the perceptual side — pre-attentive attributes, data-ink economy, chart junk, working memory limits and how visual encoding choices either reduce or inflate the effort a reader must spend to extract meaning. The argument connects this directly to decision quality in organisations with diverse analytical literacy. Findings are reported at length and translated into concrete business implications, with a conclusion that states what the client should do and on what evidence. The submission follows the prescribed structure throughout: title page, executive summary, contents, introduction, business problem, main evaluative section, findings, conclusion, Harvard reference list and appendices, presented as a single file for Turnitin.

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Data Visualisation / Business Intelligence 2,500 words

Data Visualisation (BS664) — Portfolio: Tableau and Power BI Dashboard Development for Evidence-Based Decision-Making

This Level 7 portfolio accumulates work across a full semester of data visualisation study and is submitted as one continuous document combining reflective writing, tool evaluation and an applied dashboard project. The emphasis throughout falls on interpretative rigour — showing not just that a visualisation was built, but why each design decision serves the decision-maker who has to act on it. The first activity documents sustained critical participation across the module's weekly discussion topics. Five or more substantive responses are evidenced with dated screenshots, each paired with the corresponding workshop date, and each grounded in both academic literature and practitioner sources rather than opinion alone. Live URLs to the Power BI and Tableau dashboards built during the module are included, and the activity closes with a consolidated review of how understanding developed across the units. The second activity covers formal training completion in both platforms, with certificates and badges reproduced, and then turns to accessibility. It examines the visualisations produced during that training against accessibility principles — colour contrast and colour-blind safe palettes, text alternatives, chart type legibility, cognitive load — and argues why accessibility is not a compliance afterthought but a determinant of whether a dashboard actually informs decisions inside a global organisation with a diverse user base. The third activity is a structured comparison of Tableau and Power BI, conducted through the evaluative frameworks introduced in workshops rather than a feature checklist. Benefits and limitations are weighed across data connectivity, calculation capability, visual flexibility, licensing and cost, collaboration and governance, and organisational fit. The section ends with a reasoned recommendation of one platform for the applied work that follows, with the trade-offs of that choice acknowledged openly. The fourth and largest activity is the applied project. A dataset is selected from an approved open source and its provenance, quality and limitations discussed. The target business, its stakeholder groups and the varying analytical literacy of the intended audience are analysed, because these determine what the dashboard must show and how plainly. The report then walks through the full development sequence — data preparation and cleaning, chart selection and justification, layout and interactivity, iteration in response to identified weaknesses — with screenshots evidencing each stage. Findings are reported and translated into specific, evidence-based decisions the business could take on the strength of them. The submission follows the prescribed structure with title page, contents, introduction, business problem, main analysis, findings, Harvard reference list and appendices, presented as a single file.

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Data Management / Business Analytics 2,500 words

Data Design Management (BS514) — Data Strategy Consultancy: Relational Database Design, SQL Implementation and Pipeline Transformation

This Level 7 assessment places the writer in the role of a Data Strategy and Analytics Consultant appointed by an organisation operating in a realistic industry sector. The brief is entirely simulated, so the work sets out a defensible set of assumptions about the organisation's data environment before any design begins, and populates the resulting database with synthetic but realistic records. The deliverable is a slide deck carrying full explanatory notes, submitted as a single PDF, and weighted across three connected tasks. Task one establishes the business case. It describes how the chosen organisation currently collects, stores and uses data across customer interactions, sales transactions, operational processes and digital channels, and identifies where that fragmented picture costs the business in efficiency, resource use, retention and decision quality. A SWOT analysis benchmarks the organisation against a named real-world competitor in the same sector, drawing on publicly available market information rather than assertion. The section closes with a critical evaluation of modern relational database advancements — cloud-hosted SQL services, distributed architectures and data warehousing — assessed not in the abstract but against what each would actually change about this organisation's business model. Task two carries the heaviest weighting and is the technical core. Key business entities are identified from the scenario, a current-state data flow diagram traces how data moves from collection points through to storage and reporting with the existing ETL approach made explicit, and a future-state ER diagram is then built with full attributes, primary and foreign keys, relationships and cardinality. The design is normalised to third normal form with the decomposition reasoning shown. Implementation follows in SQL: tables created with appropriate integrity constraints, at least ten realistic sample records inserted per table, and five business questions answered through working queries — highest-performing product or campaign, average conversion by category, workload distribution across staff, accounts with overdue or pending items, and most effective service channel. Outputs accompany every script. A transformation demonstrating query optimisation is included with before-and-after samples so the improvement is evidenced rather than claimed. Task three steps back to the technology decision. Two widely used data processing platforms are compared in tabular form across integration, cleaning, transformation and automation capability, judged specifically against this organisation's constraints, with a reasoned justification for the tool finally selected. The transformed dataset is then used to answer two management-level questions — where investment should be prioritised and how retention might be improved from observed behavioural patterns — each interpreted briefly and tied back to a concrete recommendation. Slide structure follows the prescribed layout, SQL scripts sit in the notes section, and the complete script file is reproduced in the appendix.

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Supply Chain Management / Strategic Sourcing 1,800 words

Strategic Sourcing and Supply Chain Resilience Under Global Uncertainty

This postgraduate supply-chain assessment examines how organisations operating in the UK are adapting their strategic sourcing and supply-chain practices in response to sustained global uncertainty. The assignment is situated within an environment shaped by major disruptions including the Covid-19 pandemic, geopolitical conflicts, trade tensions, tariff volatility and Brexit, all of which have challenged the reliability, cost efficiency and resilience of globally distributed supply networks. Students select either a British organisation or a multinational enterprise with significant UK operations and critically analyse its documented supply-chain strategy. Potential areas of investigation include insourcing and outsourcing, offshoring, reshoring and nearshoring, single versus multiple sourcing, supplier selection and monitoring, supplier and customer relationships, inventory management, digitalisation, sustainable sourcing and supply-chain risk management. The assessment requires more than description. Students must provide a critical and theoretically informed evaluation of the organisation's decisions, identifying both opportunities and risks associated with its strategic response. At least one relevant theoretical framework or analytical tool must be applied to assess the company's sourcing or supply-chain decisions. The analysis must also include at least one company practice or decision from 2025 and conclude with a detailed recommendation explaining actionable steps, implementation challenges, expected outcomes and alignment with organisational objectives. Research must draw on a minimum of 10 credible sources, including at least five academic journal articles and five non-academic sources such as news articles, industry reports or corporate publications. At least two of the non-academic sources must have been published during 2025–2026. Students may present the work as either a business report or academic essay, with consistent in-text citation and referencing. Norwich Business School normally requires the Harvard referencing system. Overview word count: approximately 340 words. Note: the document header states PG Coursework 2025–26, but one later line gives a submission deadline of 15 May 2025, which appears inconsistent with the stated academic year. I would therefore use 2025/26 for the library record and avoid publishing the deadline unless it is verified.

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Artificial Intelligence

Developing an Intelligent Chatbot and Expert System for UK Train Services

This postgraduate Advanced Artificial Intelligence group project requires students to design, implement, evaluate and demonstrate an intelligent conversational system for a UK train operating company. The chatbot combines conversational AI, expert-system concepts, predictive modelling and knowledge-based reasoning to support both railway passengers and operational staff. The coursework is worth 70% of the module and is designed to develop practical experience in applying modern AI techniques to realistic service and operational problems. The first task requires the chatbot to interact with passengers, collect journey requirements such as origin, destination, date and travel time, and identify the cheapest available train ticket. Appropriate railway ticket data sources or APIs may be used, with the selected ticket presented together with access to the relevant booking service. The second task extends the system to improve customer service through train-delay prediction. The chatbot gathers information about a passenger's current train, location, delay and destination before passing these data to one or more predictive models. Students process historical railway-performance data, train and compare suitable machine-learning models, evaluate their accuracy and integrate an appropriate model into the chatbot. The third task introduces an expert system for railway contingencies. Students extract rules and operational knowledge from provided contingency and station-disruption documents and construct a knowledge base capable of advising railway staff during events such as partial or complete line blockages. The system should gather details such as event type, location, time and severity, then provide relevant operational guidance, diversion information, alternative services and passenger advice. The overall architecture may include a user interface, NLP/NLU component, knowledge base, reasoning engine, predictive model, database and optional knowledge-acquisition component. Particular emphasis is placed on context-aware dialogue, reliable reasoning, appropriate fallback responses and effective user experience. Assessment outputs include the working chatbot, source code, a live presentation and demonstration, a detailed group technical report, and an individual contribution report. Overview word count: approximately 375 words. AI-use note: pre-trained LLMs may be used as an engine within the system, but they must not be used to generate coursework code. Any use of an LLM within the solution must be clearly justified and explained in the group report.

