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Advanced Research Topics
Generative Adversarial Networks – Building and Applying GANs to Real-World Problems
This individual assessment focuses on building and understanding Generative Adversarial Networks (GANs) and applying them to real-world problems across different domains. The assignment is based on the GAN topics covered in Unit 5 and requires students to implement GAN models, analyse their behaviour, generate synthetic data and critically evaluate the quality of the generated outputs. The assessment consists of two main parts: building and understanding GANs from scratch using synthetic two-dimensional data, followed by applying GANs to three real-world application areas involving medicine, cybersecurity and creative artificial intelligence. Part 1 focuses on developing a practical understanding of GANs through implementation using PyTorch and synthetic 2D data. Students must first reproduce the sine-wave GAN demonstrated in the tutorial. They then create and model a new two-dimensional distribution, selecting from a 2D spiral, a mixture of Gaussians, or a noisy parametric curve defined as y = sin(2x) + 0.3cos(5x) + epsilon. Students must modify the GAN architecture, for example by changing the activation function or layer depth, and visually compare the original data distribution with the generated samples. This section is intended to reinforce understanding of the generator, discriminator, GAN training process and the effect of architectural choices on generated data. Part 2 addresses three real-world GAN applications. The first application concerns optical coherence tomography (OCT) retinal images using the OCTMNIST subset of MedMNIST. Students must explore the dataset, examine class distributions and sample images, build a ConvNet-based Deep Convolutional GAN (DCGAN) using PyTorch or TensorFlow/Keras, train the model and track generator and discriminator losses. Generated retinal images must be compared with real images both visually and quantitatively, using a performance measure such as the Fréchet Inception Distance (FID). An optional extension involves implementing a conditional GAN and demonstrating generation of images for specific classes. The second application focuses on cybersecurity using the preprocessed CICIDS2017 dataset containing DDoS and benign network traffic. Students must combine the relevant data files, explore the dataset and understand its features and class balance. A GAN must be developed to generate synthetic feature vectors rather than images. The model should be trained using benign and DDoS attack data, training loss curves should be monitored, and PCA or t-SNE should be used to visualise and compare real and generated feature distributions. The quality of the synthetic data should then be evaluated. An optional extension involves expanding the analysis to the full CICIDS2017 dataset and examining generalisation across different attack types. The third application focuses on Creative AI using the QuickDraw birthday cake category. Students must explore the birthday cake sketch dataset and implement a ConvNet-based GAN (DCGAN) to generate realistic birthday cake sketches. Generated sketches should be evaluated visually and quantitatively, including comparison with real examples and an appropriate metric such as FID. An optional extension involves generating samples from additional QuickDraw categories and discussing how model performance changes across different classes and levels of sketch complexity. The submission consists of both code and a written report. The code accounts for 60% of the assessment and must complete the required modelling tasks, present generated samples, compare generated and real data, use appropriate functions and include clear annotations so another user can understand the implementation. The report accounts for 40% and must be 6–8 pages. It should explain the analytical steps undertaken, justify the selected approaches, provide brief descriptions of the models used, interpret the results and include appropriate figures, evaluation metrics and academic references. The report should critically discuss why particular network architectures were selected rather than simply providing textbook definitions.
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Research Methods
Assignment 3 – Large Language Models: LLM Coding and Report
This individual assessment for the Research Methods module focuses on the application of Large Language Models (LLMs) to a practical data science problem. The assignment is worth 25% of the module and is designed to develop students’ knowledge and understanding of research methods, investigative planning, data analysis, model evaluation and effective technical communication. Students are required to complete both a coding component and a concise written report demonstrating how an LLM has been selected, trained or fine-tuned, applied to a suitable task and evaluated against an appropriate baseline. The assessment begins with familiarisation with relevant literature. Students are expected to investigate the history and development of their chosen problem and examine the methods that have previously been used to address it. The brief provides key papers on common types of LLMs as a starting point for the literature review. Students then select a task that can be addressed through fine-tuning an LLM, with examples including sentiment analysis, fake news detection and topic classification. A publicly available dataset suitable for the selected text-classification problem must also be identified. Suggested sources include Kaggle and Hugging Face Datasets. The data must be appropriately preprocessed, including tokenisation using BERT's tokenizer and division into training and testing sets. Students then fine-tune a pre-trained BERT or BERT-style model using suitable tools such as the Hugging Face Transformers library and PyTorch. Possible model choices include BERT, RoBERTa and T5. The selected model should be appropriate for the specific problem, recognising that different language models may perform differently across tasks. Students are expected to implement a suitable training process using an appropriate optimiser and loss function. Model performance must be evaluated using relevant classification metrics, including accuracy, precision, recall and F1-score. The performance of the selected LLM should also be compared with a baseline model, such as Logistic Regression, Naive Bayes or a pre-trained BERT model. The analysis should explain the results and consider their relevance to the chosen problem. Two main submission components are required. The first is a code notebook, such as a Jupyter or Google Colab notebook, containing annotations explaining the purpose and operation of the relevant code so that another person can understand and reproduce the work. The second is a report of no more than three pages, including appropriate figures, tables and references. The report should cover the motivation and dataset, methodology, model training and evaluation, results and discussion, limitations, conclusion and possible future improvements. The assessment rubric places particular emphasis on coding quality and implementation, model architecture, analysis and interpretation, and report presentation. Strong work should demonstrate well-structured and reusable code, clear explanation of the model architecture and configuration, appropriate evaluation metrics and visualisations, meaningful comparison with relevant literature or baseline models, and critical evaluation of the model's success and possible improvements.
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Global Strategic Management
3,500 words
Global Strategic Management – Linear to Circular Business Model Transformation
This assessment for the Global Strategic Management module examines how organisations can transform from traditional linear business models towards circular business models. Students must select one of four approved organisational cases and use the same case consistently across both summative assessments. The approved cases are SKF, focusing on servitisation and remanufacturing transformation; Sandvik, focusing on closed-loop materials and tool recovery; Volvo Construction Equipment, focusing on design for remanufacturing and lifecycle optimisation; and Husqvarna, focusing on access-based and circular consumer models. The assessment requires students to apply strategic management, sustainability and circular economy concepts to evaluate the transformation of the selected organisation. Assessment A is a Strategic Poster worth 30% of the overall module mark. The poster should visually and analytically map the transformation of the selected organisation from a linear to a circular business model. It contains three main components. First, students must identify and analyse external drivers using PESTEL, explaining the factors creating pressure or opportunities for circular transformation. Second, students must explicitly reconfigure the organisation's value chain, distinguishing between primary activities such as inbound logistics, operations, outbound logistics, marketing and sales, and service or lifecycle activities, and supporting activities such as firm infrastructure, human resource management, technology development and procurement. Third, students must identify one core structural strategic tension created by the transition. This tension should represent a built-in trade-off in which improving one strategic objective may constrain or challenge another. Assessment B is a Strategic Evaluation Report worth 70% of the overall module mark. The report has a word limit of 3,500 words with a 10% tolerance, excluding the table of contents, reference list and appendices. The report requires a comprehensive strategic evaluation of the selected organisation's transition from a linear to a circular business model and should integrate concepts and frameworks covered throughout the module. The first component of the report requires a Business Model Reconfiguration Analysis using the Business Model Canvas. Students should critically analyse how the circular transformation changes the organisation's value proposition, customer segments, customer relationships, channels, key activities, key resources, key partnerships, revenue streams and cost structure. The analysis should explain how value is created, delivered and captured differently under the circular model. The second component evaluates sustainability and legitimacy using an integrated analysis of the Triple Bottom Line, SAFe framework and stakeholder power and interest analysis. Students should assess economic, environmental and social sustainability, evaluate the suitability, acceptability and feasibility of the transformation, and identify stakeholders who can enable or constrain implementation. The third component requires students to analyse the same strategic tension identified in the poster. The tension should be evaluated in greater depth using appropriate strategic, sustainability and stakeholder frameworks, followed by a theoretically grounded and operationally feasible recommendation. The final component requires students to integrate four of their strongest reflective blog posts from the twelve seminar weeks. The reflection should demonstrate intellectual development, critical thinking, engagement with theory, seminar participation and responsiveness to tutor feedback rather than simply describing the content of the seminars. The assessment is expected to demonstrate Level 7 academic standards through integrated framework application, critical evaluation, strategic judgement and advanced reflective insight. Academic sources must be cited using Harvard referencing, with appropriate in-text citations and a complete reference list.
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Fundamentals of Digital Technologies
Fundamentals of Digital Technologies – Group Project and Individual Refinement
This assessment is a project-based assignment for the DG4FDTL Fundamentals of Digital Technologies module. It is designed to develop students' practical understanding of digital technologies by combining mathematical concepts, programming, algorithms, data analysis and learning methods within a real-world project. The project is structured around a common group component, known as the Group Trunk, and an individual component in which each student develops a specialised refinement of the shared project. The project topics are designed around key areas covered by the module, including linear algebra, calculus, probability, learning algorithms, algorithms and Python programming. Students work in groups to establish a common project foundation and then develop an individual refinement that extends the functionality or analytical capabilities of the shared system. The project guidance is intended for a mixed cohort that may include Data Science, Computer Science and Business Analytics students. The assessment is divided into two main components: Proposal and Implementation. The Proposal accounts for 40% of the assessment, while Implementation accounts for 60%. Both the group trunk and individual trunk contribute to the assessment. The group proposal requires students to describe what the group intends to build, explain the problem being addressed, outline the proposed approach and identify the main functions and responsibilities within the project. Students should demonstrate a clear understanding of the project objectives and provide an appropriate plan for developing the shared system. The implementation stage requires students to develop the common group functionality and then complete their individual refinement. The shared component provides the basic project framework, while the individual refinement allows each student to investigate a specific aspect of the problem and add specialised functionality. Depending on the selected project topic, individual refinements may involve data analysis, visualisation, optimisation, prediction, monitoring, reporting or other computational features. The project topics include practical applications such as productivity and task-management systems, supermarket sales analysis and other data-driven applications. Students are expected to use Python and appropriate libraries or computational techniques to implement their solutions. The project materials provide examples involving data structures, CSV files, functions, numerical calculations, visualisation and analytical dashboards. The assessment emphasises both technical implementation and the student's ability to explain the problem, approach and functionality of the developed system. Students should demonstrate appropriate use of programming concepts, mathematical foundations, algorithms and data-analysis techniques. The individual refinement should clearly extend the common project and demonstrate the student's own contribution to the overall solution. Overall, the assessment develops practical digital-technology skills through collaborative project development followed by individual technical refinement. It provides experience in project planning, programming, computational problem solving, data analysis, visualisation and the application of mathematical and algorithmic concepts to practical problems.
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Data Science
1,000 words
Individual Project – Image Segmentation
This individual project focuses on image segmentation using a subset of the ADE20K dataset. The assessment requires students to investigate viable image segmentation models, analyse and visualise the provided data, develop appropriate data preprocessing procedures, train a segmentation model and critically evaluate the resulting performance. The assignment is worth 25% of the module assessment and is designed to assess students’ ability to apply research methods to a data science problem, communicate analytical findings effectively and select appropriate methods while understanding their advantages and limitations. Students are required to work with the provided ADE20K dataset and produce a model capable of semantic segmentation for four specified object classes: person, car, book and airplane. The model must identify which regions of an unlabelled image correspond to these four classes. The assignment specifically requires semantic segmentation rather than simply identifying whether an object is present. Treating the task as binary segmentation, where all objects are considered a single foreground class, will result in a significant penalty. The project requires students to investigate different approaches and models, perform exploratory data analysis, develop suitable preprocessing procedures and customise their selected model or models to improve performance. Students may use more than one model, although at least one model must be partially or fully trained by the student. Where multiple models are used, comparison with pretrained models is encouraged. Potential approaches include segmentation architectures such as U-Net and Mask R-CNN, with appropriate model selection justified through relevant literature and experimental evidence. The report should contain at least four core sections: an Introduction incorporating a literature review, Data Description and Exploratory Data Analysis, Methodology, and Results and Discussion. The literature review should cite at least three relevant research papers. The results section should include evaluation of three test images using the developed model and comparison with relevant published literature. Students are expected to provide a critical analysis of their work rather than simply reporting numerical results, explaining the reasons for observed outcomes and considering how modelling and implementation choices affected performance. The assessment also requires a documented Google Colab notebook containing the implemented steps used to train and evaluate the model. The notebook should be accessible to markers and should demonstrate the preprocessing, model development, training and evaluation process. The report must be between 700 and 1,000 words, excluding references and code, and should include code in text format as an appendix rather than screenshots. Figures and tables should have clear captions explaining what they present and their source. Assessment criteria include literature review and context, dataset description and exploratory data analysis, implementation quality, quality of analysis and critical discussion, and report quality and structure. Strong work is expected to demonstrate appropriate model selection, effective preprocessing and augmentation, suitable evaluation metrics such as Intersection over Union (IoU) and Dice score, meaningful visualisation and a critical interpretation of results and limitations.
