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1,000 words
Organisational Strategy and Sustainability: Strategic and Sustainability Analysis of Engineers & Planners Company Ltd
This formative Organisational Strategy and Sustainability assessment requires students to act as a management consultant for BPP Consulting Group and provide a strategic and sustainability evaluation of ENGINEERS & PLANNERS COMPANY LTD for its Board of Directors. The task focuses on how internal capabilities, external market forces, sustainability pressures, ethical practices and responsible leadership shape the organisation’s strategy within an increasingly global business environment. Formative Assessment - Organisa… ENGINEERS & PLANNERS COMPANY LTD is described as a Ghanaian-owned mining and construction contracting company, established in 1997 and headquartered in Accra. Its activities include contract mining, road construction, tailings-dam construction, land reclamation and hard-rock mining, with operations in Ghana and Liberia. The company also owns Dzata Cement, a cement manufacturing facility in Tema with an annual production capacity of approximately 2.3 million metric tons. Formative Assessment - Organisa… Formative Assessment - Organisa… The formative task addresses the first two learning outcomes of the summative assessment. LO1 – Strategic Environmental Analysis requires a brief but critical evaluation of the organisation’s internal resources and capabilities together with its external environment. Students should use one internal and one external strategic framework, with examples including PESTEL or Porter’s Five Forces, identify internal competencies, opportunities and threats, and consider how the organisation might respond to dynamic market forces in regions such as the European Union, Asia or North America. Sources of competitive advantage should also be identified from the internal analysis. Formative Assessment - Organisa… Formative Assessment - Organisa… LO2 – Ethical, Sustainable and Responsible Practices requires students to assess how ENGINEERS & PLANNERS COMPANY LTD could integrate CSR, sustainability or ethical practice into its wider business strategy. The brief recommends applying one framework such as the Triple Bottom Line or Sustainable Supply Chain Management. The discussion should connect the sustainability analysis back to the strategic issues identified under LO1. Formative Assessment - Organisa… Formative Assessment - Organisa… Students are also expected to consider how leadership shapes the organisation’s sustainability response, including engagement with stakeholders such as suppliers, customers and NGOs and the influence of regulatory frameworks such as the UN Sustainable Development Goals and relevant European Union legislation. One specific sustainability initiative should be briefly considered, such as product lifecycle management, eco-design, renewable-energy integration or supply-chain transparency. Formative Assessment - Organisa… The recommended report structure consists of an introduction, LO1 Strategic Environmental Analysis, LO2 Ethical, Sustainable and Responsible Practices, and a conclusion summarising recommended strategic and sustainability improvements. Suggested allocations are approximately 50 words for the introduction, 450 words for LO1, 450 words for LO2 and 50 words for the conclusion. Formative Assessment - Organisa… The total submission limit is 1,000 words, with the main body subject to the word-count restriction. The work must use third-person academic writing, professional formatting, appropriate tables and figures, and consistent Harvard citations. Formative Assessment - Organisa… Higher-quality work is expected to go beyond descriptive use of strategic models by critically evaluating the organisation’s internal and external environment, identifying key drivers of change, considering global and local influences on strategic choices, and linking sustainability and responsible-business practices to stakeholder expectations and regulatory pressures. Formative Assessment - Organisa… Formative Assessment - Organisa…
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Cybersecurity / Information Security Auditing
3,000 words
Physical Security Audit of University Computing Facilities Using ISO/IEC 27002:2022
This postgraduate information-security coursework requires students to act as an IT Security Auditor working for CyberSAFE Auditors and conduct a professional physical-security audit of computing resources used by students at the University of Greenwich. The audit focuses on public student-study and open computing areas within the Dreadnought and Stephen Lawrence Buildings, with findings evaluated against relevant physical-security controls from ISO/IEC 27002:2022 Section 7. 202526 SL-COMP1431 CWK 2026-am … The project begins with planning and project-control activities. Students must define their audit tasks, plan the work independently and document progress through two Work-in-Progress reports, one produced near the beginning of the project and another approximately halfway through. Each WIP report is limited to 250 words and records completed activities, encountered or anticipated problems, planned next steps and potential risk areas. 202526 SL-COMP1431 CWK 2026-am … The second phase involves practical fieldwork. Students must visit the specified university buildings and decide on suitable audit methods, timetable and evidence-gathering procedures. The audit is restricted to public student areas and must not include staff rooms, seminar rooms or utility areas. Students must also comply with client-imposed constraints, including not communicating with university staff and approaching the audit from the perspective of an ordinary student rather than conducting highly technical operating-system or server-level investigation. 202526 SL-COMP1431 CWK 2026-am … 202526 SL-COMP1431 CWK 2026-am … The final professional audit report evaluates secure areas and equipment security, including physical security perimeters, entry controls, protection of rooms and facilities, working in secure areas, equipment siting, supporting utilities and cabling security. Findings should distinguish between expected controls and observed controls, identify gaps and provide justified recommendations for immediate and future management action. 202526 SL-COMP1431 CWK 2026-am … 202526 SL-COMP1431 CWK 2026-am … Assessment places particular emphasis on practical audit methodology, secure-area analysis, equipment security, audit conclusions, gap analysis, professional reporting and the two WIP reports. 202526 SL-COMP1431 CWK 2026-am … Overview word count: approximately 355 words. AI-use note: the brief states that this coursework does not lend itself to reliance on AI-based applications such as ChatGPT and emphasises original analysis, fieldwork and proper attribution of sources. 202526 SL-COMP1431 CWK 2026-am …
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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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Artificial Intelligence / Natural Language Processing / Deep Learning
Applying Advanced AI Methods for Analysing Text Documents