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Data Mining / Data Science

Customer Service Escalation Risk Analytics Using Data Mining and Machine Learning

This advanced Data Mining assessment applies the Knowledge Discovery in Databases (KDD) process to a real-world customer-service analytics scenario. Acting as a Data Scientist, students analyse a historical Customer Service Escalation Risk dataset to help an organisation reduce serious and legal customer escalations by identifying early signs of dissatisfaction, service bottlenecks and operational risk. The findings are intended to support business decisions relating to staffing, employee training, customer-journey improvement and escalation prevention. The dataset contains information covering customer demographics, account characteristics, communication channels, issue categories, operational measures such as wait times, transfers and SLA breaches, behavioural indicators including sentiment and response delays, and commercial variables such as monthly fees and contract value. The target variable contains four escalation outcomes: No escalation, Minor escalation, Serious escalation and Legal escalation. The first stage requires data exploration, visualisation and summary, including examination of variable distributions, dataset structure, descriptive characteristics and potential data-quality issues. Students then perform appropriate data cleaning, transformation, feature engineering and preprocessing. Particular attention must be given to variables that could introduce prediction leakage because of their meaning, timing or reliability. The supervised-learning component requires development and tuning of predictive models using suitable techniques such as k-nearest neighbours, Decision Trees, Support Vector Machines, ensembles or neural networks. Models must be evaluated using appropriate multiclass metrics and compared systematically, with interpretation of influential features and model behaviour. The assessment also requires unsupervised learning. After removing the escalation target, students apply and compare clustering approaches such as K-Means and hierarchical clustering. Appropriate preprocessing, encoding, normalisation or dimensionality reduction may be used, with visualisations such as PCA, t-SNE or scatterplots used to explore cluster structure and its relationship with escalation behaviour. Overall, the project assesses the student's ability to independently design a coherent KDD workflow, justify analytical decisions, compare alternative modelling approaches and communicate actionable findings to both technical and executive audiences. Overview word count: approximately 350 words. AI-use note: the brief permits AI tools only to assist with small, specific code snippets. Any AI-generated code must be clearly acknowledged and cited, while the submitted coding, analysis, interpretation and decision-making must remain the student's own work.

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Digital Forensics / Cybersecurity 2,000 words

AI-Augmented Digital Forensics Workflow Audit: Feasibility and Risk Assessment of ForensiScan AI

This digital forensics assessment examines the feasibility, reliability and legal risks associated with introducing AI-assisted analysis into a conventional forensic investigation workflow. Students act as a Lead Forensic Consultant assessing a proposed black-box system called ForensiScan AI, which claims to automatically classify illicit images and identify suspicious intent within encrypted messaging applications using Large Language Models. The central objective is to determine whether the efficiency benefits of AI can be achieved without compromising evidential integrity, transparency or legal defensibility. The report maps the proposed AI system across the four stages of the NIST forensic process: Collection, Examination, Analysis and Reporting. For each phase, students identify the data entering and leaving the AI system and determine whether the technology should be used for preliminary triage or as part of final forensic analysis. A major component concerns verification and validation. Because AI systems may hallucinate or misclassify evidence, students must design a ground-truth protocol involving human verification, statistical sampling and known datasets. The assessment also investigates whether AI-generated results can be reproduced reliably when identical evidence is processed again. The report further addresses chain of custody and data integrity, particularly whether AI processing could alter timestamps, metadata or other forensic artefacts. Ethical and legal analysis covers potential model bias, language and contextual limitations, and whether AI-generated outputs could satisfy the requirements of the Daubert Test for expert evidence. Overall, the assessment combines digital-forensic architecture, AI governance, evidential integrity, model validation, legal admissibility and professional accountability. The grading criteria place particular emphasis on forensic soundness, protection against evidence alteration, critical analysis of AI limitations such as hallucination and non-determinism, and professional technical communication. Overview word count: approximately 340 words. AI-use note: the brief permits AI only for limited assistance such as brainstorming risks, structural feedback and grammar refinement. It explicitly prohibits full report generation, unverified forensic claims and using AI to substitute for the student's own final recommendation or verification protocol. Any AI use requires an appendix containing the tool, exact prompts and a human verification log.

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Business Strategy / Digital Business and Innovation 3,125 words

Safaricom Business Hub: Strategic B2B Digital Payment Platform for East African SMEs

This strategic business case proposes the Safaricom Business Hub, an integrated B2B digital-payment platform designed to support small and medium-sized enterprises across East Africa. The proposed solution responds to Safaricom’s need for digital diversification as traditional voice revenues mature, while leveraging the growth of M-Pesa and the wider transition toward digital financial services. The platform combines invoicing, bulk payments, cash-flow analytics and integrations with accounting systems such as Xero, QuickBooks and SAP. The report begins by analysing Safaricom’s current strategic position, including subscriber growth, revenue performance, declining voice services, M-Pesa transaction growth and market saturation. It then assesses the wider East African B2B digital-payment opportunity, considering market size, digital adoption, the SME population, regulatory developments, smartphone penetration and operational inefficiencies associated with manual invoicing and reconciliation. Competitive analysis evaluates Safaricom against established banking solutions, international fintech providers and specialist payment gateways. The strategic case identifies several potential advantages associated with Safaricom’s existing M-Pesa infrastructure, agent network, brand position and payment-services capabilities. The proposed Business Hub addresses fragmented SME financial processes through automated invoicing, bulk payment processing, real-time cash-flow analytics and integration with existing accounting platforms. The business plan also provides a four-phase implementation roadmap, covering MVP development, pilot testing, Kenyan market launch, regional expansion and ecosystem development. Financial projections consider customer growth, revenue, EBITDA, customer acquisition cost, lifetime value, payback and return on investment, alongside sensitivity scenarios for different levels of market adoption. Finally, the report evaluates competitive, regulatory and market-adoption risks and links the proposed investment to Safaricom’s broader strategic diversification objectives. Overall, the work integrates strategic analysis, fintech innovation, market opportunity assessment, implementation planning, financial modelling and risk evaluation to present a commercial case for expanding Safaricom’s digital financial-services ecosystem. Overview word count: approximately 340 words. One useful note for the public Reference Library: I would upload the clean original assignment/business plan rather than the AI-detection or similarity-report versions if you have it, because these two PDFs contain checker-report pages in addition to the coursework itself.

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Cyber Security / Cloud Management 2,500 words

Cyber Security and Cloud Defence Strategy for ShieldSafe Analytics

This Level 7 Cyber Security for Business and Cloud Management portfolio examines the security challenges faced by ShieldSafe Analytics Ltd., a multinational health analytics organisation specialising in AI-enabled diagnostics and telehealth. The organisation processes high volumes of sensitive patient information, including biometric and genomic data, across hybrid-cloud environments and IoT-enabled healthcare infrastructure. Following a suspected data-exfiltration incident involving anomalous traffic from a diagnostic platform connected to third-party cloud APIs, students are required to evaluate the organisation's information environment and develop appropriate cyber-security and cloud-defence strategies. The first task focuses on information environments and the weaponisation of information. Students identify critical elements of ShieldSafe's information environment, evaluate vulnerabilities associated with the data-exfiltration incident and examine how patient data or analytical systems could be manipulated by malicious actors. Relevant real-world healthcare cyber incidents should be used to support the analysis. The second task examines offensive and defensive Information Operations. Students analyse techniques used by nation-state actors and cybercriminal organisations, including healthcare ransomware incidents such as WannaCry, and compare offensive and defensive approaches. The analysis considers how ShieldSafe can balance these approaches while protecting sensitive data and preserving trust in AI-enabled diagnostic systems. The third task applies Information Operations within legal and ethical boundaries and requires development of a secure cloud migration strategy for ShieldSafe's legacy Electronic Health Record system. The supporting student guide specifically permits students to demonstrate an implementation using Amazon AWS, including IAM users and roles, VPC configuration, security groups, web servers, EC2 instances and AWS migration services. The final task requires a comprehensive cyber-defence strategy, including implementation of Zero Trust Architecture across cloud platforms and analysis of vulnerabilities affecting cyber-physical healthcare systems such as wearable medical devices and diagnostic equipment. Students must propose controls against both remote and local attacks. Overall, the portfolio integrates information operations, healthcare cybersecurity, hybrid-cloud protection, secure migration, Zero Trust, cyber-physical security and strategic cyber defence. The work is produced as a portfolio report using PebblePad and must use Harvard referencing throughout, with appropriate citation of academic sources, images, definitions and external arguments.