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Leading and Managing Change
2,000 words
Develop a Change Management Plan for a Specific Organisational Context
This assessment is an individual report for the BUSI1815 Leading and Managing Change module. The task requires students to develop a change management plan for a specific organisational context involving either a global organisation or a UK-based organisation. The proposed plan should be based on an actual organisational change initiative and demonstrate an understanding of the theories, concepts and tools covered throughout the module. The report requires students to analyse the selected organisational change initiative and develop a practical and theoretically informed change management plan. The analysis should consider the organisational context in which the change is taking place and examine the factors that may influence the success of the change strategy. Particular attention should be given to resistance to change and the managerial, organisational culture and social factors that can affect how change is introduced and managed. A central requirement of the assessment is the critical evaluation and application of relevant change management models and frameworks. Students should use appropriate theories and concepts to support their analysis and demonstrate how these can be applied to the selected organisation and its specific change initiative. The resulting change management plan should provide a detailed implementation strategy that explains how the proposed change can be managed within the chosen organisational context. The report should also address the management of resistance to change, particularly where the organisation operates in a global context. Students are expected to consider how managerial practices, organisational culture and social factors may create challenges or opportunities during the change process. The plan should therefore connect theoretical perspectives with practical organisational considerations and provide appropriate strategies for implementing and managing the change. The assessment has a total word length of 2,000 words and is weighted at 60% of the module assessment. The required structure consists of an Introduction of approximately 200–300 words, a main Change Management Plan of approximately 1,600–1,900 words, and Recommendations and Conclusion of approximately 200–300 words, followed by a reference list. The main section should present the change management plan for the chosen company and should be supported by relevant change management theories and concepts. The assessment addresses the learning outcomes relating to analysing the effects of resistance to change in a global context and critically evaluating change management models and frameworks. Students should therefore demonstrate critical understanding, appropriate application of theory, analysis of organisational and contextual factors, and the ability to develop a practical change management plan supported by academic evidence.
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Engineering and Environment Advanced Practice London Campus Consultancy Project
5,000 words
Group Consultancy Report – Engineering and Environment Advanced Practice
This assessment is a group consultancy project for the LD7119 Engineering and Environment Advanced Practice London Campus Consultancy Project module at Northumbria University. Students work in consultancy groups of four to five members and are assigned a live project in collaboration with an organisation and the university. The purpose of the assessment is to investigate an organisational issue or requirement and develop practical, evidence-based solutions that can support the organisation in implementing improvements. The main assessment consists of a 5,000-word Group Consultancy Report and a compulsory 10-minute client presentation. The report is expected to demonstrate a professional and commercially appropriate approach to consultancy work. It should provide a clear introduction to the project, establish the organisational context, and analyse the business requirements and needs of the client organisation. The report should then explain the research methodology used, including appropriate ethical considerations and the design of the practical work undertaken. A substantial part of the assessment focuses on research, discussion and findings. Students are expected to collect and analyse relevant evidence, present their findings clearly, and provide evidence of practical implementation and testing where applicable. The assessment therefore requires students to connect academic research and relevant theories with practical consultancy activities and organisational requirements. Evidence from diagnostic tools, feedback and other appropriate sources can be incorporated into the report to support the analysis and conclusions. The final part of the report focuses on recommendations and improvements. Students should develop practical and actionable recommendations that are relevant to the organisation and can contribute to the successful implementation of the proposed ideas. Recommendations should be supported by the research findings and should demonstrate an understanding of the organisation's requirements and potential implementation considerations. The assessment is worth 50% of the total marks available for the module, which is assessed on a pass/fail basis. The report has a 5,000-word limit, excluding the table of contents, page numbers and captions for figures and tables. The client presentation is compulsory, although it does not have marks directly allocated to it; its purpose is to help the client and supervisor understand the consultancy project. The assessment is evaluated across professional and commercial presentation and introduction, business and requirement analysis, research methodology including ethics and practical work design, research and findings including implementation and testing, and recommendations including improvements. Students are also required to acknowledge sources appropriately and complete the assessment declaration regarding their work and any use of generative AI. The assessment brief states that AI may assist with activities such as improving grammar, structure, organising ideas and providing suggestions, but the main content, analysis and conclusions must remain the student's own work.
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Cloud and Big Data Technologies
2,500 words
Cloud and Big Data Technologies – Summative Assessment: Cloud and Big Data Security Application
This summative assessment for the CST4067 Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud and big data security application. The assessment focuses on applying techniques for the complex transformation and processing of data within distributed and cloud-based environments, while considering security, privacy and access-control requirements. Students are expected to develop a practical application and document its design, implementation and evaluation through a technical report and a short demonstration video. Students are given flexibility to select their own project idea, provided that the proposed application is appropriately scoped for the available development period and demonstrates relevant cloud, big data and security technologies. Suggested project areas include data science applications using SQL, MongoDB and cloud storage, privacy-preserving data processing such as Differential Privacy, multi-party authentication, group-based security and access control, Multi-Level Security, Attribute-Based Encryption, and distributed or cloud-based applications incorporating security protocols such as SSH, SSL or IPSEC and access-control mechanisms such as RBAC. The Design and Implementation Document should be no longer than 2,500 words and should explain the major design and implementation aspects of the project. Expected content includes an introduction covering the aims, objectives, project concept, security concepts and cloud technologies used; a requirements specification addressing programme behaviour and security requirements; analysis and design covering protocols, access control and interaction, sequence diagrams or process specifications; implementation details explaining what was achieved and how it was developed; and an evaluation and conclusion discussing successful and unsuccessful aspects, problems encountered and lessons learned. Relevant references, including tutorials, books and academic articles, should also be provided using Harvard or IEEE referencing. The assessment also requires students to submit the implemented Cloud and Big Data Security application together with a highlight demonstration video. The video must be no longer than seven minutes and should demonstrate the main features of the application, including relevant interactions, implementation highlights, security features and, where appropriate, attack scenarios. Assessment is based on the Design and Implementation Document, originality, advanced features, and the effort and quality demonstrated in the application. The assessment specification places particular importance on original development, clear documentation of any tutorials or existing resources used, and evidence that the student understands the technologies implemented. Suggested technologies and project ideas include Google Cloud, Hadoop, Spark, cloud storage, data pipelines, security protocols, access control and privacy-preserving techniques.
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Databases
1,000 words
Assessment #1 – Advanced Databases: NORTHERNTOURS Database Design and Implementation
This assessment for the Advanced Databases module (KL7011) focuses on the analysis, design and implementation of a database system based on the NORTHERNTOURS scenario, a fictitious transport company operating a fleet of luxury coaches across cities, towns and tourist locations in Northern England. The assessment requires students to demonstrate advanced database knowledge through conceptual modelling, logical database design, SQL implementation, data manipulation and the evaluation of alternative database technologies. The assessment addresses learning outcomes relating to the data life cycle, advanced data modelling and database design, as well as professional, legal, ethical, security, sustainability and risk considerations. The first part requires students to develop a conceptual database design for NORTHERNTOURS using Entity-Relationship (ER) or Enhanced Entity-Relationship (EER) modelling. The design should identify relevant entities, relationships, key attributes, primary keys and structural constraints. Students then convert the conceptual model into a logical relational schema using ER/EER-to-relational mapping, identify primary and foreign keys, ensure the relations satisfy Third Normal Form (3NF), select and justify a consistent naming convention, and produce a textual data dictionary containing relevant names, descriptions and constraints. The logical design is subsequently implemented using Oracle 11g, 12c or higher through appropriate SQL DDL statements and database constraints. The second part involves populating selected database relations with self-generated sample data and demonstrating database retrieval capabilities. Students must provide SQL DML statements, relational algebra expressions and SQL queries for specified NORTHERNTOURS business requirements. The solutions must be executed in a live Oracle environment and supported with appropriate output evidence. The third part extends the database analysis by considering object-relational and NoSQL database technologies. Students evaluate which aspects of the NORTHERNTOURS conceptual design could benefit from object-relational implementation, develop and populate a suitable object-relational subset, and demonstrate it through complex queries. They also analyse where NoSQL concepts could provide benefits and discuss design choices supported by representative NoSQL implementation code. Finally, students prepare a concise report for the NORTHERNTOURS managing director addressing sustainability, professional, legal, ethical and security issues, together with diversity, inclusion, cultural, societal and environmental considerations and commercial risk management. The report should use a critical review of relevant literature, systems, developments and standards and follow Harvard referencing conventions.
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Operating Systems and Networks
2,000 words
Security of Operating Systems and Networks
This individual assignment for the Operating Systems and Networks module focuses on the security of operating systems and computer networks within a business environment. The assessment requires students to produce a professional technical report of approximately 2,000 words, supported by appropriate images demonstrating practical steps and testing activities. The assignment assesses students' knowledge and understanding of operating system and network security threats, vulnerabilities, testing approaches and practical security solutions. It also addresses the ability to develop and write complex scripts to solve problems relating to operating systems and computer networks. The assignment is based on a business scenario involving Net-Tech, a small and medium-sized technology-services enterprise. The organisation is developing a web server to provide its website to customers and is concerned about the security of the proposed system. Key concerns include operating system attacks such as buffer overflow attacks, as well as network threats involving hackers and phishing. Students are required to investigate, design and experiment with the features and functions of a web server and assess the security landscape so that appropriate security requirements can be identified. As part of the practical work, students must create a prototype Net-Tech network test rig. This involves creating two users, including a superuser and a standard user, applying appropriate baseline security, installing relevant software and security tools, conducting security tests and writing scripts to automate repetitive tasks. Students must document assumptions and parameters used in their implementation and may also make recommendations for further security measures and database-driven website functionality. The report covers several stages of the security assessment. The introduction establishes the business scenario, assumptions, aims, objectives, deliverables, available skills and resources, constraints and project scope. The background research examines common vulnerabilities, security threats, risk models, testing approaches and attack or testing tools, together with relevant legal, organisational, ethical, social and professional considerations. The pre-engagement stage focuses on setting up a suitable Linux or Kali Linux testing environment and developing a justified test plan. The engagement stage requires practical investigation of operating-system and network security. Areas include user authentication, file and directory permissions, protection against stack overflow attacks, network weaknesses, operating-system discovery, firewall configuration, listening ports, network statistics, denial-of-service prevention and script-based automation. The final post-engagement section requires students to summarise their work, identify deductions and limitations, propose mitigation measures and recommendations, and provide a self-reflection. The assessment is aligned with learning outcomes relating to understanding network security threats and developing software or scripts to solve problems in operating systems and computer networks. Students are expected to use relevant sources, reference them appropriately and include a bibliography. Practical tools such as Wireshark are specifically identified in the assignment brief, and the work should demonstrate appropriate testing methodology and security practices.