This Advanced Artificial Intelligence coursework requires students to implement and evaluate natural language processing, natural language understanding and neural-network techniques for analysing text documents. The assessment uses a supplied social-media dataset containing more than 89,000 posts linked to 947 news headlines, with each post labelled as real or fake through a combination of headline ground truth and majority-vote annotation. Each social-media post is treated as an individual text document for classification and topic-analysis purposes. CMP_6059B_7059B_2025_26_CW1-pre… The first major task focuses on identifying fake text documents. Students must preprocess the text using appropriate NLP techniques, transform documents into numerical feature representations and experiment with alternative preprocessing approaches to determine which performs best. A separate unseen test set must be reserved to evaluate generalisation, while the remaining data is used for training both shallow and deep neural-network classifiers. Students are expected to explain and justify how the data is split. CMP_6059B_7059B_2025_26_CW1-pre… Students first design a multi-layer perceptron (MLP) capable of predicting whether documents are real or fake. The architecture must be justified in terms of input dimensions, number of layers, neuron counts, activation functions and outputs. A second deep-learning neural network must then be developed for the same classification problem, with justification of the chosen network structure, layer types, activations and other configuration decisions. CMP_6059B_7059B_2025_26_CW1-pre… For both networks, students train baseline models and select three hyperparameters considered most important for improving performance. These hyperparameters must be tuned systematically, with visuals prepared to show the experimentation process and resulting performance changes. Appropriate evaluation metrics are then used to compare the trained models. The strongest MLP and deep-learning models must be saved so that they can be loaded and tested on unseen data during the final demonstration without retraining. CMP_6059B_7059B_2025_26_CW1-pre… The second task focuses on topic discovery using NLP and NLU techniques. Students perform syntactic preprocessing such as tokenisation, stop-word removal and lemmatisation or stemming, and experiment with at least two different text-representation approaches. Suggested methods include Bag of Words, TF-IDF, LDA, word vectors and word embeddings. Students must interpret the discovered topics and explain how those topics relate to document content, linked news headlines and class labels. The best topic-discovery model or models must also be saved for live analysis during the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… The assessment is completed through a bench demonstration, supported by a maximum of seven PowerPoint slides. The slides should document the system design, model-improvement process, performance evaluation and discussion of results for both fake-document classification and topic discovery. Students also submit a ZIP file containing only their Python source files. The demonstration lasts up to 15 minutes, consisting of approximately 10 minutes for presentation and technical demonstration followed by 5 minutes for questions and transitions. CMP_6059B_7059B_2025_26_CW1-pre… The marking scheme allocates 45% to fake-document identification, including descriptive analysis, preprocessing, MLP design and deep-learning design; 35% to topic discovery, including preprocessing, model development and interpretation; and 20% to the structure, organisation, professionalism and Q&A quality of the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… Important for the public Reference Library: the brief explicitly states that the use of Large Language Models or generative AI to produce any part of the submission is strictly prohibited, including code, data processing, testing, writing or PowerPoint content. Therefore, this entry should remain only a high-level public description of the assessment and should not be presented as material intended for direct student submission. CMP_6059B_7059B_2025_26_CW1-pre…
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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 Management / Consultancy / Strategic Management
5,000 words
Business Consultancy Project: Strategic Analysis, Stakeholder Evaluation and Evidence-Based Recommendations
This Business Consultancy Project assessment requires students to produce a professional 5,000-word consultancy report addressing a strategically important issue, challenge or problem facing a client organisation. The project must normally continue from the topic and client proposed in the earlier Consultancy Project Proposal, ensuring continuity between the proposal and final consultancy work. The assessment is designed to replicate professional consultancy practice through independent research, critical analysis and the development of actionable recommendations. Copy of Apr 25 Onwards brief_EE… Copy of Apr 25 Onwards brief_EE… The report begins with an Executive Summary, followed by an introduction and company/client overview. Students must define one clear business problem, establish the overall project aim and purpose, and specify the consultancy focus, which may relate to areas such as strategy, human resources, marketing or sustainability. The selected issue should be clearly connected to the organisation’s wider strategic and industry context. Copy of Apr 25 Onwards brief_EE… A dedicated Stakeholder Analysis section requires identification and evaluation of internal and external stakeholders. Students must assess stakeholder interests, influence, expectations, conflicts and power relationships, and are encouraged to apply a framework such as Mendelow’s Matrix. The purpose is to show how stakeholder dynamics influence the consultancy problem and the feasibility of proposed solutions. Copy of Apr 25 Onwards brief_EE… The largest section is Data Analysis and Framework Application, where students critically evaluate secondary evidence and apply two or three relevant management frameworks. Suitable approaches may include SWOT, PESTLE, Porter’s frameworks and the Balanced Scorecard. The analysis should incorporate credible company and industry evidence, Excel outputs, tables or charts where relevant, and connect patterns in the data to strategic, operational or HR implications. Ethical and sustainability considerations should be embedded where appropriate. Copy of Apr 25 Onwards brief_EE… The project concludes with three prioritised and evidence-based recommendations. Recommendations should be practical, justified, risk-aware and linked directly to the findings. Students must explain expected benefits and demonstrate how the proposed actions create strategic value for the client organisation. Copy of Apr 25 Onwards brief_EE… The final assessed section is a 500-word Employability Reflection covering skills developed through the project, application of theory to practice, professional behaviours, personal strengths and weaknesses, career relevance and specific future-development actions. Copy of Apr 25 Onwards brief_EE… Overall, the assessment integrates consultancy problem definition, stakeholder analysis, strategic frameworks, secondary-data interpretation, ethics, sustainability, recommendations and professional reflection within a Masters-level business project. The marking criteria reward criticality, current evidence, intellectual originality, professional consultancy thinking and accurate Harvard referencing. Copy of Apr 25 Onwards brief_EE… One important point: the brief states that the majority of references should come from sources published within the last 6–12 months, so the final project is expected to use very current company, industry and academic evidence. Copy of Apr 25 Onwards brief_EE…