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Machine Learning and Deep Learning 2,000 words

Development and Evaluation of Deep Learning Models for Healthcare Classification

This individual technical assessment focuses on the design, development, analysis and evaluation of a deep learning solution for a healthcare-related classification problem. Students select one of two provided scenarios: Polycystic Ovary Syndrome (PCOS) detection using ultrasound images or heartbeat classification using electrocardiogram (ECG) signals. The objective is to develop an appropriate deep learning approach and demonstrate critical understanding of the complete machine learning workflow, from initial data exploration through to model evaluation and reflection. Students may either design and train a deep learning model from scratch or customise and fine-tune an existing pre-trained architecture. The complete work is presented through a single Jupyter Notebook integrating Python code, technical discussion, results and visualisations. The notebook must clearly define the selected healthcare problem, explain its significance, justify methodological and architectural choices, and critically evaluate the resulting solution. The first stage involves exploratory data analysis and preprocessing, including investigation of class distributions, data imbalance and relevant patterns. Students prepare the data through techniques such as normalisation, augmentation, train-validation-test splitting and appropriate handling of class imbalance. This is followed by model design, training, validation and hyperparameter tuning, with the architecture selected according to the characteristics of the data and classification task. Model performance must then be evaluated using appropriate classification measures, including precision, recall, F1-score, ROC curves and area under the curve (AUC). The developed model should also be compared against suitable benchmark approaches, which may include traditional machine learning algorithms or alternative deep learning architectures. This comparison should identify the relative strengths and limitations of the proposed solution. The final component requires clear visual presentation and critical reflection on the complete modelling process, including limitations, challenges and opportunities for improvement. Importantly, grading prioritises methodological rigour, analytical depth and critical evaluation rather than simply achieving the highest predictive accuracy. Overview word count: approximately 330 words. AI restriction: this brief only permits automated AI tools for spelling and grammar checking. It explicitly prohibits tools such as ChatGPT, Gemini or Copilot from authoring assessment text or code; any permitted AI use must also be acknowledged.

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Machine Learning / Artificial Intelligence and Data Science 1,000 words

End-to-End Machine Learning Model Development, Tuning and Evaluation

This Level 7 Machine Learning and Intelligent Agents assessment requires students to develop and document an end-to-end machine-learning solution, covering the complete workflow from data preparation through model training, tuning, testing and evaluation. Students select an appropriate dataset or scenario, formulate a research question and determine whether the problem is most appropriately addressed through supervised learning, unsupervised learning or reinforcement learning. Suitable machine-learning techniques must then be implemented to create a model that can be systematically trained and tested. The assignment requires students to follow a structured machine-learning development process and document the complete development journey. The report should explain the selected scenario, data collection or dataset, Exploratory Data Analysis (EDA), rationale for selecting particular machine-learning methods, model training, fine-tuning and evaluation. Model performance must be assessed using appropriate established metrics, with relevant published research used to justify methodological decisions and support the interpretation of results. The technical implementation should demonstrate the ability to identify the performance of machine-learning algorithms, implement machine-learning approaches using one or more object-oriented programming languages, and determine which algorithms are most appropriate for a particular analytical brief. These requirements directly correspond to the module learning outcomes relating to machine-learning performance, implementation and algorithm selection. Students are advised to document their work within a Jupyter Notebook, combining Markdown explanations with executable code. The notebook may be submitted directly or converted to PDF. Alternatively, students may prepare the 1,000-word report in Microsoft Word, provided that the Python code is included within the submitted document. Assessment is divided into three principal areas: Introduction (20 marks), Machine Learning Process (40 marks), and Evaluation of Model Performance (40 marks). Higher-level work is expected to demonstrate strong understanding of machine-learning concepts, a functioning and thoroughly tested implementation, appropriate selection of algorithms and critical evaluation of the developed solution. Overall, the assessment integrates research-question formulation, data exploration, algorithm selection, programming, model optimisation and evidence-based evaluation within a reproducible machine-learning workflow. All academic sources must be presented using Harvard referencing.

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Strategy and Innovation

Strategic Analysis and Innovation Strategy for a UK Organisation

This postgraduate Strategy and Innovation assessment requires students to conduct a detailed strategic analysis of an allocated organisation and present the findings through a series of professionally designed information dashboards. The purpose of the analysis is to establish the organisation's current strategic position and use the resulting evidence to recommend an appropriate and justifiable innovation strategy. The work is expected to demonstrate master's-level evaluation, interpretation and synthesis rather than descriptive or checklist-based analysis. The first major analytical area concerns the organisation's external environment. Students evaluate the broader business environment, analyse the firm's industry and undertake competitor analysis, including consideration of the organisation's strategic group. Analysis should concentrate on the most consequential environmental factors and integrate general and industry-level evidence rather than simply listing factors. Competitor evaluation can examine issues such as products and services, market commonality, positioning, distinctive value propositions, first-mover advantage and breakthrough innovation. The internal analysis examines the firm's resources, capabilities and sources of competitive advantage. Appropriate analytical techniques should be used to assess competitive resources and key capabilities, with the internal value chain helping to identify factors capable of supporting competitive advantage. Students must also evaluate the organisation's business-level strategy and consider whether its resources and capabilities support its competitive positioning. Corporate-level analysis considers the firm's degree of diversification, internationalisation strategy, growth through acquisitions, mergers or organic development, and any cooperative strategies. These elements should be evaluated against the organisation's capabilities, resources and competitive context. The analysis concludes by interpreting the evidence to establish the firm's overall strategic position and recommending an innovation strategy or approach to innovation. Presentation quality is also important: the report should use an effective combination of text, charts, diagrams and other graphical forms, with professional formatting and accurate referencing. Overview word count: approximately 330 words.

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Machine Learning / Data Mining / Text Mining

Machine Learning Analysis of Classification Models and Text Mining on Furniture Review Data

This technical machine-learning report demonstrates the practical application of predictive modelling and text mining using WEKA. The work is divided into two major tasks. The first evaluates and compares Support Vector Machine and Decision Tree classification models, while the second applies text-mining techniques to furniture-review data and compares multiple classifiers after preprocessing, feature selection and class balancing. The first task uses the Screenshots.arff dataset to investigate the performance of libSVM and J48 Decision Tree classifiers. A 70% training and 30% testing split is applied, and the models are manually tuned to examine how different parameter settings affect predictive performance. For libSVM, an RBF kernel is used while different gamma and cost values are tested through grid-search-style experimentation. The report identifies gamma 0.03 and cost 2 as the strongest tested combination, producing approximately 91.67% accuracy on the test split. The J48 model is also optimised by adjusting the confidence factor used for pruning. Several confidence-factor values are examined, with 0.09 producing the strongest reported result of 80% accuracy. The optimised SVM and J48 models are then compared using five-fold cross-validation, where libSVM achieves 89.75% accuracy compared with 81.25% for J48. The second task focuses on text mining of Furniture Reviews. Text preprocessing includes TF-IDF term weighting, stopword removal, stemming, conversion to lowercase and word-count generation. The resulting textual dataset is transformed into a numerical feature representation suitable for machine-learning classification. Dimensionality reduction is performed using InfoGainAttributeEval with Ranker, selecting the 900 most informative attributes. The dataset is then balanced using WEKA techniques including Resample and SpreadSubsample to reduce class bias before classification. Finally, three classifiers—Naive Bayes, libSVM and J48—are evaluated on the balanced text dataset. The reported accuracies are 90.52% for Naive Bayes, 58.62% for libSVM and 78.45% for J48. The analysis concludes that Naive Bayes performs strongest for the processed furniture-review dataset, while the wider exercise demonstrates the importance of preprocessing, parameter tuning, feature selection, class balancing and appropriate model evaluation in producing reliable classification results. Important: this upload appears to be the completed student report, not the actual assessment guideline. Because the document does not state the university, module name, academic level, academic year, required word count or prescribed referencing style, I would leave those fields as Not specified rather than guessing.