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information Governance and Cyber Security
2,500 words
Information Governance Policy for Healthcare Assistant (HCA)
This assessment is the group component of the Information Governance and Cyber Security module and requires a 2,500-word report for the fictional Healthcare Assistant (HCA) organisation. HCA is presented as a large private hospital group operating across the UK, with specialist healthcare services, intensive care facilities and a wide network of GPs, departments, partner hospitals, medical centres and third parties. The organisation processes highly sensitive information including patient personal information, admission details, health records, staff information and other organisational data. These data are shared across different sites and partners and are also used for analytical purposes, treatment planning and marketing activities. The assessment requires students to develop an Information Governance Policy for HCA and provide an accompanying report that justifies the policy contents, selected framework, risk assessment methodology and implementation strategy. The objective is to establish a robust information governance structure capable of protecting HCA's information assets while supporting legal, regulatory and contractual compliance. The first component of the report focuses on the introduction, purpose and scope of the Information Security Policy. Students should establish the organisational context, explain why information governance is important to HCA and define the people, processes, technologies and information assets covered by the policy. Particular attention should be given to the confidentiality, integrity and availability of sensitive healthcare and organisational information. The second component focuses on the identification and allocation of roles and responsibilities. The report should establish appropriate accountability for information governance and information security and consider responsibilities relating to legal, regulatory and contractual obligations. Relevant roles may include senior management, information security leadership, data protection personnel, information asset owners, information asset administrators, employees, contractors and third parties. The third component requires the development of an Information Governance Policy Framework and recommendations for a minimum of eight controls to establish an effective Information Security Management System. The assessment brief identifies ISO/IEC 27001:2022 as an appropriate framework within the developed policy and requires the framework and controls to be justified in relation to HCA's organisational context and security requirements. The fourth component focuses on implementation and monitoring. Students should develop an implementation plan explaining how the information governance policy and security controls can be introduced and maintained. Appropriate monitoring mechanisms should be considered to identify security threats, mitigate vulnerabilities, maintain accountability and support continuous improvement. Overall, the assessment requires a practical and critically justified approach to information governance within a healthcare environment. The report should demonstrate how an effective information governance policy can protect sensitive patient information, support regulatory compliance, establish accountability and strengthen HCA's ability to manage evolving cyber security threats.
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Global Business Strategy
2,500 words
Global Business Strategy: Strategic Analysis and Growth of a Global Company
This Global Business Strategy assessment is an individual e-portfolio that requires students to conduct a comprehensive strategic analysis of a selected global company facing a significant business challenge. The assessment focuses on the identification and analysis of a real organisational problem, evaluation of the global business environment, development of sustainable strategic options, the role of strategic leadership, and recommendations for future growth and competitiveness. Students are required to select a global company from any industry and investigate challenges such as loss of market position, difficulties in gaining or regaining market share, operational challenges, or declining revenues and profits. Relevant strategic theories, models and academic evidence should be used throughout the analysis. The first stage focuses on selecting the organisation, identifying its strategic purpose, vision, mission and objectives, and analysing the significant problem affecting the company. Students are expected to investigate both internal and external aspects of the global business environment and identify factors that may have contributed to the organisation's challenges. Appropriate theories and strategic models should be applied to provide a critical analysis of the business context. The second stage examines sustainable business strategies, diversification and competitive advantage. Students are required to conduct a resource audit and use an appropriate strategic model to assess how the organisation can sustain competitive advantage. The assessment also requires students to propose an appropriate mode and strategy for entering an international market in which the selected company does not currently operate, supported by relevant theories and models. The third stage addresses strategic change, leadership and governance. Students must demonstrate an understanding of effective leadership styles and their relevance to strategic change, good governance and ethical business practices. The role of ethics in international business should be examined using relevant practical examples and evidence. The fourth stage focuses on good strategy execution, evaluation, recommendations and conclusion. Students are expected to justify how the selected organisation can execute strategy effectively using available resources and process management tools that support continuous improvement. At least two strategic options should be considered, with attention given to their feasibility, potential impact, alignment with organisational goals and available resources, as well as the role of leadership and leadership styles. The assessment concludes with a summary report of approximately 1,000–1,500 words, synthesising the key learning and insights gained from the individual tasks. The overall report should be approximately 2,500 words, excluding references and appendices, and should follow the ULBS Harvard referencing style. Evidence and artefacts may include business-journal articles, professional or government reports, relevant videos, analytical tables, competitive-position graphs and financial or balanced-scorecard information. The completed e-portfolio is compiled in PebblePad and submitted as a PDF through Turnitin.
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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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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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Computer Science / Algorithms / Parallel Computing / Clustering
Parallel Algorithms for Hierarchical Clustering: Single-Link, Minimum Spanning Trees and Parallel Architectures
This research paper investigates parallel algorithms for hierarchical clustering, a clustering technique in which individual data points initially form separate clusters and the closest clusters are repeatedly merged until a hierarchical tree structure, or dendrogram, is formed. The paper reviews important sequential clustering algorithms, surveys previous parallel approaches and proposes parallel methods for several commonly used inter-cluster distance metrics. Prims_algorithm_for_hierarchica… The paper distinguishes between graph-based metrics and geometric metrics. Graph metrics include single-link, average-link and complete-link clustering, while geometric metrics include centroid, median and minimum-variance methods. It also discusses the Lance–Williams updating formula, which provides a general framework for updating inter-cluster distances after agglomeration. Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… A major focus is the relationship between single-link hierarchical clustering and the Euclidean minimum spanning tree. The paper explains that the cluster hierarchy for single-link clustering can be obtained from a minimum spanning tree, making minimum-spanning-tree algorithms highly relevant to efficient hierarchical clustering. It presents practical single-link algorithms with O(n²) time complexity and discusses space requirements and nearest-neighbour update properties. Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… The paper also examines algorithms for metrics satisfying the reducibility property, where nearest-neighbour chains can be used to efficiently determine which clusters to merge. Minimum-variance and graph-based metrics satisfy this property, while centroid and median metrics do not necessarily do so. Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… For more general clustering metrics, the paper describes priority-queue-based algorithms with O(n² log n) sequential time complexity. It then reviews previous parallel work, including parallel implementations of SLINK, Ward’s method and Prim’s minimum spanning tree algorithm. The cited parallel Prim implementation achieves O(n log n) time when sufficient processors are available. Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… The core contribution is a set of parallel algorithms for hierarchical clustering on PRAM, butterfly and tree architectures. For single-link clustering, the paper shows how a parallel minimum-spanning-tree approach can be used and reports an O(n log n) running time using n/log n processors. Similar optimal results are described for centroid, median and minimum-variance clustering, while average-link and complete-link methods are more difficult to optimise on local-memory architectures. Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… Overall, the paper demonstrates how hierarchical clustering can be accelerated through parallel computation while preserving the computational structure of different clustering metrics. Its main themes include minimum spanning trees, Prim’s algorithm, single-link clustering, nearest-neighbour methods, PRAM computation, parallel data structures and asymptotic complexity analysis. Prims_algorithm_for_hierarchica… Important: because this file is a journal research paper rather than a university assessment brief, fields such as module name, academic level, assignment type and formal word count do not genuinely apply.
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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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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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Entrepreneurship / Leadership and Management
3,500 words
Entrepreneurial Practice: Strategic Analysis, New Venture Development and Professional Reflection
This individual Entrepreneurial Practice assessment requires students to critically analyse a real organisational issue using one of the approved employer case studies and develop an entrepreneurial business proposal aligned with the organisation’s needs. The complete assessment is structured as a 3,500-word-equivalent portfolio consisting of a written report, briefing notes, a narrated PowerPoint presentation and a professional reflection. Entrepreneurial Practice Assign… Task 1 is a 1,500-word organisational analysis worth 30% of the marks. Students critically examine the challenges facing the selected organisation using appropriate strategic-analysis tools. They must also evaluate the organisation’s leadership models and communication strategies and assess their impact on employees, organisational culture and performance. The section concludes with three justified recommendations intended to improve organisational performance. Entrepreneurial Practice Assign… Task 2A consists of 1,000-word briefing notes focused on a proposed entrepreneurial venture. Students critically appraise the stages of entrepreneurial practice from idea generation through to delivery, including the benefits of the proposed venture and suitable funding sources. The task also requires analysis of business risk-management strategies and critical evaluation of the entrepreneurial traits, characteristics, skills and competencies needed to position the proposed venture strategically. Entrepreneurial Practice Assign… Task 2B converts the business proposal into a short narrated PowerPoint pitch. The presentation communicates the rationale and organisational benefits of the business idea, funding opportunities, key risks and mitigation approaches, and the entrepreneurial competencies needed for successful implementation. The intended audience includes employees, managers, senior management and the Board of Directors, so professional communication and persuasive presentation are important. Entrepreneurial Practice Assign… Task 3 is a 500-word personal and professional reflection based on an area of the CMI Code of Conduct and Practice. Students may use reflective frameworks such as Gibbs, Kolb, Rolfe or Burton and explain how the selected professional principle applies to their current or future career. Entrepreneurial Practice Assign… Overall, the assessment integrates strategic analysis, leadership, entrepreneurship, venture development, funding, risk management, professional communication and reflective practice. Overview word count: approximately 360 words. AI-use note: the assignment is classified as AI Amber. AI may be used only within the permitted support categories, and students must disclose which AI tools were used and briefly explain how they were used. Entrepreneurial Practice Assign… Important for your public Reference Library: the brief explicitly states that the document and its case-study materials must not be passed to third parties or posted on any website or social-media platform. Therefore, do not upload this assessment brief itself publicly. Only publish the finished student work if you have the right to do so and it does not reproduce restricted case-study material. Entrepreneurial Practice Assign… Entrepreneurial Practice Assign…
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Business Consultancy Project: Strategic Analysis, Stakeholder Evaluation and Evidence-Based Recommendations
Business Consultancy, Strategic Management, Business Analysis, Consultancy Project, Stakeholder Analysis, Mendelow Matrix, Business Strategy, Data Analysis, Secondary Research, SWOT Analysis, PESTLE Analysis, Porter’s Five Forces, Balanced Scorecard, Ethics, Sustainability, UN Sustainable Development Goals, Recommendations, Risk Analysis, Implementation Barriers, Organisational Strategy, Business Problem Solving, Employability Skills, Professional Development, Reflective Practice, Project Management, Evidence-Based Decision-Making
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Computer Science / Algorithms / Network Optimisation
An Improved Dijkstra’s Shortest Path Algorithm for Sparse Networks
This technical research paper investigates an improved version of Dijkstra’s shortest path algorithm for sparse weighted networks. Traditional implementations of Dijkstra’s algorithm can achieve a time complexity of O(m + n log n) when Fibonacci heaps are used, but the authors argue that heap construction increases implementation complexity. The proposed approach modifies the original algorithm so that heap construction is avoided while maintaining competitive performance on sparse graphs. An_improved_Dijkstra’s_shortest… The study focuses on the single-source shortest path problem in weighted directed graphs with non-negative edge lengths. It begins by reviewing a refined Dijkstra algorithm in which each vertex maintains a distance label representing an upper bound on the shortest distance from the source vertex. The main computational bottleneck is repeatedly identifying the unvisited vertex with the smallest distance label. A naïve implementation requires O(n²) time, while Fibonacci-heap implementations improve efficiency but introduce additional implementation complexity. An_improved_Dijkstra’s_shortest… An_improved_Dijkstra’s_shortest… The authors propose an improved Dijkstra algorithm that maintains distance labels in an ordered list. When a distance label changes, the corresponding entry is reinserted into the appropriate location rather than rebuilding or maintaining a heap. The algorithm exploits the characteristics of sparse networks, where each vertex is connected to only a relatively small number of edges. This is particularly relevant to road networks, where the maximum degree of each node is typically low. An_improved_Dijkstra’s_shortest… To support efficient reinsertion, the paper introduces a predefined step-size vector and a binary-search-style process for locating the correct insertion position. This approach reduces the number of comparisons required while avoiding the division operations commonly associated with standard binary search implementations. An_improved_Dijkstra’s_shortest… An_improved_Dijkstra’s_shortest… The theoretical analysis shows that the proposed method requires approximately O(m + Dmax log(n!)) comparisons and arithmetic operations, where m represents the number of edges and Dmax is the maximum number of edges incident on a vertex. The authors argue that this complexity makes the approach especially suitable for large-scale sparse networks where the maximum node degree remains relatively small. An_improved_Dijkstra’s_shortest… An_improved_Dijkstra’s_shortest… The algorithm is evaluated through numerical experiments implemented in MATLAB. Two families of randomly generated sparse networks are tested, with network sizes ranging from approximately 10,000 to 21,000 nodes. The first experiment uses a maximum node degree of four, while the second uses a maximum degree of six. Experimental ratios reported in the paper remain close to the theoretical complexity estimate as network size increases. An_improved_Dijkstra’s_shortest… The paper concludes that the improved Dijkstra approach is practical for large sparse networks, particularly road-traffic networks. By avoiding Fibonacci-heap construction and using an ordered-list reinsertion strategy, the algorithm aims to simplify implementation while maintaining competitive computational performance for shortest-path calculations. An_improved_Dijkstra’s_shortest… Important: because this is a published journal article rather than a university assignment brief, fields such as module name, assessment level and assignment word count are not stated in the source. For the portal, it is safer to use Not specified / Not applicable for those fields rather than inventing academic-assessment details.