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Data Science / Time Series Analysis / Machine Learning
Time Series Modelling Case Study: Oil Price Forecasting with ARMA and Alternative Models
This Time Series Modelling Case Study requires students to analyse real-world oil-price time-series data and develop forecasting models capable of predicting future values. The assessment combines traditional statistical time-series techniques with an alternative forecasting approach, requiring students to demonstrate practical modelling skills, critical research engagement and evidence-based interpretation of forecasting results. The coursework is completed individually and contributes 40% of the assessment. assing,,, (1) The assessment is divided into three main parts. Part 1 focuses on developing an ARMA-based forecasting model using daily oil-price data covering approximately 2024 to 2026. Students begin with exploratory data analysis and initial visualisation before testing whether the time series is stationary. Where necessary, appropriate transformations or differencing must be applied to obtain stationarity. Students then define an ARMA model and identify suitable p, d and q parameters using an AIC-based model-selection procedure across the parameter ranges specified in the brief. assing,,, (1) Model adequacy must be assessed using diagnostic analysis. Students inspect residuals, generate additional ACF plots, examine residual distributions and evaluate prediction performance using appropriate metrics such as RMSE. The selected model is then used to forecast oil prices 24 months into the future, with appropriate confidence intervals added to communicate forecast uncertainty. assing,,, (1) Part 2 requires students to research and implement an alternative forecasting approach. Suggested examples include LSTM and Prophet, although another appropriate model may be proposed. Students conduct a literature review supporting the alternative method, build and where relevant hyperparameter-tune the model, generate another 24-month forecast, visualise predictions and confidence intervals, and calculate suitable evaluation metrics. This component is intended to demonstrate independent research and the ability to propose an alternative solution rather than relying only on the conventional ARMA approach. assing,,, (1) Part 3 consists of a 6–8 page technical report explaining the modelling process, forecasting results and resulting inferences. The report should provide a critical analysis rather than simply reproducing numerical outputs. Students are expected to explain why results occurred, justify modelling choices, evaluate how those choices influenced performance, compare forecasts with subsequently observed real data where possible, and construct a coherent narrative supported by plots, images, summary statistics and academic literature. Future improvements to the modelling approach should also be critically discussed. assing,,, (1) Submission consists of both the report and working code. The code may be submitted directly or through an accessible Colab or GitHub repository and must reproduce all models, figures and numerical results presented in the report. The assessment allocates 60% of the marks to code and 40% to the report. Within the coding component, modelling and forecasting completion accounts for 40 marks and code quality and annotation for 20 marks. The report is assessed on analysis and inference, methodological justification, comparison of the two modelling approaches, presentation quality, figures and use of appropriate references. assing,,, (1) Key technical expectations include appropriate testing for stationarity, use of methods such as ADF, ACF, PACF and differencing, systematic model selection, forecasting, evaluation and clear comparison between the traditional ARMA model and the chosen alternative approach. Higher-quality work is expected to interpret what the forecasts mean, identify potential improvements and demonstrate sound technical communication rather than merely reporting model outputs. assing,,, (1) Important for the public Reference Library: the brief explicitly states that students must not use generative AI to write the report, and the rubric indicates that AI text-generation use may result in zero marks for the whole assignment. Therefore, the public entry should remain a high-level description of the assessment rather than material intended for direct submission. assing,,, (1)
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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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Digital Transformation / Management and Leadership
1,500 words
Leading Through Digital Change: Digital Transformation Strategy, Emerging Technologies and Leadership
This postgraduate assessment requires students to act as a Digital Transformation Consultant and evaluate how a selected organisation should respond to accelerating technological change. Students choose one organisation from the options provided in the brief and prepare a Digital Transformation Report and accompanying poster designed to recommend changes that can help the organisation maintain competitive advantage and create business value. Summative_Assessment_Leading_Th… The first section focuses on a Digital Transformation Strategic Framework. Students must critically review and apply one recognised framework, choosing from the McKinsey 4Ds, BCG 3 Stages, Gartner’s 6 Steps or Cognizant’s 4 Pillars. The analysis should identify appropriate digital-transformation objectives and explain how these can support business functions such as operations, ICT and marketing. Higher-level work is expected to move beyond description and critically evaluate recent digital initiatives and organisational challenges. Summative_Assessment_Leading_Th… The second component is an A4 poster examining future digital trends. Students must evaluate two disruptive technologies or techniques likely to influence the relevant industry, employment and the labour market over the next five years. Suggested technologies include artificial intelligence and machine learning, 5G connectivity, IoT, robotics, drone delivery, blockchain, augmented reality and virtual reality. Academic literature and real-world examples are expected to support the evaluation. Summative_Assessment_Leading_Th… The third section addresses Digital Leadership. Students analyse and recommend two leadership styles that could support organisational transformation. Relevant approaches may include hyperaware agile leadership, ethical-tech leadership, people-oriented leadership, agile leadership and Goleman’s leadership styles. The analysis should explain how leadership capabilities can support collaboration, organisational networks and people during digital change. Summative_Assessment_Leading_Th… The final submission should contain a clear introduction, conclusion and Harvard-referenced evidence, and must be written in the third person with professional academic presentation. Summative_Assessment_Leading_Th… Overview word count: approximately 330 words.