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Big Data Analytics / Data Analytics 4,000 words

Big Data Analytics Using Python and Business Intelligence with Tableau

This individual Big Data Analytics assessment requires students to critically analyse data using programming languages, statistical techniques, data visualisation methods and business intelligence software. The assessment combines practical data analytics using Python with interactive business intelligence and dashboard development using Tableau, requiring evidence of technical implementation, research, critical appraisal and justification of the selected analytical approaches. The first section, worth 70%, is based on a dataset containing accidental drug-related deaths recorded in Connecticut between 2012 and 2024. The dataset contains 12,964 observations and 49 variables. Students are required to conduct exploratory data analysis using Python, develop three to four research questions, formulate null and alternative hypotheses, and apply appropriate statistical methods. The analytical process also requires an evaluation of alternative technologies and methodological approaches, supported by relevant research. Students must justify their selected methodology and present a clear workflow diagram. The solution-development stage involves data preprocessing, descriptive statistical analysis, visualisation, answering the research questions and conducting statistical significance testing to determine whether the null hypothesis should be accepted or rejected. Evidence of coding and a link to working code are also required. The final Python component requires evaluation of the findings, consideration of limitations and recommendations for future development using emerging technologies. The second section, worth 30%, focuses on Business Intelligence using Tableau and uses a historical Olympic Games dataset containing 271,116 rows and 15 columns. Students analyse relationships between medals and host cities, athlete age and medal type, season and medal counts, and sex and medal type. The section culminates in an interactive Tableau dashboard containing at least four interconnected sheets, where changes to relevant parameters are reflected across the dashboard. Overall, the assessment develops practical competence in Python-based analytics, statistical reasoning, research-question development, hypothesis testing, data visualisation and interactive business intelligence dashboard design. Overview word count: approximately 350 words. AI note: the brief permits AI for limited support such as grammar, structure, organisation of ideas and suggestions, but states that the main content, analysis and conclusions must remain the student's own work. AI use must also be declared.

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Business Management / Strategic Management 50,000 words

Starbucks Labour Relations and Unionisation: Strategic Business Consultancy Analysis

his Masters-level Business Project presents a strategic consultancy analysis of Starbucks, focusing on the company's ongoing labour-relations challenges, unionisation disputes and associated legal, operational and reputational implications. The project examines how employee relations and organised labour can develop from a human-resource concern into a wider strategic management issue affecting corporate governance, organisational performance, stakeholder trust and long-term brand value. The report follows the consultancy structure required by the Business Project assessment, beginning with identification of a current organisational challenge and the resulting issues affecting the client organisation. The official brief requires the selected challenge to be connected with current affairs, emerging industry trends and the organisation's wider business strategy. It also requires evaluation of both internal and external stakeholders and analysis using conceptual and numerical secondary data. In the completed project, Starbucks' labour dispute and unionisation environment are examined through several strategic-management frameworks. PESTLE analysis is used to assess political, economic, social, technological, legal and environmental influences surrounding labour relations. SWOT analysis evaluates Starbucks' organisational strengths, weaknesses, opportunities and threats, while Porter's Generic Strategies considers the relationship between differentiation, competitive positioning and labour-management practices. The project also applies stakeholder analysis to groups including senior leadership, employees, unions, regulators, customers, investors and other external stakeholders. A major part of the analysis considers stakeholder power, interest, legitimacy and urgency. The assessment brief specifically requires students to identify internal and external stakeholders, assess their power and interests, and evaluate how the investigated business challenge affects each stakeholder group. The project concludes with evidence-based strategic recommendations designed to improve labour relations, governance and organisational resilience. Recommendations include strengthening good-faith bargaining, developing more consistent labour-governance practices, improving workforce systems and employee participation, incorporating labour-relations indicators into ESG and corporate reporting, and adopting a coordinated legal and reputation-management strategy. Overall, the work demonstrates the application of strategic management, stakeholder analysis, corporate governance, HRM and secondary-data evaluation to a contemporary organisational challenge. The official assessment requires recommendations to arise directly from the critical analysis and to explain how they create strategic value for the client organisation and its wider industry. The completed submission itself is titled Business Project and focuses on Starbucks; its contents include the organisational challenge, purpose of the report, stakeholder impact, secondary-data evaluation, and recommendations and conclusion.

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Machine Learning / Data Science

Linear Regression and Stability of the Moore–Penrose Pseudoinverse Using Python

This machine learning practical and assessment activity develops an understanding of linear regression, Ordinary Least Squares and the Moore–Penrose pseudoinverse using Python. The work progresses from generating synthetic regression datasets to implementing regression algorithms manually, applying established machine learning libraries, analysing real datasets and evaluating the stability of estimated regression coefficients. The laboratory component begins with the generation of synthetic linear regression data using NumPy, including explanatory variables, random noise and an outcome variable. Students then implement simple linear regression without relying on machine learning libraries, using the least-squares solution to estimate the intercept and slope. The resulting observations and fitted regression line are visualised using Matplotlib. The work is subsequently extended to multiple linear regression, where several independent variables are used and coefficients are first calculated manually before the same problem is solved using Scikit-learn. The laboratory also introduces application of regression to the Scikit-learn Diabetes dataset, including feature and target standardisation, model fitting, prediction, correlation analysis and interpretation of regression coefficients. It also highlights the importance of residual analysis when assessing whether a linear model is appropriate. The associated weekly challenge focuses on the stability of linear regression solutions estimated using the Moore–Penrose pseudoinverse. Using a house-price dataset containing variables such as property size, number of bedrooms, distance from the city centre and property age, students construct the design matrix, standardise features and the response variable, and calculate regression coefficients using the pseudoinverse. Students then investigate model robustness by repeatedly fitting the regression model to random subsamples of different sizes and analysing the mean and standard deviation of each coefficient. Tables, boxplots or error-bar visualisations can be used to compare coefficient variability. The final discussion considers which variables are most influential, which coefficients are most stable, how sample size affects stability and whether coefficient interpretation remains reliable across different samples. The final work is submitted as a single PDF exported from Jupyter Notebook or Google Colab, combining documented Python code, experimental results, plots and written interpretation in a professionally organised notebook. Overview word count: approximately 360 wor

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International Human Resource Management / International Business 801 words

A Comparative Analysis of Cultural Factors Shaping Human Resource Staffing Strategies in International Enterprises

This International Human Resource Management assessment examines how cultural, institutional and employment-related factors influence recruitment and international staffing decisions when an organisation expands into a new national market. Students take the role of an HR representative for VetDopomoga, a Ukraine-based international veterinary clinic chain considering expansion into either the United Kingdom or India. The task requires a comparative analysis of Ukraine and the selected host country, followed by evidence-based recommendations for adapting the organisation's recruitment strategy. The first element of the assessment evaluates the cultural and institutional environment of the selected country. Students consider factors such as communication style, hierarchy and power distance, individualism versus collectivism, time orientation, labour-market conditions, professional expectations and the regulatory or employment environment. These factors must be compared with the corresponding context in Ukraine to identify implications for international HR practice. Students must then apply an appropriate international staffing strategy, selecting from ethnocentric, polycentric, regiocentric or geocentric approaches. The selected strategy should be justified in relation to VetDopomoga's proposed international expansion and recruitment requirements. The poster must also apply one cultural framework. Students may use either the Cultural Intelligence Framework developed by Earley and Ang or Erin Meyer's Culture Map. Two relevant dimensions from the selected framework should be analysed to explain potential benefits and challenges for recruitment across Ukraine and the selected host country. Practical application is an important component of the assessment. Students research recruitment best practices used by international veterinary organisations operating in the UK or India and assess which practices VetDopomoga could adopt, modify or avoid. The poster concludes with three evidence-based reasons supporting expansion into the selected country. The final poster should be concise, visually structured and supported by diagrams, charts, icons or other relevant visuals. At least seven academic or professional sources are required, including the core International Human Resource Management textbook, with Harvard-style in-text citations and a full reference list. One important guideline: the assessment brief indicates Category 2 AI use — proofreading only, meaning AI-generated assessment content is not permitted under that category.

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Business and Management 34,000 words

Examining the Role of Cross-Cultural Communication in Enhancing Team Efficiency: Evidence from McDonald’s Multicultural Workforce in London

This dissertation examines the role of cross-cultural communication in enhancing team efficiency within McDonald’s multicultural workforce, with the study framed around the challenges and opportunities created by culturally diverse working environments. The research considers how differences in language, communication styles, cultural expectations and workplace behaviour can influence employee collaboration, productivity and organisational effectiveness. The study aims to assess cross-cultural communication within multicultural teams, analyse its relationship with team success, and identify cultural factors that can support stronger workplace collaboration. The research adopts a qualitative secondary-research methodology under an interpretivist and inductive approach. Evidence is drawn from peer-reviewed academic literature, McDonald’s annual and diversity reports, and relevant hospitality-sector studies. The collected evidence is examined through thematic analysis to identify recurring patterns relating to communication barriers, workforce productivity, multicultural collaboration and inclusive leadership. The analysis is organised around four principal themes: cross-cultural diversity and workforce productivity; communication barriers and cultural challenges; diversity management and inclusive leadership; and HR practices, employee motivation and organisational performance. The discussion considers issues such as language barriers, cultural adjustment, misunderstandings, workplace conflict, leadership representation, employee inclusion, recruitment, training, digital HR systems and the use of AI-enabled workforce technologies. The study finds that workforce diversity alone does not automatically generate higher productivity. Instead, the effectiveness of multicultural teams depends substantially on the communication structures, inclusive leadership practices and HR support systems used by the organisation. Effective cross-cultural communication can strengthen coordination, customer responsiveness, employee engagement and operational efficiency, while poorly managed communication differences can contribute to delays, misunderstanding, stress and conflict. The report concludes with recommendations for continued cross-cultural communication training, inclusive leadership development, conflict-resolution initiatives and multilingual communication support. It also recognises the limitation of relying on secondary evidence and identifies primary research with employees and managers as a potential direction for future research. Overview word count: approximately 350 words.