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Web Applications / Artificial Intelligence / Software Development
Smart Clinic Appointment and Patient Management System with AI-Based Demand Prediction
This Web Applications and AI coursework requires students to design, implement and evaluate a Smart Clinic Appointment & Patient Management System for a small healthcare clinic. The application combines conventional web-development functionality with an artificial-intelligence component for predicting appointment demand. The system is expected to use Java EE technologies, including Java Servlets, JSP, Web Services and JDBC, together with a relational database such as MySQL or PostgreSQL. f3855340dabd17407efd386c38cfdc3… The patient-facing side of the application should allow users to browse and search clinic services by department or specialty, price, availability and duration. Patients must be able to view detailed service information, select a clinician where appropriate, choose an available date and time, enter their details and confirm an appointment. The system should also provide a booking reference and basic appointment-history functionality. f3855340dabd17407efd386c38cfdc3… The administrative interface focuses on operational management. Staff should be able to add, update and remove services, configure consultation duration and pricing, manage clinician working hours and appointment-slot availability, and generate basic reports. f3855340dabd17407efd386c38cfdc3… A separate machine-learning component requires students to implement appointment-demand prediction using WEKA regression embedded in Java. The provided sample dataset contains Year, Month, Promotions Cost and Booking Requests. Students must expand this dataset to at least 60 realistic rows, including seasonal changes and plausible variation in marketing expenditure and demand. A regression model is then trained to predict booking requests for the following year based on promotional spending, including estimation of future demand if promotions expenditure increases by 10%. f3855340dabd17407efd386c38cfdc3… The assessment also requires evidence of professional software-development practice. Students must provide application-design artefacts such as design patterns, ER diagrams, wireframes and sketches, document the development process, demonstrate correct use of JSP, Servlets, Web Services and JDBC, and provide evidence of implementation through code, database content and screenshots. Regular GitHub commits are required to demonstrate ongoing development. f3855340dabd17407efd386c38cfdc3… f3855340dabd17407efd386c38cfdc3… The final submission includes a DOCX or PDF report containing system-design and implementation information, links to a private GitHub repository and a demonstration video of no more than five minutes. The assessment is classified as Green for AI use, meaning AI tools may support tasks such as generating example datasets, suggesting code snippets and brainstorming features or tests, provided their use is clearly declared in the report. f3855340dabd17407efd386c38cfdc3… Overall, the coursework integrates full-stack Java web development, relational database design, web services, software engineering and machine-learning regression within a healthcare appointment-management scenario. Important: the uploaded brief states that it is for Coventry University Group students' own use and must not be passed to third parties or posted publicly. f3855340dabd17407efd386c38cfdc3… So for a public Reference Library, use an original summary like the one above rather than publishing the original brief itself.
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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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3,000 words
ParkaLot Enterprise Parking Garage Management System: Software Analysis, Design and Prototype Development
This enterprise software engineering project requires students to analyse, design and prototype a centralised parking garage management system for ParkaLot Group, a UK operator of multi-storey parking facilities. The existing organisation relies heavily on manual processes, local spreadsheets and simple barrier-based vehicle counts, resulting in limited real-time occupancy information, inconsistent reservation arrangements, decentralised billing and reduced ability to optimise parking capacity and revenue. COMP1471 CW 2526 (1) The proposed system supports ParkaLot’s wider digital transformation by integrating customer registration, reservations, parking-space allocation, occupancy monitoring, contracts and billing. Customers can check availability and reserve parking through an online portal, while frequent and corporate users can establish recurring arrangements or block reservations. License-plate recognition and individual parking-space sensors enable automated access control and real-time occupancy tracking. COMP1471 CW 2526 (1) Additional functionality includes centralised electronic billing, dynamic pricing, promotional schemes and predictive decision-making for controlled overbooking. Historical usage data may be analysed to estimate no-shows, early departures and overstays, while operational dashboards support staffing, pricing and capacity-management decisions across the garage network. COMP1471 CW 2526 (1) Development is undertaken in two phases. The first uses structured analysis and design, requiring an Entity Relationship Diagram, Data Flow Diagram including a context diagram, and implementation of a prototype database. The second expands the solution using object-oriented analysis and UML, with emphasis on adaptable and reusable software design. COMP1471 CW 2526 (1) The final report covers the software-engineering **5 Ps—Problem, Process, Project, Product and People—**alongside ERD and DFD models, UML use cases, at least three sequence diagrams, a detailed class diagram and application of design patterns such as GRASP. Students must also submit prototype evidence, source code, personal reflection, peer assessment and work-contribution documentation. COMP1471 CW 2526 (1) Overall, the assessment integrates requirements engineering, structured modelling, object-oriented design, database development, design patterns, software project management, implementation and acceptance testing within a realistic enterprise-system case study. Overview word count: approximately 360 words. AI-use note: the brief permits Levels 1–4 of generative-AI use, including research and exploration at Level 4, but all final submitted text, code, diagrams and designs must be the students’ own work. Level 4 use requires disclosure, an appendix of prompts/outputs and reflective commentary. COMP1471 CW 2526 (1)
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Project Management / Professional Development / Business Strategy
5,000 words
Professional Project Portfolio and Strategic Business Presentation
This Level 7 assessment requires students to compile a comprehensive Portfolio of Evidence demonstrating their project contribution, reflective learning, professional skills and career development. The portfolio has a maximum length of 5,000 words and is designed to evidence both technical or disciplinary project engagement and the student’s development as a reflective professional. The assessment is organised into three main components: a Discipline Report, Project Work Logbook, and Career Development Plan. Assessment Overview The Discipline Report, worth 40% of the portfolio, provides an overview of the project and its conclusions, alongside a self-reflection on the student’s project experience. Students also complete a personal skills analysis and provide a confidential summary of their team’s delivery. This section therefore combines project understanding with critical self-evaluation, skills awareness and evaluation of collaborative performance. Assessment Overview The Project Work Logbook, worth 30%, records the student’s activities on a weekly basis. Students summarise the work completed each week and document problems or challenges encountered during the project together with the approaches used to resolve them. The marking criteria emphasise consistency, level of detail, organisation and the ability to identify and address project-related difficulties. Assessment Overview Assessment Overview The Career Development Plan, also worth 30%, requires students to define a realistic future career pathway, identify relevant jobs and organisations, and evaluate the skills and knowledge needed to progress toward those goals. Students must also complete an additional professional-development course, such as CPD or LinkedIn Learning, to demonstrate practical upskilling and continued professional development. Assessment Overview A related team presentation forms a second assessment activity. Teams of four to five students must present face to face for approximately 10–15 minutes to a small management-board audience. All team members are expected to contribute approximately equal content and speaking time, with provision for questions and answers. The presentation focuses on the team’s overall findings, analysis of the business problem, practical recommendations, proposed implementation approach and final conclusion. assessment Overview 1 Strong presentations are expected to be engaging, professionally structured and supported by relevant evidence, graphics, tables and other appropriate visual material. Higher-level work should move beyond description by applying theory critically to a real-world business problem, recognising the limitations and contextual applicability of theoretical concepts and producing recommendations that are appropriate to the organisation being analysed. assessment Overview 1 Overall, the assessment develops and evaluates project reflection, professional communication, teamwork, career planning, critical analysis, employability, problem solving and evidence-based business recommendation skills. It combines an individual reflective portfolio with a collaborative management-style presentation, allowing students to demonstrate both personal development and the ability to communicate project findings professionally.