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Computer Science / Algorithms / Computational Complexity
Modified Merge Sort for Large-Scale Data: Algorithm Analysis, Complexity and Evaluation
This Computational Algorithms and Paradigms assignment critically examines a modified merge sort algorithm designed for large-scale datasets. The work focuses on the computational problem of sorting very large collections of data efficiently while preserving the stability and predictable complexity associated with classical merge sort. The analysed approach replaces recursive processing with an iterative successive-merging strategy intended to reduce stack overhead and improve practical performance on large datasets. Dubba ramesh(up) The first section identifies the underlying computational problem and frames the main research questions. These include how standard merge sort can be modified to improve large-scale performance, whether recursion can be replaced with a non-recursive iterative process, whether the proposed double-merge technique reduces resource consumption, and how its computational performance compares with classical merge sort. Dubba ramesh(up) A technical section then reconstructs the algorithm in pseudocode. The modified process begins with subsequences of size one and repeatedly merges adjacent sorted subsequences, doubling the merge size after each iteration until the entire dataset is sorted. This bottom-up approach removes the recursive decomposition used in conventional merge sort. Dubba ramesh(up) The assignment also identifies the algorithm's principal inputs and outputs. Inputs include the dataset, number of elements, subsequence boundaries and temporary storage required during merging. The resulting output is a fully sorted and stable sequence. Dubba ramesh(up) Complexity analysis shows that the modified algorithm processes approximately n elements across log₂(n) merging levels, resulting in O(n log n) time complexity in both best and worst cases. Because an auxiliary array is used during merging, the reported space complexity is O(n). Dubba ramesh(up) The final critical evaluation highlights the main benefits of the modified approach, including removal of recursive-call overhead, greater stability when processing very large datasets, predictable performance and preservation of merge-sort stability. Its main limitation is the continued requirement for auxiliary memory during the merge operation. The work also notes that the performance advantages are most relevant for large-scale datasets and may be less significant for smaller inputs. Dubba ramesh(up) Overall, the assignment integrates algorithm interpretation, pseudocode extraction, input-output analysis, complexity analysis and critical evaluation within the context of large-scale sorting. Note: this upload appears to be the completed student response rather than the original assessment brief, so the referencing style and exact formal overall word limit are not stated. I would leave the reference-style field as Not specified unless you also upload the official 7COM1078 guideline.