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Computer Science / Algorithms and Optimisation

Genetic Algorithms for the Balanced Spanning Tree Problem

This technical research work investigates the Balanced Spanning Tree Problem, an optimisation problem that seeks to construct a spanning tree capable of balancing two competing network objectives: the low overall cost associated with a Minimum Spanning Tree and the short source-to-destination distances provided by a Shortest Path Tree. For an undirected, weighted and connected graph with a designated root vertex, a balanced spanning tree is defined using two parameters, α and β. The first limits the distance between the root and each vertex relative to the corresponding shortest path in the original graph, while the second limits the total tree weight relative to the Minimum Spanning Tree. Finding an optimal balanced spanning tree is computationally challenging because determining whether a graph contains an (α, β)-balanced spanning tree is an NP-complete problem. The research therefore proposes genetic algorithms as heuristic optimisation techniques for two variants of the problem: minimising β while α is fixed, and minimising α while β is fixed. The proposed genetic algorithm represents individual spanning trees as chromosomes composed of graph edges. An initial population of valid spanning trees is generated before evolutionary operations are repeatedly applied. The approach incorporates chromosome selection, crossover, mutation, fitness evaluation and stopping criteria. Four selection strategies are examined: Random Selection, Roulette Wheel Selection, Stochastic Universal Sampling and Tournament Selection. The fitness function is based on the relationship between the Minimum Spanning Tree weight and the total weight of the candidate chromosome. Experimental evaluation is performed using randomly generated weighted graphs containing 6, 10, 15 and 20 vertices. The experiments investigate different values of the balancing parameters, selection mechanisms and population sizes. The implementation uses a population size of 30, a maximum of 300 generations, crossover probability of 0.9 and mutation probability of 0.01 in the principal experiments. The reported results show that the genetic approach can generate high-quality balanced spanning trees and, for the tested instances, produced solutions matching the corresponding optimal balanced spanning trees. The study also examines how balancing parameters and population size influence execution time and convergence, demonstrating the practical use of evolutionary computation for complex graph-optimisation problems.

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Machine Learning / Artificial Intelligence and Data Science 1,011 words

End-to-End Machine Learning Model Development, Testing and Evaluation

This Level 7 Machine Learning and Intelligent Agents assignment requires students to develop and document an end-to-end machine-learning solution, covering the complete process from data preparation through model training, tuning, testing and evaluation. Students independently select a suitable dataset or scenario, formulate an appropriate research question and determine whether the problem should be addressed using supervised learning, unsupervised learning or reinforcement learning. Appropriate machine-learning algorithms must then be implemented to create a model capable of being trained and objectively tested. The assessment encourages the use of a structured machine-learning development methodology. Students are expected to explain the selected scenario and data source, perform suitable data preparation and Exploratory Data Analysis (EDA), and provide a reasoned justification for the machine-learning methods selected. The development process should demonstrate how the chosen algorithms are trained and fine-tuned before their performance is evaluated using established and relevant metrics. Published academic research should be incorporated to justify methodological choices and support the interpretation of results. The technical work is normally documented within a Jupyter Notebook, combining Markdown explanations with executable code cells. Alternatively, the report may be produced in Microsoft Word provided that the Python implementation is included. The assignment therefore assesses both conceptual understanding and practical programming competence. Students must demonstrate an ability to identify the performance of machine-learning algorithms, implement machine-learning techniques using an object-oriented programming language, and evaluate which algorithms are appropriate for a particular analytical brief. Assessment places particular emphasis on three areas: the Introduction, the Machine Learning Process, and the Evaluation of Model Performance. The marking criteria reward strong understanding of machine-learning concepts, a functioning and thoroughly tested implementation, appropriate selection of algorithms, and critical evaluation of the final solution. At the highest achievement level, implementations are expected to work without exception, satisfy the required functionality, demonstrate comprehensive testing and extend beyond the basic requirements. Overall, the assignment combines research-question formulation, data analysis, algorithm selection, machine-learning implementation, model optimisation and evidence-based evaluation within a reproducible technical workflow. All academic sources and supporting material must be presented using Harvard referencing.

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Management / Digital Transformation and Leadership 1,500 words

Leading Through Digital Change: Digital Transformation Report and Future Technology Poster

This Masters-level assessment for the Leading Through Digital Change module examines how organisations can respond strategically and effectively to rapid technological and digital transformation. Students take the role of a Digital Transformation Manager for one selected international organisation and prepare a professional Digital Transformation Report accompanied by an A4 digital poster. The purpose is to evaluate the organisation's current digital context and recommend changes that can strengthen competitive advantage and create sustainable business value. The first component requires critical evaluation and recommendation of one appropriate digital transformation strategic framework. Students may apply frameworks such as the McKinsey 4Ds, BCG Three Stages, Gartner's Six Steps or Cognizant's Four Pillars. The analysis should establish clear digital transformation objectives relevant to organisational functions such as operations, ICT and marketing, while using organisational evidence, academic research and practical examples to justify the proposed strategic direction. The second component is an academic poster evaluating two disruptive technologies or techniques expected to affect the chosen organisation, its industry, employment and the labour market over the next five years. Potential technologies include Artificial Intelligence and Machine Learning, 5G connectivity, the Internet of Things, robotics, drone delivery, blockchain, augmented reality and virtual reality. The poster should combine academic literature with real-world examples to demonstrate the likely opportunities, challenges and wider organisational implications of technological disruption. The final component focuses on digital leadership. Students analyse and recommend two suitable leadership approaches for managing and supporting digital transformation. Relevant approaches may include agile leadership, ethical-tech leadership, people-oriented leadership, hyperaware agile leadership and Goleman's leadership styles. Overall, the assessment integrates digital strategy, innovation, emerging technologies and leadership. The wider module also covers digital transformation strategies, data-driven decision-making, leadership in the digital age, artificial intelligence in contemporary business, digital risk management and planning for the future. Reference style: Harvard. Main report word limit: 1,500 words. Poster: A4 size with no specified word count.

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Management / Business Management 5,000 words

Principles of Management: AstraZeneca Management Analysis and Skills Development Report

This Masters-level Principles of Management assignment examines the management practices, organisational environment and managerial functions of AstraZeneca plc. The assessment is structured as a 5,000-word management report in which the student acts as a business consultant and critically evaluates how management theories, concepts and practices contribute to organisational performance and value creation. The work requires the application of relevant academic literature alongside case-study evidence and independent research on AstraZeneca. The first part of the assessment considers key management theories and practices. Students are expected to examine how management contributes to AstraZeneca's success, apply an appropriate dimension of the Competing Values Framework, and select an additional relevant management theory. Suggested theoretical perspectives include Scientific Management, Bureaucratic Theory, Administrative Theory, Contingency Theory and Total Quality Management, although students are encouraged to select theories most appropriate to the organisation rather than applying every available model. The assignment also evaluates AstraZeneca within the global business environment. Frameworks such as LoNG-PEST or Porter's Five Forces may be used to analyse global, national and local influences, including significant opportunities and threats. Students then critically analyse an internal organisational challenge and consider how the core management functions of planning, organising, leading and controlling can be applied to address it. Supporting analytical approaches may include Value Chain analysis, VRIO and stakeholder analysis. A further component focuses on personal and professional development. Students complete a Personal SWOT analysis and construct a Skills Development Plan linked to future career aims. This is followed by a 500-word reflective statement examining personal management competencies, including self-management, problem-solving and decision-making. An appropriate reflective framework, such as Borton, Kolb or GROW, is applied to structure the reflection. Overall, the assessment integrates management theory, organisational analysis, global business considerations, managerial decision-making and reflective professional development. It is designed to demonstrate Level 7 critical analysis and the practical application of management concepts to a contemporary multinational organisation. The wider module covers management theories, stakeholder management, global management, planning and decision-making, human resource management, leadership, operations and finance for managers