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Big Data Analytics / Machine Learning
3,000 words
Machine Learning on Big Data Using PySpark: Large-Scale Data Analysis and Predictive Modelling
This group-based Machine Learning on Big Data project requires students to apply machine learning techniques to a large real-world dataset using PySpark DataFrames and Spark machine-learning libraries. Students select a substantial dataset, ideally between approximately 300 MB and 1 GB, from sources such as Kaggle, workplace data or other valid repositories, and develop an end-to-end big-data analytics workflow. CN7030 CRWK 26T1 The project begins with data loading and preprocessing using PySpark. Students are expected to handle missing values, perform data normalisation and feature engineering, identify class imbalance and propose appropriate mitigation strategies. Where text datasets are selected, additional preprocessing may include stemming, lemmatization and TF-IDF representation. CN7030 CRWK 26T1 The modelling stage requires implementation of an appropriate machine-learning approach using PySpark MLlib or Spark ML. The brief expects a multiclass rather than binary classification problem and allows techniques including multiclass classification, ensemble learning, clustering and text mining. Students must justify their model choice and consider model robustness, bias and variance when attempting to improve predictive performance. CN7030 CRWK 26T1 Students then perform hyperparameter tuning using techniques such as grid search or random search and evaluate the resulting model with appropriate measures. Relevant evaluation outputs may include accuracy, F1-score, precision, recall and a confusion matrix. Results should also be visualised or clearly presented and interpreted to identify meaningful patterns and performance characteristics. CN7030 CRWK 26T1 The project additionally requires consideration of Legal, Social, Ethical and Professional (LSEP) issues. Students discuss potential ethical concerns associated with their dataset, including bias and privacy risks, and propose suitable mitigation strategies. The final work is consolidated into a single user-friendly HTML analytics report that clearly presents the group's preprocessing, modelling, optimisation, evaluation and interpretation. CN7030 CRWK 26T1 CN7030 CRWK 26T1 Overview word count: approximately 335 words. If you are also uploading the presentation separately to the Reference Library, that should be a second entry under “Presentations and Academic Posters”, because the presentation forms a distinct 40% component and assesses understanding of Spark, preprocessing, modelling, optimisation, evaluation and responses to examiner questions. CN7030 CRWK 26T1
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Software Engineering / Enterprise Systems Development
3,000 words
Enterprise Software Engineering Development: ParkaLot Parking Management System Analysis, Design and Prototype
This Enterprise Software Engineering Development assessment is based on the ParkaLot Group, a fictional operator of multi-storey parking garages across major UK cities. The organisation currently relies on fragmented manual processes, basic barrier-based vehicle counting, local spreadsheets, on-site payment and inconsistent customer access arrangements. Students act as software engineering consultants and are required to analyse these operational weaknesses and design a centralised enterprise parking management system capable of supporting reservations, customer accounts, vehicle identification, billing, real-time occupancy monitoring, dynamic pricing and management reporting. COMP1471 CW 2526 The proposed ParkaLot platform is intended to integrate customers, parking spaces, reservations and billing across the garage network. Planned functionality includes online registration and reservation, recurring and corporate parking arrangements, licence-plate recognition, automated space allocation, sensor-based occupancy tracking, centralised monthly billing, electronic payments, promotional pricing and predictive overbooking. The system must also provide dashboards and historical reporting to assist management with capacity planning, staffing, pricing and operational decision-making. COMP1471 CW 2526 The coursework is completed in two main development phases. Phase 1 – Structured Analysis and Design requires an Entity Relationship Diagram representing the logical data model, a Data Flow Diagram including a Level 0 context diagram, and implementation of a prototype database. Phase 2 – Object-Oriented Development extends the system using object-oriented analysis, design principles and UML, with substantial business and user-interface functionality implemented using suitable OO technologies. COMP1471 CW 2526 The final report contains analysis of the 5 Ps of software engineering: Problem, Process, Project, Product and People. Students discuss the business problem and commercial risks, justify the development methodology followed, define resources and budget, document project artefacts and requirements, and identify the main stakeholders involved in the project. The technical design section includes the ERD, DFD, UML use-case model, at least three sequence diagrams, a detailed class diagram and discussion of design patterns. COMP1471 CW 2526 Students must additionally submit a functioning prototype that reflects the design and participate in acceptance testing and a live demonstration. Individual students are questioned on both theoretical and technical aspects of the submitted system. The assessment also evaluates group contribution, peer and self-assessment, personal reflection, research quality, communication and professional teamwork. COMP1471 CW 2526 The weighting places substantial emphasis on technical design and implementation: the UML design is worth 24 marks, the software prototype 15 marks, design patterns 6 marks, and acceptance testing/demonstration 25 marks. This makes the coursework strongly focused on demonstrating the relationship between requirements analysis, software architecture, UML modelling, implementation quality and working enterprise-system functionality. COMP1471 CW 2526
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Data Science / Data Management / Professional Practice
4,500 words
Tesla Data Science Professional Case Study: Data Management, Leadership, Entrepreneurship and Ethics
This composite case-study assessment for The Data Science Professional module requires students to critically analyse Tesla from several interconnected professional perspectives, including data management, artificial intelligence ethics, leadership, organisational development, entrepreneurship and business risk. The coursework is designed to combine technical data-science capability with strategic, ethical and managerial decision-making. a82a1aa6a2e7a3268ad021c4f5b3d44… Part A – Database Design and Distributed Frameworks focuses on data management. Students design an Entity-Relationship model and relational schema for a Tesla-related vehicle-hire business, identifying entities, relationships, cardinalities, identifiers, primary keys and foreign keys. The accompanying guidance specifies entities relating to vehicles, employees, outlets, clients, hire agreements, insurance, faults and employment records. a82a1aa6a2e7a3268ad021c4f5b3d44… 39cdc24cc46a13ddf444b8e7af838eb… The SQL component uses an Online Music database to examine Tesla customer preferences. Students create relational tables using Oracle standard SQL and write queries involving users, music, publishers, categories and download records. Evidence of implementation and query results must be provided using Oracle Live SQL. 39cdc24cc46a13ddf444b8e7af838eb… Part A also requires a critical assessment of the security, privacy and ethical implications of Tesla’s Full Self-Driving technology, connecting technical development with responsible data and AI practice. a82a1aa6a2e7a3268ad021c4f5b3d44… Part B – Leadership and Developing People requires critical evaluation of Tesla’s leadership model, organisational culture and their effects on employees and organisational performance. Students must propose a leadership and people-development strategy capable of supporting the organisation as it expands. a82a1aa6a2e7a3268ad021c4f5b3d44… Part C – Entrepreneurial Practice and Managing Risk examines a proposed Tesla spin-out venture developing innovative low-cost green hydrogen production systems. Students critically assess management support for the venture, propose an evidence-based approach to entrepreneurial risk, develop an entrepreneurial leadership role descriptor, and evaluate how GDPR and AI/data ethics may support or constrain entrepreneurial practice. a82a1aa6a2e7a3268ad021c4f5b3d44… Overall, the assessment integrates technical database design, SQL, data ethics, organisational leadership, entrepreneurship, sustainability and professional decision-making within a single Tesla-focused case study. Overview word count: approximately 360 words. Important: the assessment brief itself explicitly states that it must not be passed to third parties or posted on any website. So for a public Reference Library, use the metadata and your own finished work where permitted, but do not upload the assessment brief/guidance PDFs themselves publicly. a82a1aa6a2e7a3268ad021c4f5b3d44… The AI status is Amber: generative AI may be used for limited inspiring/planning purposes, but usage must be acknowledged with the tool, prompts and relevant evidence; the brief also specifically prohibits using LLMs to generate the Part A(3) essay. a82a1aa6a2e7a3268ad021c4f5b3d44… a82a1aa6a2e7a3268ad021c4f5b3d44…
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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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Computer Networks / Network Security / Cloud and Software Defined Networking
3,500 words
Network Systems and Security: Ad Hoc, Cloud and Software Defined Networking Emulation
This Network Systems and Security coursework requires students to design, implement and critically evaluate a series of practical network-emulation environments covering wireless Ad Hoc networking, cloud services and Software Defined Networking (SDN). The project combines Python-based network configuration with practical connectivity testing, cloud deployment, controller-based networking and theoretical evaluation of contemporary network-security technologies. The first task involves designing an Ad Hoc wireless network representing an emergency-response scenario. Students configure at least three wireless stations using Mininet-WiFi, assign appropriate network parameters and demonstrate connectivity through ICMP communication. The task requires discussion of the design and implementation together with the Python script used to configure the emulated environment. 7COM1076+ref+def+CW+2025+26+ The second task focuses on cloud-service emulation. Students develop a simple static website, deploy it through Render.com and GitHub, and access the hosted service from a Mininet-emulated network. Required evidence includes the Python implementation, cloud configuration screenshots, commands used to provide internet connectivity, webpage access through an xterm environment and the associated HTML code. 7COM1076+ref+def+CW+2025+26+ The third practical component examines Software Defined Networking using the ONOS controller. Students construct an emulated topology containing hosts, servers and programmable switches, demonstrate complete ICMP connectivity and perform a TCP transmission lasting 600 seconds. Evidence must include the network-emulation script, ONOS graphical interface and connectivity results. 7COM1076+ref+def+CW+2025+26+ The final analytical section critically evaluates whether Software Defined Networking and Network Functions Virtualisation (NFV) complement one another and examines security algorithms used within cloud computing. Students compare two selected cryptographic approaches, evaluating their respective advantages and disadvantages. 7COM1076+ref+def+CW+2025+26+ Overall, the coursework integrates network modelling, wireless networking, cloud deployment, SDN control, Python scripting, connectivity testing and security analysis. The marking scheme gives substantial weight to system modelling, cloud and SDN implementation, ICMP/TCP functionality, technical analysis and overall report quality. 7COM1076+ref+def+CW+2025+26+
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Business Consultancy / Digital Marketing / Social Media Marketing
3,000 words
You Keep Me Sane: Social Media Growth and Digital Product Marketing Live Business Project
his Extended Work Project is a live business consultancy assignment completed for You Keep Me Sane, a podcast and social-media brand seeking to expand its online audience and increase sales of its digital products. The organisation promotes its podcast across platforms including Instagram, Facebook, TikTok, YouTube and LinkedIn and identified a need for additional support with regular content creation, social-media scheduling and digital-product promotion. Project Brief_You Keep Me Sane … The client challenge focuses on improving the organisation's social-media presence while reducing the workload associated with producing and publishing content. The project brief specifically identifies the need to create reels, carousels and other social-media posts, potentially using Canva, and to support an existing Buffer-based scheduling process. The organisation aims to publish approximately three times per week while also promoting new digital products. The intended commercial outcomes are growth in social-media following and increased digital-product sales. Project Brief_You Keep Me Sane … The academic assessment requires students to document the project journey in chronological order, from the initial client brief and early meetings through planning, research, idea development, feedback, implementation, challenges and final outcomes. The 3,000-word report should explain the business context, methods and frameworks used, significant milestones, problems encountered, adaptations made and recommendations provided to the client. EWP 5TH MAY 2026 (1) EWP 5TH MAY 2026 (1) Professional reflection is also an important element of the project. Students are expected to evaluate team learning, skills development, project-management experience and the practical lessons gained from working on a genuine business challenge. The report should demonstrate critical thinking and reflective analysis rather than simply describing activities completed. EWP 5TH MAY 2026 (1) The accompanying 10-slide presentation mirrors the report and communicates the project overview, organisational background, initial brief, research, development process, challenges, outcomes, recommendations and learning in a concise visual format. EWP 5TH MAY 2026 (1) Overall, the project integrates live business consultancy, social-media strategy, content development, digital-product marketing, client engagement, teamwork, project management and reflective professional learning, with emphasis on demonstrating practical value created for the client organisation.
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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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Information Visualisation / Data Analytics / Data Science
1,500 words
Information Visualisation Project Using Power BI and Python
Assignment overview — ready to paste This Information Visualisation project requires students to design, implement and critically evaluate effective data visualisations using two different technological approaches: Microsoft Power BI and Python. The assessment focuses on the practical application of information-visualisation principles, including data preparation, visual design, interaction, audience requirements and the extraction of meaningful patterns and insights from complex datasets. Assignment 002 Coursework 2025-… Task 1 focuses on interactive visualisation using Power BI. Students work with the UK Department for Transport's Road Safety Open Data (STATS19), which contains information relating to road traffic accidents, casualties, vehicles, locations, times and contributing factors. Students may analyse one year or multiple years of data depending on their visualisation objectives. Assignment 002 Coursework 2025-… The Power BI work requires students to identify an appropriate target audience and report type, select relevant variables, clean and transform the data, develop an appropriate data model and create analytical measures using Data Analysis Expressions (DAX). The resulting dashboard should communicate the context of the data and reveal meaningful trends, patterns and insights. Assignment 002 Coursework 2025-… Task 2 requires students to develop visualisations programmatically using Python, with Jupyter Notebook recommended as the implementation environment. Students independently select a real-world, publicly available dataset containing at least 10,000 observations and more than five variables. Unlike Task 1, visualisation tools that automatically construct visualisations, such as Tableau or Power BI, cannot be used for this component because programming is an explicit requirement. Assignment 002 Coursework 2025-… Assignment 002 Coursework 2025-… The submitted Jupyter Notebook should operate as an educational technical report explaining the selected dataset, preprocessing procedures, visualisation choices and resulting insights. Students are expected to justify their visualisation techniques, critically evaluate findings and discuss challenges encountered during development. The textual content of the notebook is limited to 1,500 words, excluding code and visualisations. Assignment 002 Coursework 2025-… The complete assessment contains several deliverables, including a maximum 6-minute Power BI demonstration video, a maximum 2-page Power BI report, the .pbix file, an 8-minute Python/Jupyter visualisation demonstration, the Jupyter Notebook, dataset and README file. All materials must ultimately be packaged into a single ZIP submission. Assignment 002 Coursework 2025-… Assignment 002 Coursework 2025-… Important: the brief does not specify a named referencing style or academic level, so I would select Not specified for those two portal fields rather than guessing. It also explicitly states that generative AI must not be used to create any part of the assessed submission, including code, debugging, writing, paraphrasing or bibliographies. Assignment 002 Coursework 2025-…
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Operating Systems and Networks
2,000 words
Security of Operating Systems and Networks – Individual Assignment
This individual assignment focuses on the security of operating systems and computer networks. Students are required to produce a professional technical report of approximately 2,000 words demonstrating a deep and systematic understanding of operating-system security, networking functions, security threats, vulnerabilities and practical security testing. The assessment carries 50% and forms 100% of the module assessment. Students are expected to support their work with appropriate technical evidence, images showing practical steps and relevant sources. The assignment uses a business scenario involving Net-Tech, a small and medium-sized technology-services enterprise. The organisation is concerned about the security of its proposed system, including operating-system attacks such as buffer overflow and network threats such as hacking and phishing. Students are required to investigate, design and experiment with the features and functions of a web server used to serve the company's website, while assessing the security landscape and presenting findings that can support appropriate organisational security decisions. As part of the practical work, students must create a prototype Net-Tech network test rig. This includes creating two users, with one configured as a superuser and another as a standard user, applying appropriate baseline security measures, installing suitable software and identifying vulnerabilities, using appropriate tools to conduct security tests, and writing scripts to automate repetitive tasks. Students must document assumptions and parameters used within the project, including further security implementations and recommendations such as the use of a suitable database for a database-driven website. The report is structured around introduction, background research, pre-engagement, engagement and post-engagement activities. The introduction should explain the business scenario, assumptions, aims, objectives, deliverables, available skills and resources, constraints and project plan. The background research should address common vulnerabilities, threats, risk models, security-testing approaches, relevant attack and testing tools, legal and organisational requirements, and ethical, social, professional and sustainability considerations. The pre-engagement section covers the test-rig setup and testing strategy. The engagement section requires practical comparison and demonstration of operating-system and network security, including user authentication, file and directory permissions, protection against stack-overflow attacks, network weaknesses, operating-system discovery, firewalls, listening ports, network statistics, prevention of denial-of-service attacks and scripting for automation. The post-engagement section requires a summary of the work, deductions and limitations, mitigation measures and recommendations, and personal reflection. The assessment requires students to use relevant sources, provide a bibliography and demonstrate appropriate analysis, evaluation and reflection. The assignment is designed to assess learning outcomes relating to knowledge of network security threats and the development of complex software and scripts relevant to operating systems and computer networks.