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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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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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Digital Forensics / Cyber Security
3,000 words
Digital Forensics Investigation of a USB Device: Evidence Acquisition, Analysis and Reporting
This Digital Forensics assessment requires students to undertake a simulated professional investigation of a USB storage device while acting as a digital forensic trainee for a fictional forensic-services organisation. The nature of the suspected wrongdoing is initially unknown, requiring the investigator to apply appropriate forensic methodologies, tools and analytical techniques to identify suspicious activity, recover relevant artefacts and determine whether evidence of malicious or unauthorised behaviour exists. 7070SCN_Assessment Brief_2526MA… The investigation begins with authorisation, evidence handling and chain of custody. Students must demonstrate secure receipt and management of the USB device, operate within the authorised scope of the investigation and maintain records covering each stage of the evidence lifecycle. Forensic acquisition requires use and validation of a software write blocker, creation of bit-for-bit forensic images and cryptographic hashing to demonstrate that working and evidential copies maintain integrity. 7070SCN_Assessment Brief_2526MA… The analytical stage involves examination of the working forensic image using appropriate digital-forensic tools. Students investigate file-signature mismatches, password-protected files, registry artefacts, USB history, user activity, metadata and timeline relationships. Where suspicious executable files are discovered, static and/or dynamic malware analysis may be undertaken. The investigation can therefore span documents, images, applications, executables and registry hives. 7070SCN_Assessment Brief_2526MA… Students must maintain objective and chronological forensic case notes documenting evidence acquisition, preservation, analysis and interpretation. These records should support reproducibility and potential evidential admissibility. The final forensic report summarises findings, explains and justifies the tools and methodologies used, records integrity-verification results, discusses relevant legal, ethical and professional principles and provides appropriate recommendations for further action. 7070SCN_Assessment Brief_2526MA… The assessment also encourages advanced forensic analysis where relevant, including OSINT, password recovery or decryption, data carving, regular-expression searching, advanced registry analysis and static or dynamic malware investigation. Professional practice is assessed through adherence to recognised forensic methodologies, industry best practice, legal and ethical obligations, chain-of-custody records and evidence-handling procedures. 7070SCN_Assessment Brief_2526MA… Overall, the coursework integrates forensic acquisition, evidence preservation, artefact analysis, advanced investigation techniques, professional documentation and legally defensible reporting within a realistic digital-forensics case-study environment. Important for your public Reference Library: this brief explicitly states that it is for Coventry University Group students' own use and must not be passed to third parties or posted on any website. 7070SCN_Assessment Brief_2526MA… So use the metadata and an original high-level overview like the one above, but do not upload or publicly reproduce the assessment brief itself.
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Data Mining / Data Science
Customer Service Escalation Risk Analytics Using Data Mining and Machine Learning
This advanced Data Mining assessment applies the Knowledge Discovery in Databases (KDD) process to a realistic customer-service risk scenario. Acting as a Data Scientist, students analyse a historical Customer Service Escalation Risk dataset to help an organisation identify early indicators of dissatisfaction and operational bottlenecks that may lead to serious or legal customer escalations. The resulting analysis is intended to support strategic decisions relating to staffing, employee training, customer-journey improvement and escalation prevention. CMP-7023B_Assessement_2 (2) The dataset incorporates customer demographics, account characteristics, communication channels, issue categories, operational measures such as waiting times, transfers and SLA breaches, behavioural indicators including sentiment and response delays, and commercial variables such as monthly fees and contract value. The target variable, escalation_level, contains four categories: No escalation, Minor escalation, Serious escalation and Legal escalation. CMP-7023B_Assessement_2 (2) Students begin with data exploration and visualisation, producing appropriate descriptive statistics and identifying patterns, distributions and potential data-quality concerns. They then perform data cleansing, transformation, feature engineering and preprocessing. Variables that may introduce leakage or unreliable predictions because of their meaning, timing or quality must be critically assessed and justified. CMP-7023B_Assessement_2 (2) The supervised-learning stage requires students to develop, tune and compare predictive models using techniques such as k-nearest neighbours, Decision Trees, Support Vector Machines, ensemble methods or neural networks. Appropriate multiclass evaluation metrics must be used, alongside interpretation of influential variables and model behaviour. CMP-7023B_Assessement_2 (2) The assessment also includes unsupervised learning, requiring comparison of clustering methods such as K-Means and hierarchical clustering after removal of the target variable. Students may apply encoding, normalisation and dimensionality-reduction methods such as PCA or t-SNE and must interpret how the resulting clusters relate to escalation behaviour. CMP-7023B_Assessement_2 (2) Overall, the project assesses independent analytical judgement, modelling justification, comparative evaluation and clear communication of actionable findings for both technical and executive audiences. CMP-7023B_Assessement_2 (2) Overview word count: approximately 340 words. AI-use note: AI tools may only assist with small, specific code snippets. Any AI-generated code must be clearly acknowledged and cited, while the analysis, coding decisions, interpretation and final evaluation must remain the student's own work. CMP-7023B_Assessement_2 (2)
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Leadership / Digital Leadership / Business Management
4,000 words
Leadership in a Digital Age: Critical Self-Analysis, AI Transformation and Personal Development
This Leadership in a Digital Age assessment requires students to critically analyse their leadership strengths, behaviours and development needs within contemporary digital environments. The individual 4,000-word report combines academic leadership theory, diagnostic self-assessment, professional reflection, digital transformation and forward-looking personal development. Its central purpose is to demonstrate self-awareness and evaluate how leadership capabilities must evolve in response to technological, organisational and workforce change. NUL Assessment Brief LD7090 202… The first section requires critical evaluation of two or three contemporary leadership theories or models in relation to digital leadership. Students should avoid outdated approaches and instead examine how relevant theories align with the behaviours and characteristics required of leaders operating in technology-enabled organisations. NUL Assessment Brief LD7090 202… The second section focuses on self-analysis. Students use diagnostic tools relating to areas such as temperament, workplace culture, motivation, emotional control, management skills and Belbin team roles. Results should be reported and interpreted before being used to construct a personal SWOT analysis concentrating specifically on leadership characteristics relevant to the digital age. NUL Assessment Brief LD7090 202… A further component examines the leadership of hybrid, multi-generational teams. Students identify challenges that may arise in such environments and evaluate leadership capabilities and behaviours that could address them. Ethical, social and legal responsibilities associated with digital leadership must also be considered. NUL Assessment Brief LD7090 202… The report additionally evaluates how digital leaders can use Artificial Intelligence to support digital transformation and organisational performance. Appropriate workplace examples may involve machine learning, predictive analytics, intelligent automation or generative AI, with consideration of required resources such as data infrastructure, organisational skills and external partnerships. NUL Assessment Brief LD7090 202… The final section requires a Personal Development Plan containing justified leadership-development objectives, learning activities, measurable success criteria and timescales. These objectives should emerge directly from the earlier self-analysis and demonstrate how the student intends to become a more effective leader in a current or future digital role. NUL Assessment Brief LD7090 202… Overall, the assessment integrates leadership theory, reflective self-evaluation, hybrid-team management, AI-driven transformation and structured professional development within the context of leadership in the digital age.