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Human Resource Management / People Practice 5,500 words

Essentials of People Practice: Recruitment, Employment Relations, Performance, Reward and Learning

This comprehensive CIPD Level 3 assessment examines the principal operational areas of people practice across the employee lifecycle. The unit covers recruitment and selection, employment legislation and employee relations, wellbeing and inclusion, performance management, reward, learning and development, and the practical application of people-profession knowledge. The assessment uses the fictional insurer Jemijo, which employs more than 2,500 people across office, home and hybrid working arrangements. Much of its workforce operates within customer-service and insurance call-centre environments, including a 24/7 emergency claims service. The first task addresses recruitment and selection. Learners examine the employee lifecycle, job analysis, job descriptions, person specifications, recruitment through corporate websites and commercial job boards, structured interviews, assessment centres and appropriate recruitment records. It also includes the use and critical review of AI-generated appointment and non-appointment letters. Written responses for the main questions total approximately 1,500 words. A separate practical task requires learners to devise selection criteria, shortlist candidates and conduct a recorded one-to-one simulated interview for a People Assistant vacancy. Further written tasks examine employment law, working time, employee wellbeing, discrimination, diversity and inclusion, and fair dismissal. This section is approximately 1,250 words. Performance and reward topics then cover objective setting, motivation, continuous performance reviews, total reward, non-financial reward and equitable pay, with approximately 1,500 words allocated. The final component addresses learning and development, including induction and training benefits, learning needs, face-to-face and blended learning, coaching, mentoring, accessibility and evaluation of training effectiveness. Approximately another 1,250 words is allocated to this section. This produces approximately 5,500 assessed written words, alongside additional practical evidence that is excluded from the formal word count. Important for your portal: I would not select “Masters” just because the assignment-type options say “MS”. These are explicitly CIPD Level 3 Foundation Certificate assessments, so Academic level = Not specified is the accurate choice with the options your system currently provides.

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People Practice / Human Resource Management 2,500 words

Business, Culture and Change in Context: Organisational Acquisition Case Study

This CIPD Level 3 assessment examines how the external business environment, organisational culture and planned change influence organisations and their people. The assessment is based on a case study involving Best Pharmacy Ever (BPE), a small group of local pharmacies considering the acquisition of Emrosu, a much larger national pharmacy chain with approximately 100 stores and an established online pharmacy operation. The acquisition creates significant implications for organisational structure, culture, technology, people practices and change management. Learners are required to provide written responses to nine case-based questions. The assessment begins by examining external factors that may affect the acquisition and identifying appropriate post-acquisition business goals. It also requires consideration of the organisations' products, services and customers and the ways technology could support people professionals, improve working practices and strengthen collaboration following organisational growth. A central component of the assessment concerns organisational culture. Learners must explain the meaning and importance of culture, consider organisations as interconnected systems, and assess how the actions of people professionals can influence wider organisational outcomes. The final questions focus on planned organisational change, the contribution people professionals can make during periods of transition and the potential impact of significant change on employees. The unit therefore develops understanding of environmental analysis, organisational systems, workplace culture, technology and effective change management from a people-practice perspective. Written answers should make clear and consistent use of the case study and demonstrate application of relevant people-practice concepts rather than providing generic theoretical descriptions. The required submission is approximately 2,500 words, with a permitted variation of ±10%, subject to the CIPD word-count policy.

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International Entrepreneurship / Business Management

International Entrepreneurship: Start-up Business Idea and International Market Expansion Pitch

This group presentation assessment requires students to develop and pitch an internationally oriented entrepreneurial business idea. Students may create their own start-up or select an existing small or medium-sized enterprise or family firm. Large multinational organisations are not permitted. The selected business must have an international dimension, either from its inception or through credible plans to undertake international activities such as overseas sourcing, manufacturing, selling or market expansion within approximately three to five years. The presentation requires students to clearly explain the proposed business idea, its products or services and the customer value proposition. Students must demonstrate how a viable market opportunity or gap has been identified and explain how the proposed venture intends to capture that opportunity. Consideration may also be given to social entrepreneurship and the potential contribution of the business to local communities. A significant part of the assessment examines how an entrepreneurial venture can overcome the practical challenges associated with start-up development and internationalisation. Students should evaluate resource scarcity and the role of entrepreneurial networks or open innovation, propose appropriate funding mechanisms such as angel investment, venture capital or crowdfunding, and explain how the venture's intellectual property could be protected. Students must also present a credible international growth strategy. This includes identifying suitable foreign markets, analysing competitors, selecting appropriate market-entry modes, defining target customer segments and developing suitable marketing and promotional strategies. All arguments should be supported with reliable evidence, with sources cited directly within the presentation and a reference list included. The assessment places emphasis on knowledge and application of entrepreneurship theories, clarity and focus of the business idea, quality and reliability of supporting materials, professional presentation delivery and the group's ability to respond confidently during questioning. All group members are expected to contribute to the work and presentation. The accompanying Group Engagement Form specifically records whether members contributed equally and allows agreed contribution percentages to be documented. Reference style: enter Not specified rather than automatically selecting Harvard. The brief requires reliable sources, slide citations and a reference list, but the uploaded assessment documents do not prescribe a named referencing system. If the actual library file you are uploading is the FourGaz presentation sample, rather than a new 2025/26 submission, use the title “FourGaz: International Entrepreneurship Start-up and Market Expansion Strategy” and do not label it 2025/26, because that sample is dated 25 November 2019.

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Cyber Security / Digital Forensics 3,500 words

Digital Forensics Portfolio: Disk Image, Memory and Windows Registry Investigation

This Level 7 Digital Forensics portfolio requires students to conduct a structured forensic investigation across disk, memory and Windows Registry evidence. The assessment develops practical investigative skills alongside professional forensic reporting and requires students to preserve evidence integrity, document methodology, interpret technical artefacts and communicate findings clearly. The portfolio is equivalent to 3,500 words and forms 60% of the module assessment. The first part involves analysing a seized USB forensic image in the context of a suspected insider involved in video piracy and potentially more serious criminal activity. Students must follow ACPO digital forensic best practice, verify image integrity before and after examination, maintain a clear chain of custody, identify significant device properties and artefacts, and justify conclusions using evidence. Tools such as FTK Imager and Autopsy may be used, alongside other appropriate forensic utilities. The scenario also requires examination of an encrypted VeraCrypt container discovered within the evidence. The second part focuses on memory forensics using a Windows memory dump. Students are expected to reconstruct process execution timelines, examine suspicious processes including PowerShell, Notepad and AtomicService, identify process owners and SIDs, extract relevant memory artefacts and produce an executive summary suitable for a non-technical audience. The third part requires an extensive Windows Registry and system artefact investigation. Students examine operating-system information, users, network configuration, login activity, suspicious files, executable and DLL creation, BAM records, Prefetch artefacts, scheduled tasks, persistence mechanisms and evidence of potentially malicious activity. Findings must be supported with screenshots, extracted artefacts or other appropriate evidence. The assignment must use the university's official portfolio template and be submitted as a PDF. The template organises the work into forensic image analysis, memory investigation and Windows Registry investigation sections. For a public Reference Library entry, this title is better than simply “Digital Forensics Coursework” because it clearly communicates the three major technical components of the work.

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Computer Science / Machine Learning

Machine Learning Portfolio Challenge: Support Vector Machines and Kernel Methods

This machine learning portfolio challenge requires students to demonstrate both theoretical understanding and practical application of machine learning methodologies introduced during the second block of the module. During Weeks 7–12, students select one of the machine learning methods covered in class and apply it to a dataset of their choice. The completed work contributes to an assessment portfolio and must demonstrate a clear, systematic and professionally documented experimental process. The accompanying learning material places particular emphasis on Support Vector Machines (SVMs) and kernel-based machine learning. SVMs are presented as maximum-margin classifiers that construct a decision hyperplane between classes, with support vectors playing the key role in defining the classification boundary. The material also introduces soft-margin optimisation, slack variables, the regularisation parameter C, primal and dual formulations, and the use of KKT multipliers. SVMs are additionally discussed in relation to multiclass classification through approaches including one-versus-all and Error Correcting Output Codes (ECOC). Kernel methods extend these principles by replacing explicit feature transformations with similarity functions. Students encounter concepts including Gram matrices, feature mappings, Mercer conditions and the kernel trick, alongside common kernel choices such as linear, radial basis function and polynomial kernels. The material demonstrates how kernel methods can represent nonlinear decision boundaries in the original input space while retaining a linear representation in an embedded feature space. The final submission must be produced as a single PDF lab notebook. It should contain clear and well-commented MATLAB, Python or equivalent code explaining each methodological step, experimental results presented through appropriate tables and/or plots, and narrative discussion explaining the selected approach, observations and conclusions. The notebook should integrate code, outputs and written explanation into a coherent and professional submission. Students may also optionally present their solution in class, where the quality of explanation and discussion can contribute additional marks.