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Data Warehousing and Big Data
Data Warehousing and Big Data – Inventory Management Data Warehouse
This individual coursework for CS7079 Data Warehousing and Big Data requires students to design, implement and test a data warehouse based on a business case scenario and then export and migrate data to a Big Data platform for further processing. The assessment focuses on the inventory management business process of ABC Consumer Electronics Outlet Ltd, a multi-channel consumer electronics retailer operating from six stores around London and conducting online business across the UK and Europe. The company manages more than 10,000 products across approximately ten categories and more than 200 brands. The company already uses cloud-based and business applications including Vend, Linnworks and Xerox, which generate large volumes of transactional data. However, these datasets are stored separately by the individual applications, making it difficult for managers to produce integrated reports and perform analysis to support business decision-making. A business analyst has therefore recommended the development of a data warehouse capable of meeting the company's reporting and analytical requirements. For this coursework, the solution is focused specifically on the inventory management business process. The inventory management case involves three main business activities: sending purchase orders to suppliers when stock reaches minimum levels, receiving purchase orders and storing stock in appropriate locations, and controlling and maintaining stock levels including adding new products and adjusting existing stock. The required analysis includes daily stock levels for the previous month, weekly identification of products at minimum stock levels, analysis of stock levels by brand, product type and supplier, daily and weekly sent and received stock orders, and analysis of received stock orders by supplier and month. Students must analyse and design a dimensional data model that represents these business activities. This includes defining the grain of three central fact tables, identifying appropriate dimensions, defining dimension attributes and fact measures, and producing simple star schemas showing the relationships between fact tables and dimensions. The design must be justified according to the available data sources and the reporting and analysis requirements. The implementation stage requires students to create the relational database using Microsoft SQL Server Management Studio. Students must create the database, dimension tables and fact tables, establish appropriate primary-key and foreign-key constraints, and demonstrate implementation and testing through SQL commands and their results. Test data must then be populated into the data warehouse. The Big Data component requires migration of test data from the data warehouse to an Apache Hadoop platform using the Hortonworks Data Platform. Students must export the data warehouse data to an external data file, migrate the file into Apache HDFS, create a suitable data structure for loading the data into Hive, and demonstrate Apache Pig for manipulating the loaded data. Implementation and testing of the Big Data storage environment must also be demonstrated through commands and results. The coursework concludes with a personal reflective section in which students discuss what they have learned throughout the overall coursework and the challenges encountered during the process. The final submission is expected to be a well-written, structured and well-presented report combining data warehouse design, database implementation, testing, Big Data migration and reflective learning.
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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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Principles of Data Science
3,000 words
Principles of Data Science – Predictive Modelling and Data Analysis
This individual assessment for the Principles of Data Science module requires students to select, apply and critically evaluate data science methods, tools and techniques using one of three provided datasets and its associated scenario. The main assessment takes the form of a 3,000-word report in which students explore their chosen dataset, identify an appropriate predictive modelling approach, build and evaluate models, interpret the findings and critically reflect on the overall process and outcomes. The assessment addresses the principles and foundations of data science, statistical methods, data preparation, visualisation, predictive modelling, decision making and the critical evaluation of data science techniques and tools. Students begin by exploring the selected dataset to understand its structure, characteristics and limitations. Although the supplied datasets have already been cleaned, students may undertake additional data preparation or transformation where necessary. Any preprocessing decisions must be justified in relation to the requirements of the selected analytical methods. Feature selection should also be considered as part of preparing the data for model development. The assessment requires students to identify suitable forms of analysis for the selected scenario and justify their choice of methods. At least two different techniques must be used to develop models with predictive capacity for the response variable in the chosen dataset. The models must be trained and tested consistently, using the same training and test datasets so that their performance can be compared fairly. Where appropriate, students should also provide insight into feature importance and explain the contribution of relevant variables to predictive performance. Model performance must be evaluated using suitable metrics, followed by a clear description of the findings and recommendations appropriate for the intended audience. The report should document the complete analytical workflow, including data exploration, preprocessing, feature selection, model development, testing and evaluation. Students are expected to explain and justify the decisions made throughout the process rather than simply presenting code or model results. The assessment also requires students to demonstrate practical proficiency in data science tools and techniques. The brief expects the use of R for completing the assignment and requires evidence of important elements of the code, although the complete code does not need to be submitted. Data visualisation must be used to support the written discussion and communicate relevant findings effectively. The assessment is evaluated across theoretical knowledge and method selection, data exploration and processing, technical application and model evaluation, communication of findings, and overall presentation and referencing. The assessment therefore combines technical implementation with critical analysis, requiring students to explain why particular methods were selected, evaluate their effectiveness and consider the limitations and implications of the resulting findings. A separate second assessment component accompanies the written report. This component requires a presentation of the key findings from the written work using a maximum of five slides and a presentation duration of no more than seven minutes. It should summarise the dataset, methods, key findings and project outcomes while providing critical reflective commentary on lessons learned, factors affecting success and potential real-world applications.
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Programming for Data Science
2,500 words
Individual Coursework – Programming for Data Science
This Individual Coursework assignment for the Programming for Data Science module requires students to demonstrate practical programming skills and the ability to critically select and apply Python data science tools and libraries. The assessment is worth 20 credits and has a word count of 2,500 words plus 10%, excluding the reference list and output. Students are required to submit one clearly organised report covering two tasks and their individual subtasks. Task 1 focuses on designing, building, testing, explaining, adapting and critiquing a Python program. Students are required to implement a dice-based football match simulation for two players using at least two Python functions. The implementation should follow the logic of the physical dice game, demonstrate good Python coding style, and use functions designed with high cohesion and low coupling. The report must include the full Python code and carefully selected output from a sample match that demonstrates the progression of the game and changes in the score. Students must not implement a Python class or graphical visualisation. The second part of Task 1 requires students to explain how they designed their implementation, including assumptions, incremental development and testing. They must then modify their program to estimate at least two performance measures relevant to a football manager. The coursework asks students to investigate how these measures change when a restrictive shot clock is introduced and to model a realistic “Hail Mary” shot when the shot clock is close to expiring. Students must provide modified Python code, clearly identify the changes made, include selected output for checking the logic, and provide a robust conclusion based on comparison of the results. Task 2 focuses on critically assessing, selecting and applying Python data science libraries and algorithms. Students must describe an applied data science problem involving unstructured data such as image, audio, video or text. They must provide a specific example based on the context of a Coventry University fresher and explain how the problem could be solved manually. Students then select two Python libraries, justify their selection, compare their capabilities, apply both libraries to the chosen problem, and provide the relevant Python code and output. The final part of Task 2 requires a critical assessment of the selected Python libraries using the student's experience and additional sources. Factors may include coding difficulty, adaptability, level of control and quality of the resulting solution. Students must make a reasoned assessment of the suitability of the libraries for their chosen application and support their discussion with appropriate references. The assignment assesses three module learning outcomes: understanding essential programming concepts relevant to data science; designing, building, testing, explaining, adapting and critiquing small programs in a high-level programming language; and critically assessing, selecting and applying data science tools, libraries or algorithms for different applications and tasks. The submission must be provided as a single Microsoft Word or PDF report, organised by subtask, with each task starting on a new page. Python code, relevant output and plots must be included directly in the report. The brief requires APA referencing and states that sources should be cited in-text with a reference list for each task where relevant.
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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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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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Programming for Data Science
4,000 words
Programming for Data Science – Individual Portfolio
This individual portfolio assessment for the Programming for Data Science module at Coventry University consists of four tasks designed to assess programming, debugging, data science, data visualisation, data protection and data ethics skills. The assessment carries 20 credits and has a total value of 4,000 words equivalent, excluding the reference list and output. Students are required to submit one clearly organised report containing all four tasks, with each task beginning on a new page. Python code, outputs and relevant plots must be included directly within the report. Task 1 focuses on analysing, critiquing and debugging Python code. Students are required to identify syntax errors, logical errors, style and readability issues in a supplied program, determine what the program is intended to calculate, and make appropriate corrections. Students must test the program using varying values, explain the changes made, and improve its overall readability and annotation so that an unfamiliar user can understand it. The task also requires students to investigate computational efficiency by measuring execution time for different input limits and identifying more efficient coding or logical approaches. Task 2 requires students to design, build and test a simple Python implementation of the Blackjack card game. The program should simulate a single-player game against a computer-controlled dealer, allow the player to choose between hitting and standing, automatically simulate the dealer's turn, and offer the option to play additional rounds. Complex rules such as splitting and betting are excluded, and students must not implement a Python class or graphical visualisation. The submission must include the Python code and the output from five games, with sufficient storytelling in the output to allow the code to be tested from the results shown. Task 3 assesses the student's ability to critically assess, select and apply data science tools. Students work with animal lifespan information from the AnAge database and use Python, including pandas and appropriate graphical libraries, to explore and communicate insights. The task involves summarising the number of animal species represented within each animal Class and producing plots of maximum longevity against adult weight for the four Classes with the most represented species. Students must discuss whether smaller or larger animals live longer, identify extreme outliers, compare trends between animal groups and consider implications for ageing research. Task 4 examines data protection and data ethics using the Cancer Genome Atlas (TCGA) as a case study. Students must explain how a potential data breach could occur, identify the personal and sensitive information that could be compromised, and discuss consequences for patient confidentiality, institutional reputation, participation in future research and possible legal or public relations responses. A second part considers a hypothetical UK database and requires discussion of GDPR and the UK Government Data Ethics Framework, including informed consent, anonymisation, transparency, ethical governance, privacy and public trust in biomedical research. The assessment assesses two module learning outcomes. MLO2 focuses on designing, building, testing, adapting and critiquing small programs in a high-level programming language and is assessed through Tasks 1 and 2. MLO3 focuses on critically assessing, selecting and applying data science tools, libraries or algorithms throughout the data science project lifecycle and is assessed through Tasks 3 and 4. The assignment requires APA referencing and asks students to provide in-text citations and reference lists where relevant. The brief also classifies the assessment as “Amber” for Generative AI: AI tools may be used for inspiration but not to generate answers or analyse datasets. Any permitted use must be clearly acknowledged, documented and cited using APA style.