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Machine Learning / Cloud Computing / Artificial Intelligence
4,004 words
Cloud-Based Machine Learning for Financial Fraud Detection
This Machine Learning on Cloud assessment requires students to design, implement and critically evaluate a cloud-oriented machine-learning solution for financial fraud detection. Working as a group, students address a scenario in which a financial-services organisation requires an automated model capable of detecting fraudulent transactions, reducing financial losses and improving customer security. The project combines machine-learning development with critical evaluation of cloud infrastructure, data preparation, model performance and responsible AI considerations. NUL - LD7187 -Assessment Brief … The project begins with a cloud feasibility study comparing at least two major machine-learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Students evaluate factors including performance, scalability, cost, compliance, system integration and vendor lock-in before providing a justified recommendation. The project then moves into Exploratory Data Analysis, where patterns, anomalies and correlations within the fraud dataset are investigated using visualisations such as heatmaps, histograms and boxplots. NUL - LD7187 -Assessment Brief … A substantial part of the assessment focuses on data preprocessing and class imbalance. Students are expected to clean and transform the dataset, apply scaling and encoding, perform feature engineering and investigate techniques such as SMOTE, undersampling and cost-sensitive learning. These decisions must be justified in terms of their potential effect on predictive performance. NUL - LD7187 -Assessment Brief … Students must then select and train at least two machine-learning models. Suggested algorithms include Logistic Regression, Random Forest, XGBoost and Neural Networks. Appropriate cross-validation and hyperparameter-tuning procedures should be applied, followed by systematic evaluation using precision, recall, F1-score, AUC and precision-recall curves. Supporting visualisations should include confusion matrices, ROC curves and feature-importance analysis. NUL - LD7187 -Assessment Brief … The final component addresses professionalism and ethics in cloud-based AI, including bias, fairness, transparency, data privacy and environmental sustainability. Overall, the project integrates cloud-platform selection, exploratory analytics, preprocessing, imbalanced-data handling, predictive modelling, model evaluation and ethical AI into an applied financial fraud-detection solution. NUL - LD7187 -Assessment Brief … Note: the uploaded brief does not explicitly name a referencing system. If your portal requires a selection, I would use Not specified rather than assume Harvard.
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Leading Through Digital Change
2,500 words
Leading Through Digital Change (CW1)
The Leading Through Digital Change (CW1) assessment is a summative coursework assignment for MSc Management programmes at BPP University Business School. The assessment accounts for 100% of the module marks, with a minimum of 50% required to pass. Students must submit the assessment through Turnitin and follow the Harvard Referencing System. The coursework consists of a 1,500-word business report together with an A4-size poster. The assessment requires the student to act as a Digital Transformation Consultant responsible for coordinating technology expertise within an organisation. The Chief Information Officer (CIO) requires a Digital Transformation Report and Poster that evaluate and recommend changes designed to maintain competitive advantage and create business value. Students must select one organisation from the options provided in the assessment brief: Tata Motors, Meezan Bank Limited, Hulu, or Puma SE. Part 1 addresses the Digital Transformation Strategic Framework and is linked to Learning Outcome 1. The student must critically review and propose one appropriate digital transformation framework for the selected organisation. The brief specifies that one framework should be used from McKinsey 4Ds, BCG 3 Stages, Gartner's 6 Steps or Cognizant's 4 Pillars. The section should also include digital transformation objectives supporting areas such as operations, ICT and marketing. Part 2 addresses Future Digital Trends and Learning Outcome 2. Students must design a poster evaluating two disruptive technologies or techniques likely to affect the smartphone industry, employment and the labour market over the next five years. Suggested technologies include Artificial Intelligence and Machine Learning, 5G Connectivity, Internet of Things, Robotics, Drone Delivery, Blockchain, and Augmented or Virtual Reality. Academic literature and real-life examples should support the discussion. Part 3 addresses Digital Leadership Recommendations and Learning Outcome 3. Students must analyse and propose two appropriate digital leadership styles that the selected organisation should develop to effectively manage and support digital transformation. The brief identifies possible approaches including hyperaware agile leadership, ethical-tech leadership, people-oriented leadership, agile leadership and Goleman's six leadership styles, while allowing other relevant approaches. The required report structure includes the BPP University administration cover sheet, table of contents, list of abbreviations where appropriate, introduction, Part 1, Part 2 (Poster), Part 3, conclusion, references and appendix where required. The main business report is limited to 1,500 words, while the poster has no separate word limit but must fit on A4 paper. The report must be written in the third person, use professional formatting, include page numbers, correctly label tables and figures, and use Harvard in-text citations and references. The marking criteria emphasise critical evaluation rather than simple description. Higher performance requires evidence of extensive personal research, critical analysis of digital transformation frameworks, emerging technologies and leadership approaches, supported by academic literature and real-world examples. The rubric also expects strong links between the organisation's technological challenges, transformation objectives, disruptive technologies and proposed leadership approaches.