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Data Science / Deep Learning 3,000 words

Advanced Research Topics (7PAM2016) — Building GANs from Scratch and Applying Them to Medical Imaging, Network Traffic and Sketch Generation

This Masters-level assessment asks for a complete generative adversarial network study, delivered as an annotated code submission carrying sixty per cent of the marks and a six-to-eight page technical report carrying the remaining forty. The work spans four separate GAN implementations, moving from a controlled synthetic setting into three contrasting real-world application domains. Part one builds a GAN from scratch in PyTorch on synthetic two-dimensional data. The tutorial sine-wave generator is reproduced first as a baseline, then a new distribution is modelled — a noisy parametric curve of the form y = sin(2x) + 0.3cos(5x) with an additive noise term — before the architecture itself is varied. Activation functions and layer depth are altered systematically and the resulting sample distributions plotted against the originals, so the effect of each architectural choice on convergence and sample fidelity can be seen rather than asserted. Part two applies the same principles at scale across three domains. The medical strand trains a DCGAN on the OCTMNIST subset of MedMNIST, generating synthetic optical coherence tomography retinal images, tracking generator and discriminator losses across training, and evaluating output both visually and quantitatively using Fréchet Inception Distance. A conditional GAN extension conditions the generator on class label so that images for a chosen retinal pathology can be produced on demand. The cybersecurity strand shifts from images to feature vectors, using preprocessed CICIDS 2017 network intrusion data. Benign and DoS traffic is combined and explored for class balance, a GAN is built to synthesise tabular feature vectors rather than pixels, and real against generated distributions are compared through PCA and t-SNE projections, with a discussion of how well the model generalises across attack types. The creative strand trains a DCGAN on the QuickDraw 'birthday cake' sketch category, tracking visual outputs epoch by epoch and benchmarking generated sketches against real ones, with an extension covering additional categories of differing sketch complexity. The accompanying report explains the analysis steps and the reasoning behind each architectural decision rather than restating textbook definitions of the method. It gives brief descriptions of the models used, presents generated samples and loss curves as figures, interprets the evaluation metrics, and reflects honestly on failure modes — training instability, mode collapse, and the visible flaws in synthetic output that determine whether such data is fit for downstream use. The code is written as reusable functions, commented for a reader other than its author, and reproduces every figure and numerical value quoted in the report.

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Computer Science / Database Systems 4,000 words

Advanced Databases (KL7011) — NORTHERNTOURS Coach Travel Database: EER Design, Oracle Implementation, Object-Relational and NoSQL Extensions

This piece of work addresses a four-part Masters-level assessment in Advanced Databases built around NORTHERNTOURS, a fictitious coach travel operator running services across cities, towns and tourist sites in the North East of England. The company sells tickets through a network of independent travel agents, each currently working from a paper-based sales book, while NORTHERNTOURS itself maintains separate paper records for routes, schedules, seat availability, vehicles and drivers. The brief asks for a single computer-based system capable of replacing both, tracking every agent transaction while giving the company control over ticket issue and seat allocation. Part one covers the conceptual and logical design. An enhanced entity-relationship model was produced covering agents, agent employees, customers, tickets, routes, stops, schedules, vehicles, drivers and the meal provision recorded at each stop, with key attributes, primary keys and full structural constraints shown. Because the scenario does not name identifiers for most entity types, appropriate surrogate and natural keys were devised and justified. The diagram was then mapped to a logical relational schema, normalised to third normal form, with a documented naming convention applied consistently across relations, attributes and keys, and every element recorded in a text-based data dictionary giving names, data types, descriptions and constraints. Part two moves to implementation in Oracle. A full DDL script creates the relations with primary and foreign keys and a substantial set of check constraints — key format patterns, positive seat counts and fare values, date ordering on schedules. Sample data populates the relevant tables, and two retrieval problems are answered twice over, once in relational algebra and once in SQL: schedules between Newcastle and Berwick-upon-Tweed with seven or more seats free in the coming fortnight, and the agent with the highest ticket sales across a defined month. Spooled session output evidences each script running. Part three revisits the conceptual design to argue where object-relational features earn their place — nested route-and-stop structures and composite address and contact types being the clearest candidates — implemented using Oracle object types, VARRAYs and nested tables, and demonstrated through two multi-join aggregate queries. A parallel discussion identifies the schedule and availability workload as a fit for document-oriented NoSQL storage, with representative code and a reasoned account of the denormalisation trade-offs involved. Part four is a report to the managing director covering sustainability, professional, legal, ethical and security obligations, alongside diversity, inclusion, cultural and environmental matters, commercial risk evaluation and mitigation, supported throughout by current literature and published standards.

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Engineering and Professional Practice 3,000 words

Internship Reflective Report and Poster Presentation — Professional Practice in an Industry Placement

An internship module assessment asks students to convert a period of workplace experience into evidenced academic reflection. It is not a report on what the host organisation does; it is an account of what changed in the student's own understanding of their discipline as a result of working outside the taught programme. The written component typically runs to around three thousand words and is built in weighted sections. An opening section covers how the placement was secured — the reasoning behind the choice, the search and application process, and what that process itself taught the student. The bulk of the marks then sit on the reflection proper: what the student actually did, how existing disciplinary knowledge held up when applied in an unfamiliar setting, and where gaps appeared. A final section addresses personal and professional development, supported by specific examples rather than general claims of having "grown in confidence." The second component is a short poster presentation summarising the placement and its learning, delivered live. Posters are assessed on professional presentation, clarity, correct referencing and balance — students commonly over-fill them with text that a viewer cannot read at presentation distance. Formatting requirements on this type of assessment are unusually prescriptive and carry marks: a cover page with name, student ID, tutor name and declared word count; a table of contents; page numbers; captions on every figure and table; specified font, size and line spacing; numbered headings; and a strict file-naming convention. Marks are lost here for no reason other than inattention. Note also that in-text citations and quotations usually count toward the word limit even when tables and references do not, and markers may simply stop reading once the limit is exceeded by more than the allowed margin. The defining challenge of reflective assessment at this level is criticality. Rubrics consistently distinguish description from evaluation: recounting tasks performed scores at the lower bands, while analysing why something was difficult, what it revealed about a gap in preparation, and what will be done differently scores at the top. Reflective frameworks give this structure, but they must organise genuine experience rather than substitute for it. This assessment is inherently personal — the content derives entirely from the student's own placement. Our support is therefore confined to guidance: explaining what distinguishes descriptive from critical reflection with worked examples, showing how a reflective framework structures a section, advising on poster layout and information density, checking formatting and referencing against the specification, and reviewing a student's own completed draft against the published rubric.

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Entrepreneurship and Business Start-up 5,000 words

Enterprise Start-up Portfolio — Business Plan, Fundraising Strategy and Entrepreneurial Self-Assessment

An enterprise start-up portfolio assessment asks students to originate a business idea and build the full supporting case for it, then present that case both in writing and as a recorded investor pitch. It is a pass/fail module structure in which every component must be passed individually — a strong business plan cannot compensate for a weak pitch, or vice versa. The written portfolio divides into two unequal halves. The larger part is the business plan itself, built to a fixed section structure with prescribed word allocations: an executive summary; the business idea set against an identified market gap, with market research and competitor analysis; customer profiles and segmentation; product development; marketing and communications; financials and business models covering costing, pricing, sales and revenue; and the founding team with its core competencies. The word allocations are not decorative — financials carry the single largest weighting in both the word budget and the marking rubric, which tells students where analytical depth is expected and where concision is. The smaller part is reflective. It covers the fundraising strategy — which funding sources are realistic for this venture, and how each would be targeted, approached and secured — and the student's own entrepreneurial tendency, informed by a standardised self-assessment instrument taken online. The rubric here rewards critical self-reflection over description: reporting a test result scores poorly; interpreting what the result means for how this founder should build a team and where they need support scores well. The pitch component is assessed on visual and audio quality, content and message, comprehension, delivery, and evident preparation. Technical execution carries real marks, which students routinely underestimate. Two things separate strong submissions. The first is internal consistency: the revenue model must follow from the pricing, the pricing from the customer segment, the segment from the identified gap. Plans that read as seven separate essays under seven headings lose marks even when each section is individually competent. The second is specificity in the financials — costing assumptions stated and justified, rather than round numbers presented without derivation. Note that assessments of this type commonly require the student to retain all drafts and earlier versions of their work, and to sign a detailed declaration itemising exactly how any AI tools were used. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining what a market gap argument needs to be credible, showing how competitor analysis should be structured rather than listed, clarifying how costing and pricing assumptions should be built and presented, reviewing whether a fundraising strategy matches the venture's actual stage, explaining how reflective writing is assessed at postgraduate level, and checking a completed draft against the published rubric.