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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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Software Development Project
Software Development Project – Group Presentation, Demo Video and Peer Assessment
This group-based assessment for the Software Development Project module at Coventry University requires a team of five students to design, develop and demonstrate a full-stack software product of their choice. The project must use technologies or platforms agreed by the team and requires students to contribute to different aspects of the software development process, including design, implementation and testing. The assessment is worth 20 credits and is submitted as a 15-minute MP4 presentation and demonstration video, with a permitted variation of plus or minus 10%. The main component is a group presentation and demo video. Students must evaluate the key Agile techniques applied during development and project management, supported by evidence of using appropriate software tools. The presentation should demonstrate how Agile practices were applied to the group project and should address techniques such as user stories with story-point estimation, story mapping, sprint planning, task boards, work breakdown and burndown charts. The assessment also requires evaluation of project risks and the social, professional, legal and ethical issues associated with the software project. A second major component is the live demonstration of the software features developed by the group. Students must demonstrate the most significant or innovative features implemented using their selected technologies and platforms. The demonstration should show how the software meets business requirements and provide a clear rationale for the development decisions. The marking criteria assess the quality, complexity and creativity of the software, including front-end, computation and back-end development, live data inputs and, where applicable, Create, Read, Update and Delete (CRUD) operations. The assessment also includes an individual peer-assessment component. Each student must rate every other team member's contribution using a whole-number score from 0 to 10, with 10 representing the highest contribution. The peer assessment accounts for 10% of the overall assessment, while the group presentation accounts for 60% and the software features demonstration accounts for 30%. The module learning outcomes focus on evaluating and applying appropriate software development approaches such as Agile, applying current technologies and platforms to meet business requirements, evaluating software solutions against quality metrics, and evaluating commercial risks alongside professional, social, legal and ethical considerations. The brief also places the assessment in the Amber AI category, meaning AI may be used to assist, but any AI tools used during the research process must be acknowledged and relevant AI-generated information must be cited and referenced using Coventry University APA style.
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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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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
2,473 words
Strategic Evaluation of Hindustan Unilever Limited (HUL): Organisational Strategy and Sustainability
This 2,473-word MSc Management coursework for BPP University’s Organisational Strategy and Sustainability module examines the strategic, sustainability and leadership dimensions of Hindustan Unilever Limited (HUL). The report applies the module’s learning outcomes through four main areas: strategic environmental analysis, ethical and sustainable practice, evaluation of strategic options, and personal leadership and sustainability reflection. The assignment is structured around PESTEL and VRIO for strategic analysis, the Triple Bottom Line for sustainability evaluation, the Ansoff Matrix for strategic options, and Emotional Intelligence and Transformational Leadership for personal reflection. The first section evaluates HUL’s external and internal environment. PESTEL analysis considers political and legal requirements, economic pressures, social changes, technological developments and environmental risks. The report highlights issues including packaging regulation, extended producer responsibility, data privacy, commodity and energy volatility, rural–urban differences, AI-enabled operations, digital traceability, climate risk, water stress and packaging circularity. VRIO analysis then evaluates HUL’s purposeful brands and portfolio breadth, rural route-to-market, digital operations and analytics, and supplier development and sustainability governance as sources of competitive advantage. The second section assesses HUL’s ethical, sustainable and responsible practices through the Triple Bottom Line framework of People, Planet and Profit. The analysis considers workforce and community priorities, customer responsibility, renewable energy, emissions, water stewardship, waste and packaging, as well as productivity, efficiency, risk mitigation and sustainable growth. A specific packaging circularity transition is proposed, including design-for-recycling, post-consumer recycled content, lightweighting, refill and reuse pilots, collection partnerships and consumer education. The third section evaluates four strategic options using the Ansoff Matrix: urban refill and concentrate formats, rural assisted commerce through “Shakti 2.0”, an ingredient traceability platform, and precision revenue-growth management in quick-commerce. Each option is considered in relation to strategic fit, VRIO capabilities, risks, implementation actions, ownership and KPIs. The final section provides a first-person reflection on leadership and sustainability. It uses Goleman’s Emotional Intelligence and Transformational Leadership to examine learning from the strategic and sustainability analysis, including self-awareness, self-regulation, empathy, collaboration and evidence-based decision-making. It also presents a 90-day leadership development plan focused on decision briefs, stakeholder pre-mortems and a personal Triple Bottom Line checklist.
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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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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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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 / 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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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assessment requires students to develop a comprehensive analytical solution to a real-world healthcare prediction problem using the WiDS Datathon 2025 Health Outcomes Prediction Dataset. The dataset contains socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents, with the principal objective of developing predictive models for ADHD diagnosis. The assessment is designed to demonstrate the complete data-science lifecycle, from data preparation and exploratory analysis through predictive modelling, interpretation and evidence-based recommendations. Students begin by exploring the dataset's features, data types and distributions before addressing missing values, outliers and other inconsistencies. Appropriate feature engineering should be undertaken where necessary, followed by Exploratory Data Analysis (EDA) using relevant visualisations to identify relationships, patterns and correlations within the data. Students with limited computational resources may use a representative subset, provided that the sampling method preserves the integrity and distribution of the original dataset and is clearly justified. A major component of the assignment involves developing and comparing at least three classification models. Appropriate techniques may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Model performance should be evaluated using measures including accuracy, precision, recall, F1-score and ROC-AUC, allowing students to identify the strongest-performing model through systematic comparison. The assessment also requires model interpretation and explainability. Students should explain the results of the selected model and may apply techniques such as SHAP or LIME to investigate feature importance and individual predictions. A feature-importance visualisation must be produced, and the most influential variables should inform practical recommendations for healthcare professionals regarding the potential use of predictive modelling in supporting earlier ADHD diagnosis and intervention. Overall, the assignment integrates data cleaning, exploratory analytics, predictive modelling, model comparison, explainable AI and research-informed healthcare recommendations. Students must submit a comprehensive report of no more than 2,500 words, alongside a Jupyter Notebook containing the implementation and outputs. The report must use Harvard referencing, with appropriate academic research integrated into the analysis, recommendations and conclusion.
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Computing and Digital Technologies
3,000 words
Contemporary Computing and Digital Technologies: AI Agents Hackathon Reflective Report
This postgraduate reflective assessment forms part of the Contemporary Computing and Digital Technologies module and is based on experiential learning undertaken through an AI-focused hackathon. The hackathon theme, “AI Agents Unleashed – Building the Future of Automation,” requires MSc students from computing-related disciplines to collaborate on intelligent agent-based solutions capable of automating complex tasks, solving real-world problems and supporting human decision-making. The hackathon encourages students to investigate agent-based system design, intelligent automation and responsible AI development. Potential applications include autonomous cybersecurity monitoring, multi-agent systems for information gathering and decision support, automated data pipelines, intelligent software-development assistants, and conversational systems such as virtual tutors or career coaches. Students may use code-based approaches or platforms such as Flowise, Microsoft Power Automate and Make.com, while more advanced implementations can use technologies including LangChain, AutoGen, Python-based agent SDKs, APIs and large language models. The 3,000-word Individual Reflective Report, worth 70% of the module assessment, evaluates the student's learning and professional development arising from these experiential activities. The first component is a 2,000-word Portfolio of Evidence, requiring evidence-based reflection on participation in the hackathon. Students should evaluate their leadership and teamwork competencies using concrete evidence such as screenshots, code commits and feedback while identifying key lessons for personal and professional development. They must also consider how the experience applies to future research, career development or professional practice. The remaining 1,000 words comprise a Critical Self-Reflection examining the student's personal contribution and achievement of learning-contract goals. Students are expected to critically consider challenges encountered, how those challenges were addressed, lessons learned and their development as effective collaborative team members. Overall, the assessment integrates technical experimentation, reflective practice, teamwork, leadership, professional development and responsible use of emerging AI technologies, supported by a structured portfolio of evidence
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Business Intelligence / Data Analytics / Project Management
Business Intelligence and Data Analytics for Project Progress Evaluation
This assessment for the Data Analytics and Project Management for Business Intelligence module requires students to critically evaluate the progress of a live-style project through the application of business intelligence and data analytics techniques. Working as a group of Project Analysts, students select either a Convocation project or Concert project associated with Northumbria University London and prepare a professional presentation assessing its progress, stakeholders, deliverables and performance. The assessment begins with development of a clear project problem or opportunity statement, followed by identification and analysis of the key stakeholders and deliverables associated with the selected project. Students then use Business Intelligence and Data Analytics techniques to examine project progress and communicate findings through dashboards. The assessment permits the use of Microsoft Excel or other suitable software for dashboard development. A central part of the task is the creation and critical evaluation of a project dashboard. Students are expected to use realistic assumed or projected data where necessary, as the assessment is designed to test the ability to design, interpret and critically evaluate dashboards rather than the accuracy of real project data. The data should therefore be internally consistent, relevant to the selected project and capable of generating meaningful management insights. Students must also critically justify their chosen BI and analytics tools, explaining how the dashboard supports project monitoring and informed decision-making. The assessment concludes with recommendations for the successful implementation and use of a Business Intelligence or Data Analytics solution within the selected project. Academic references and relevant examples should be used to support the analysis. The marking criteria place the greatest emphasis on application of BI tools and techniques (30%), followed by stakeholders and deliverables (20%) and justification of BI tools (20%). Project and opportunity analysis, conclusion and recommendations, and presentation and referencing each contribute a further 10%. Higher-performing work is expected to demonstrate critical evaluation, meaningful dashboard insights, strong theoretical or industry justification and professionally presented recommendations. If the file you want to upload is the resubmission report instead Use the same University, Subject and Module Name, but change these fields: Title: Business Intelligence and Project Analytics: Individual Critical Analysis of Project Performance Assignment type: MS Technical and Scientific Writing Word count: 1,500–2,000 words Key topics: Business Intelligence, Data Analytics, Project Management, Stakeholder Analysis, BI Tools, Dashboard Evaluation, Project Opportunity Analysis, Project Deliverables, Critical Analysis, Project Recommendations The resubmission is an individual written report in which the student selects only one area from the original project—Project & Opportunity Analysis, Stakeholders & Deliverables, Application of BI Tools & Techniques, Justification of BI Tools, or Conclusion & Recommendations—and develops it in depth. It should use the same Convocation or Concert project context while demonstrating independent critical analysis, reflection and application of theory.
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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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Cloud Computing / Big Data Technologies / Cyber Security
2,500 words
Cloud and Big Data Security Application: Design, Implementation and Evaluation
This assessment for the Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud-based or distributed data application. The project focuses on practical solutions involving the complex transformation, processing, storage and security of big data within cloud environments. Students are expected to demonstrate how distributed data can be organised in the cloud, how data pipelines can be used to access or process distributed databases, and how appropriate security controls can be incorporated into the resulting architecture. Students have considerable freedom when selecting their application. Suggested project directions include developing a data-science solution using SQL or MongoDB with cloud storage and an appropriate security policy; implementing privacy-preserving distributed processing using techniques such as Differential Privacy; creating multi-party authentication and group-based access-control mechanisms; or designing Multi-Level Security, Attribute-Based Encryption or Role-Based Access Control solutions. Projects may also examine distributed or cloud applications using security protocols such as SSH, SSL or IPsec. Creativity and originality are explicitly encouraged. The written component is a Design and Implementation Document of no more than approximately 2,500 words. It should present the project aims and objectives, application concept, cloud and security technologies, functional and security requirements, architecture and design decisions, protocols, access-control mechanisms, implementation process, achievements, problems encountered and overall evaluation. Relevant diagrams, such as interaction or sequence diagrams, may be used to explain system behaviour and architecture. The assessment also requires submission of the functioning Cloud and Big Data Security application and a 7-minute highlight demonstration video. The video should demonstrate the application's major features, implementation details, security functionality and, where appropriate, attack scenarios. Assessment places strong emphasis on the quality of the design and implementation documentation, originality, use of advanced features, and the overall effort and technical quality of the completed application. Students are therefore expected to demonstrate independent development rather than simply reproduce an existing tutorial.