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Team Research and Development
Team Research and Development – Research Question, Hypothesis and Statistical Data Analysis
This group assessment for 7COM1079 – Team Research and Development requires students to collaboratively select a dataset and conduct a structured research investigation using data analysis. The assessment is worth 40% of the module mark and requires group members to work together to develop and submit a final research report. All group members are expected to contribute equally to the work, with one designated group member submitting the final assessment on behalf of the group. The main objective of the assignment is to develop a meaningful research question and corresponding hypothesis based on a selected dataset. The research question defines a specific claim or issue that the group intends to investigate and provides the starting point for constructing and testing an evidence-based argument. The hypothesis provides a proposed explanation or prediction that can be examined through analysis of the available data. Together, the research question and hypothesis establish what evidence is required, how that evidence will be evaluated and how effectively the findings can support or challenge a particular position. The assignment permits three types of research questions. The first involves establishing a difference in means between two groups. The second involves establishing a difference in proportion between two groups. The third involves establishing a correlation between two measures. The selected research question should therefore be appropriate for the characteristics of the chosen dataset and should allow the group to perform meaningful statistical analysis. Following the selection of the dataset and formulation of the research question and hypothesis, students are expected to produce appropriate data visualisations and statistical analysis. The analysis should provide evidence relevant to the research question and allow the proposed hypothesis to be examined systematically. The resulting findings should be interpreted in relation to the original research question and hypothesis rather than simply presenting numerical results. The assignment provides a Microsoft Word final report template containing the required table of contents, chapter and subchapter names and explanations of the expected content. Students are instructed to download and use this template when preparing their final report. Work produced by individual students during their first assignment may be incorporated where the student examined the same dataset allocated to the group, provided the material is appropriately incorporated into the group submission. All italicised instructional text in the template must be removed before submission. The completed amended template and the group's dataset file must be submitted through Canvas. Acceptable submission formats include PDF, DOC, DOCX, CSV and XLS. The assignment has a late-submission penalty, and the submission deadline is stated as being available in the assignment specification on Canvas. Assessment is based on the criteria provided in the module rubric. The rubric is used to assess the group's work, with group members initially receiving the same mark, although peer review may be taken into account. The assignment therefore combines collaborative research, statistical reasoning, data visualisation, analytical interpretation and academic report writing. The assignment instructions explicitly state that students should not use AI for this assessment. The module team also reserves the right to arrange a viva if academic misconduct is suspected. The final report should therefore represent the group's own research, analysis, interpretation and contribution.
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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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2,000 words
Covid 19 and the Automotive Industry
This individual report examines the impact of COVID-19 on the automotive industry and the management of automotive supply chains during the disruption. The assignment requires students to select a company operating in the automotive sector and focus the report on a particular product manufactured by that company. The analysis must consider how the COVID-19 pandemic disrupted the supply chain and how the resulting social impacts and government-imposed restrictions affected automotive supply chain activities. The report requires consideration of both upstream and downstream activities within the selected automotive supply chain. Students are expected to analyse the effects of COVID-19 on the chosen country, organisation or product and examine how entities within the supply chain responded to the disruptive events and government restrictions. The analysis should consider the actions taken by supply chain participants and evaluate the successes and failures associated with their approaches to managing the disruption. A central requirement of the assignment is to examine supply chain management and adaptation during the pandemic. Students should explain how the selected automotive company and relevant supply chain entities responded to disruption and restrictions, considering the challenges affecting the movement and management of supply chain activities. The report should then provide independent analysis of the effectiveness of the approaches used rather than simply describing the events or responses. The assignment also requires students to develop recommendations for sustainably managing an automotive supply chain in the event of similar disruptions in the future. These recommendations should follow from the analysis of the selected company, product and supply chain and should address how supply chain management could be improved to respond more effectively to disruptive events. The suggested report structure consists of an introduction, background, supply chain impacts, supply chain management, analysis, recommendations and conclusions, followed by references. The background section should discuss the COVID-19 disruption and describe the relevant automotive supply chain, while the supply chain impacts section should examine the effects on the selected country, organisation or product. The supply chain management section should discuss how the supply chain adapted to the disruption, followed by an independent analysis of the successes and failures of those management responses. The report must be supported by ample and suitable academic and other appropriate references. The brief specifically requires Harvard referencing and states that students should cite core books, journal articles and non-journal articles. Blogs and Wikipedia should be avoided. The completed report has a maximum length of 2,000 words and the stated submission deadline is 5:00 pm on 10 December 2025.