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Operations and Supply Chain Management 1,800 words

Strategic Sourcing and Supply Chain Resilience Under Global Uncertainty — A Firm-Level Critical Evaluation

This type of postgraduate coursework asks students to take a single real firm — a British company, or a multinational with substantial UK operations — and critically evaluate how its sourcing and supply chain strategy has adapted to a decade of sustained disruption: the pandemic, geopolitical conflict, tariff volatility, and departure from the European single market. The analytical scope is broad but the depth expectation is narrow. Students choose one or two themes rather than surveying all of them: insourcing versus outsourcing and location decisions; single versus multi-sourcing across supplier segments or regions; how supplier selection and monitoring criteria have shifted toward reliability and geographic proximity; alignment between sourcing decisions and wider corporate objectives; supplier and customer relationship management under cross-border friction; inventory tactics such as stockpiling and safety stock repositioning; digital adoption for visibility and compliance; sustainable sourcing under cost pressure; or risk management around currency, customs and compliance. The evidence base is deliberately mixed. Alongside academic literature, students are expected to draw on annual reports, shareholder briefings, company statements, practitioner journals and recent news coverage — with a minimum spread of credible sources across both categories, and some of the non-academic material drawn from the most recent year so the analysis reflects current firm behaviour rather than historical commentary. At least one theoretical framework from the module or the wider literature must be applied to the firm's decisions, and at least one recent documented practice must be examined with specific sourcing. Two requirements distinguish strong submissions. The first is criticality: the brief explicitly separates evaluation from description, and marks weight critical analysis most heavily. Reporting what a firm did is not the task; assessing whether it was the right response, and what opportunities and risks it created, is. The second is the recommendation — at least one specific, actionable proposal with steps, anticipated obstacles, expected outcomes, and a clear line back to the firm's own stated goals. A generic suggestion to "diversify suppliers" fails this test; a costed, sequenced proposal grounded in the firm's actual constraints does not. Word-count conventions are stricter than students often expect: in-text references, tables, illustrations and front matter typically count, while appendices and the reference list do not. Our support on assessments of this type is guidance-based. Typical areas of help include: narrowing a firm and theme so the analysis fits the word limit, explaining the difference between description and critical evaluation with worked examples, clarifying how a theoretical framework should structure an argument rather than sit decoratively in a paragraph, checking source mix and Harvard referencing consistency, and reviewing a completed draft against the published assessment weightings.

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Available Model Answers (99)

Suez Canal Blockage: A Case Study of Global Supply Chain Disruption

Automated Container Deployment and Administration in the Cloud

A Comparative Analysis of Cultural Factors Shaping Human Resource Staffing Strategies in International Enterprises

CW1: Applying advanced AI methods for analysing text documents

CW: Security Evaluation

Individual Reflection on Case Study Discussion – International Business Strategy

Time Series Modelling Case Study – Oil Price Forecasting

Computational Algorithm Analysis – Research Paper Algorithm

Writing a Literature Review in Computing Science

Calculus and Optimisation: Python Implementation of Gradient Descent, Derivatives and Polynomial Regression

Critical Analysis of Computational Algorithms: Research Paper Evaluation and Complexity Analysis

Applying Advanced AI Methods for Analysing Text Documents

Individual Reflection on Tencent’s Strategic Reorganisation and International Business Strategy

Automated Container Deployment and Administration in the Cloud

Comparative Analysis of Cultural Factors Shaping Human Resource Staffing Strategies in International Enterprises

Time Series Modelling Case Study: Oil Price Forecasting with ARMA and Alternative Models

Critical Review of Global Logistics: Supply Chain Strategy, Resilience and Sustainability

Digital Forensics Investigation: USB, Memory and Windows Registry Analysis

Parallel Merge Sort with Load Balancing

AI Innovation Consultancy: Evaluating Artificial Intelligence Solutions for Business Problems

Secure Property Contract Exchange: Cryptographic Protocol Design, Threat Modelling and Post-Quantum Readiness

Research Specialism Report: Critical Review of Open Research Questions in Computer Science

Generative Modelling Case Study: GANs for Medical Imaging, Cybersecurity and Creative AI

Software Measurement, Goal–Question–Metric Analysis and Process Model Evaluation for DETO

Ethical Hacking and Penetration Testing: Vulnerability Exploitation, Privilege Escalation and Mitigation

Digital Forensics Investigation of a USB Device: Evidence Acquisition, Analysis and Reporting

Java Group Exercise Booking and Management System for Furzefield Leisure Centre

Catnip Games International: SOC Automation and Incident Response Platform

Artificial Intelligence and Machine Vision: Neural Network-Based Image Processing Application

Statistical Programming with R: Data Analysis, Probability, Regression and Business Decision-Making

Global Business Strategy – Strategic Analysis of a Global Company

Success Through Business Ethics – Ethical Analysis of an FMCG Brand

AP Research Project – Reflective Individual Report and Poster Presentation

Critique of a Data Science Book and Selected Chapter

Independent Critical Analysis 30

Specialism Research Report

Environmental Sustainable Construction & Logistic Sources – EG7037

Three Weekly Digital Marketing Tutorial Challenges

Principles of Management – Tesco Management Report

Leading Through Digital Change (CW1)

LD7152 – AP Research Project

Team Research and Development – Research Question, Hypothesis and Statistical Data Analysis

Data Mining – Classification, Model Optimisation and Evaluation

7COM1076 Network Design, Modelling and Evaluation Coursework

Networking and Security Practice – Recorded Demonstration

CW1: Policy and Legal Aspects Report – IoT System

Strategic Case Study – Corporate Strategy

Digital Security Risk and Audit – Information Security Audit of ABC Air

Network Security Evaluation and Monitoring – Reconnaissance, Incident Response and APTs

Machine Learning for Big Data

Ethical Hacking – Professional Penetration Testing Report

Cryptography – Secure Land Transaction Contract Exchange Protocol

Reflective Analysis of Entrepreneurial Capabilities and Development: The GymBuddy Venture

Cryptography – Secure Contract Exchange Protocol

International Marketing and Brand Management – International Brand Expansion Project

Leadership and Change – Reflective Learning Portfolio

Management Practices and Value Creation at Tesco

Emerging Technology and Cloud Computing – SafeCloud Project

Strategic Evaluation and Analysis of Hindustan Unilever Limited (HUL)

Enhancing Sustainable Supply Chain Practices through Digital Transformation: A Case Study of Unilever Plc

Consultancy Project Proposal

Customer Experience Strategy Report

Bright Network Academy – Learn New Skills

Network Systems and Administration – Linux System and Network Administration Portfolio

Information Organization System Case Study

Multimodal Sentiment Intelligence Platform for Dynamic Market Insights

Digital Marketing Analytics (BS783) — GA4 Performance Analysis and 90-Day Campaign Strategy for the Google Merchandise Store

Data Visualisation (BS666) — Business Analyst Client Report: Dashboard Development, Tool Evaluation and Accessibility in Power BI and Tableau

Data Visualisation (BS664) — Portfolio: Tableau and Power BI Dashboard Development for Evidence-Based Decision-Making

Data Design Management (BS514) — Data Strategy Consultancy: Relational Database Design, SQL Implementation and Pipeline Transformation

Strategic Sourcing and Supply Chain Resilience Under Global Uncertainty

Developing an Intelligent Chatbot and Expert System for UK Train Services

Customer Service Escalation Risk Analytics Using Data Mining and Machine Learning

AI-Augmented Digital Forensics Workflow Audit: Feasibility and Risk Assessment of ForensiScan AI

Safaricom Business Hub: Strategic B2B Digital Payment Platform for East African SMEs

Cyber Security and Cloud Defence Strategy for ShieldSafe Analytics

Development and Evaluation of Deep Learning Models for Healthcare Classification

End-to-End Machine Learning Model Development, Tuning and Evaluation