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Computer Networks and Network Engineering
3,500 words
Wireless, Cloud and Software Defined Networking: Network Modelling, Emulation and Evaluation
This technical networking assignment focuses on the design, implementation, emulation and evaluation of wireless, cloud and Software Defined Networking environments. It combines practical network modelling with analytical discussion and requires students to demonstrate their understanding of modern networking architectures through Mininet-WiFi, Mininet, cloud deployment technologies and an ONOS Software Defined Networking controller. The first task involves creating an ad-hoc wireless network for an emergency-response scenario. A minimum of three wireless stations must be configured using specified parameters such as transmission range, antenna height, antenna gain, SSID and wireless capabilities. Students are required to explain the network design and implementation, provide the Python configuration script used with Mininet-WiFi, and demonstrate connectivity using ICMP communication between appropriate stations. The second task examines cloud-service emulation. Students must construct a Mininet topology containing a switch and two hosts and deploy a simple static website using Render.com. The task requires evidence of cloud configuration, GitHub repository integration, commands used to provide internet access to the emulated host, webpage access through Xterm, screenshots of the resulting webpage and the associated HTML code. The third task addresses Software Defined Networking (SDN). Students create an emulated environment involving three hosts and three servers, implement the required network topology and use the ONOS controller to provide control-plane programmability. Evidence must include the Python emulation script, ONOS GUI output, host-to-server connectivity testing and TCP transmission testing. The final analytical component requires students to critically evaluate the relationship between Software Defined Networking and Network Functions Virtualisation (NFV) and assess security algorithms used in cloud computing, including a comparison of the advantages and disadvantages of two selected algorithms. The coursework therefore integrates practical network configuration, connectivity testing, cloud deployment, programmable networking and academically referenced technical evaluation. Overview word count: approximately 335 words. Important: the brief explicitly states that AI-generated report or code content is prohibited, so this Reference Library description should be treated only as catalogue/metadata content, not as coursework material for submission.
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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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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assignment focuses on developing a comprehensive analytical solution to a real-world healthcare prediction problem. Using the WiDS Datathon 2025 Health Outcomes Prediction Dataset, students are required to analyse complex and high-dimensional healthcare data containing socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents. The principal predictive objective is to determine ADHD diagnosis from the available features. Students may use a representative subset of the dataset where computational resources are limited, provided that the sampling approach maintains the integrity and distribution of the original data and is appropriately justified. The assessment requires a complete data-science workflow beginning with data understanding and preprocessing. Students investigate the dataset's features, data types and distributions before addressing missing values, outliers and inconsistencies. Appropriate feature engineering should then be undertaken where it can improve the predictive capability of the models. Exploratory Data Analysis is used to identify important patterns, relationships and correlations, supported by relevant visualisations that communicate meaningful insights. A major component of the work involves the development and comparison of at least three classification models for predicting ADHD diagnosis. Suitable approaches may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Models are evaluated using performance measures including accuracy, precision, recall, F1-score and ROC-AUC, after which the most effective model is selected based on the evidence obtained. The assessment also places substantial emphasis on model interpretation and explainability. Students must interpret the selected model and may use approaches such as SHAP or LIME to explain feature importance and individual predictions. A feature-importance visualisation is required, and the most influential variables should inform practical recommendations. The final section translates analytical findings into recommendations for healthcare professionals, considering how predictive modelling could assist early ADHD diagnosis and intervention. Research literature must be integrated into the recommendations and conclusion. The assessment therefore combines preprocessing, exploratory analysis, predictive modelling, explainable AI and evidence-based healthcare decision-making within a single applied data-science project. The required report is a maximum of 2,500 words, with code, supplementary charts and tables permitted in appendices. A Jupyter Notebook containing the implementation and outputs is also required. Harvard referencing must be used throughout.
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Business Management / Business Consultancy / Supply Chain Management
5,000 words
Digital Transformation for Sustainable Supply Chain Transparency at Unilever: Blockchain, IoT and AI
This MSc Business Consultancy Project requires students to undertake an evidence-based consultancy investigation addressing a strategically significant business problem within a selected organisation. The assessment is designed to replicate professional consultancy practice by requiring students to define a focused organisational challenge, critically analyse secondary evidence, apply relevant management frameworks, evaluate stakeholder implications and develop practical recommendations that create value for the client organisation. The final submission is a 5,000-word consultancy report, including a 500-word employability reflection. The reference project examines Unilever Plc and focuses on the challenge of improving transparency and traceability across its complex global supply chain. Particular attention is given to the potential application of Blockchain, Internet of Things (IoT) and Artificial Intelligence (AI) to support real-time traceability, predictive analytics, ethical sourcing, operational efficiency and sustainability performance. The project considers how digital transformation could support Unilever's sustainability objectives while responding to growing regulatory, environmental and stakeholder pressures. The consultancy report requires a structured analysis consisting of an executive summary, introduction, company/client overview, clearly defined business problem and consultancy focus, and detailed stakeholder analysis. Students then undertake an extensive data analysis and framework application section using two or three relevant theoretical models alongside credible secondary evidence, industry reports, company data, tables, charts or Excel outputs. Findings should be interpreted critically and linked back to appropriate strategic or management frameworks while incorporating ethical and sustainability considerations. The project concludes with three prioritised, actionable and evidence-based recommendations, including consideration of implementation risks, barriers and anticipated benefits. Students must also critically reflect on the employability skills developed through the consultancy project, including research, analysis, problem-solving, project management, communication, professional behaviour, ethical awareness and future career development. All academic and professional evidence must be cited using the Harvard Referencing System, with emphasis on credible and current sources.
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Business Intelligence / Data Analytics / Project Management
Business Intelligence and Data Analytics Dashboard for Project Progress Evaluation
This group assessment requires students to act as Project Analysts and critically evaluate the progress of a university project using business intelligence, data analytics and project management techniques. The scenario is based on Northumbria University London planning two major events: a Convocation and an associated Concert. Students must select one of these projects and assess its progress through a professionally structured presentation. The assessment requires students to begin by defining the main problem or opportunity associated with the selected project. They must then identify and analyse the project's key stakeholders and deliverables. A major component of the work involves evaluating project progress using suitable business intelligence and data analytics techniques and developing an appropriate dashboard, which may be created using Microsoft Excel or other suitable software. Students must not only present the dashboard but also critically analyse and justify its design, selected metrics, analytical methods and usefulness for project monitoring and decision-making. Because the projects are treated as already being in progress, students are permitted to create assumed or projected data to demonstrate their dashboards. The assessment does not primarily evaluate the accuracy of real project data; instead, it assesses students' ability to design, present and critically evaluate meaningful dashboards. Any assumed data should therefore remain realistic, internally consistent and relevant to the selected project. The presentation should conclude with evidence-based recommendations for the successful implementation and use of the proposed business intelligence or data analytics solution. Academic references and relevant examples must support the presentation, and a single reference list must be included. Assessment weighting places particular emphasis on application of BI tools and techniques (30%), followed by stakeholders and deliverables (20%) and justification of BI tools (20%). Project and opportunity analysis, conclusions and recommendations, and presentation/referencing are each worth 10%. The final assessment is a 15-minute group presentation followed by a 5-minute question-and-answer session. Every group member must contribute to the presentation. The PowerPoint submission is made electronically through Turnitin. Do not select Harvard automatically for this one. Unlike the previous Roehampton brief, this Northumbria brief requires academic references and a reference list but does not state a specific referencing style in the uploaded document.
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Computing / Artificial Intelligence / Digital Transformation / Management Consultancy
7,000 words
AI Readiness and Programme Adoption Strategy for the NewFutures: AI Programme at Northumbria University London
This postgraduate consultancy project focuses on developing an AI readiness, skills-development and programme-adoption strategy for students and recent alumni at Northumbria University London. The project supports the university’s participation in NewFutures: AI, a funded AI skills and career-readiness programme offering a four-week online course covering responsible AI foundations and specialist pathways in Marketing and Communications, Finance and Accounting, Business Operations and Logistics, Administration, and ICT and Technical Support. The central consultancy challenge is to understand the AI literacy, confidence, readiness and training needs of Northumbria University London students and alumni and translate that evidence into a practical implementation and outreach strategy. The client aims to reach approximately 12,000 students and recent alumni and support a target of 6,000 LMS registrations during the 2026–2027 programme period. The project requires primary and secondary research into AI readiness, demand for different AI-skilling pathways and barriers to participation such as awareness, time, perceived value and accessibility. The consultancy team is also expected to benchmark comparable initiatives and use research evidence to develop recommendations appropriate to different academic disciplines and student and alumni groups. The implementation component focuses on designing an evidence-based outreach and adoption campaign, including appropriate communication channels, messaging, timing, incentives, faculty engagement and stakeholder participation. Recommended channels may include email campaigns, newsletters, social media, student services, events, learning platforms and alumni communications. The project also requires an implementation timeline and indicative budget for the 2026–2027 programme period. The wider assessment develops professional consultancy capability through business and requirements analysis, research methodology, ethical research practice, practical implementation, testing and strategic recommendations. The project charter additionally establishes milestones for research design, data collection, analysis, report development, review and presentation, together with defined responsibilities for project management, data analysis, AI expertise and stakeholder communication. The individual component complements the consultancy work through critical reflection on personal contribution, skills development, decision-making, problem-solving, communication, collaboration, technical capability, innovation and continuous professional development.
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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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Artificial Intelligence
2,000 words
End-to-End Applied AI Development — Comparative Machine Learning and Neural Network Modelling on a Public Dataset
This assessment runs a complete applied AI development cycle end to end: problem definition, dataset selection, preprocessing, model building, optimisation, evaluation and critical reflection. Students identify a real-world problem themselves, formulate a research question from it, and source a suitable dataset from a recognised public repository such as UCI, Kaggle, Data.gov or OpenML. Dataset choice carries more weight than students expect. It must be genuinely suitable for supervised learning, complex enough to make preprocessing and feature engineering meaningful, and — critically — structured so that a traditional machine learning approach and a deep learning approach can be sensibly compared on it. A dataset too small or too clean makes the neural network component pointless; one too large or too noisy makes the whole pipeline unfinishable within the page limit. The source must be referenced and the choice explicitly justified against the research problem. The modelling requirement is fixed: at least two supervised machine learning models, plus one artificial neural network built in a mainstream deep learning framework, all trained and tested. The comparison between them is the analytical core of the work. Reporting that the neural network scored higher is not an answer; explaining why, in terms of the data's structure and each model's inductive assumptions, is. Marks are distributed across problem framing, the traditional models, the deep learning model, evaluation and critical analysis including responsible AI considerations, and academic communication. That responsible AI component is easy to overlook and is not decorative — it asks what the model's limitations mean for anyone who might rely on it. Presentation requirements are specific. The report is page-limited rather than purely word-limited, and every plot must be described in the text while also being legible enough to communicate on its own — a common failure is dense default library output pasted in without axis labels or scale. The implementation is documented in a notebook combining markdown and code cells so the development process is visible, not just the final result, and submissions typically include the cleaned dataset alongside the code. The strongest submissions treat the notebook and the report as one argument. Weaker ones produce a working notebook and then write a report that describes it, rather than a report that uses it as evidence. Our support on assessments of this type is guidance-based. Typical areas of help include: advising on whether a candidate dataset can actually support the required model comparison, explaining how to justify preprocessing decisions, clarifying which evaluation metrics suit which problem type and why accuracy alone is often misleading, showing how to structure a critical limitations and responsible AI discussion, checking Harvard referencing, and reviewing a student's own draft against the published marking criteria.
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Database Design and Implementation for KAP Speciality Chocolates Ltd
Design and implement a relational database system for KAP Speciality Chocolates Ltd based on the given case study. The assignment requires creating an Extended ER Diagram, developing SQL database structures with appropriate constraints, writing SQL queries for business requirements, populating the database with sample data, and demonstrating normalisation from Unnormalised Form (UNF) to Third Normal Form (3NF). Expected Deliverables: One PDF report containing: Extended ER Diagram with entities, attributes, relationships, keys and constraints SQL DDL statements for database implementation SQL DML queries with testing evidence/screenshots Normalisation process from UNF to 3NF
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