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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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Leadership and Change
3,000 words
Leadership and Change – Reflective Learning Portfolio
This 3,000-word coursework is a reflective learning portfolio focused on leadership, organisational change and reflective practice. The assessment is worth 100% of the module mark and is structured into four individual components. It is designed to assess leadership capabilities such as strategic thinking, emotional intelligence, communication, adaptability, team building, ethical decision-making and change management, while encouraging students to connect leadership theories and self-assessment tools with their own development. Component 1 focuses on individual leadership style and requires students to provide the results of the Fastest MBTI Test and critically reflect on what the outcome means in relation to their preferred leadership style. Students are expected to connect their MBTI results with leadership approaches covered in the module, including the Situated Leadership model. This component represents 20% of the assessment and has a suggested length of 600 words. Component 2 examines team roles. Students must present evidence from their Team Roles Test, identify their preferred team role and explain why it suits them. They must then identify two other Belbin team roles that would be important when building a team and explain their relevance. Connections can also be made between the Team Roles Test, Belbin’s framework and the results of the Conflict Management questionnaire. This component contributes 20% and has a suggested length of 600 words. Component 3 requires students to develop an individual organisational change plan in response to a fictional case study in which ARU is considering the adoption of AI to mark student coursework assignments and examinations. The plan evaluates change enablers and barriers, stakeholder involvement, ethical considerations and resource constraints. Students then develop an implementation strategy using a suitable change-management framework such as Lewin’s Change Model or Kotter’s 8-Step Model, including stakeholder engagement, communication, resistance management and strategies for sustaining change. This component represents 30% and has a suggested length of 900 words. Component 4 is a 900-word reflective account of what students learned about leadership while working as part of a team to design the organisational change plan. The reflection considers how leadership emerged, team collaboration, missing or required roles, conflict management and personal development. Students may structure the reflection using Kolb’s Reflective Learning Cycle or Gibbs’ Reflective Cycle and support their discussion with theories such as transformational, situational and distributed leadership, as well as Belbin’s team-role framework. Overall, the learning outcomes require students to critically reflect on their leadership style, evaluate classical and contemporary leadership theories, understand organisational change processes and develop evidence-based change strategies. The assessment also emphasises reflective practice and personal leadership development through experiential learning in a group setting.
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Principles of Data Science
2,000 words
Principles of Data Science – Data Analysis Portfolio
This portfolio assignment for the Principles of Data Science module at Coventry University requires students to analyse the Global Life-Work Balance Index 2025 dataset using statistical and data science techniques in R. The dataset ranks 60 countries according to life-work balance using factors including statutory annual leave, paid maternity leave, sick leave, healthcare, public safety, public happiness, LGBTQ inclusivity and average working hours per employee. The assignment has a 2,000-word equivalent limit, excluding the reference list and output. The portfolio consists of two main tasks. Task 1 is a group task involving multivariate data analysis. Students must use R to perform Principal Component Analysis (PCA) and Cluster Analysis on the dataset. For PCA, students analyse quantitative variables, produce and interpret relevant visualisations such as screeplots, biplots and loadings plots, and investigate the effects of Region and Healthcare System. The PCA analysis also requires comparison of the overall dataset with countries from Europe. The cluster analysis component requires students to cluster both countries and variables using different distance metrics and hierarchical clustering methods. Students compare methods such as Manhattan and Euclidean distances and single linkage and Ward’s method, present comparisons in compact tables, and interpret relevant dendrograms. They must then compare the conclusions obtained from PCA and Cluster Analysis, identifying common insights and apparent conflicts and discussing the extent to which the results are explainable rather than simply interpretable. Task 2 is an individual task focusing on Exploratory Data Analysis and Linear Models. Students create a scatter matrix using ggpairs(), investigate strongly correlated variables, and identify quantitative variables that may help predict Region for European and Asian countries. They then develop and critically assess linear regression models for predicting Score, including models based on employment variables and broader quantitative predictors. Model comparison and selection use concepts including AIC, while diagnostic plots are used to identify countries requiring further investigation. The individual task also requires students to use European Life-Work Balance Index 2023 data to make predictions for European countries not included in the 2025 dataset and to construct a Residuals versus Fitted Values plot. Finally, students must combine the conclusions from their individual linear modelling work with the PCA and Cluster Analysis findings to identify specific discoveries about the variables and countries in the dataset. R code, output and relevant plots must be included directly within the reports. The assignment encourages use of the R tidyverse and requires appropriate referencing of sources. The brief specifies APA-style referencing for the individual and group work. It also states that generative AI may be used for inspiration but not for generating answers or analysing the datasets, and any permitted AI use must be acknowledged and documented.
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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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Leading Through Digital Change
1,000 words
Digital Transformation Report – Leading Through Digital Change
This assessment is a formative coursework task for the Leading Through Digital Change module at BPP University Business School. The assignment requires students to act as a Digital Transformation Consultant and prepare a Digital Transformation Report for one selected organisation. The purpose is to evaluate the organisation’s current digital transformation requirements and propose changes that can help maintain competitive advantage and create business value. Students are required to select one organisation from the options provided in the assessment brief. The listed organisations include Tata Motors, Meezan Bank Limited, Hulu, and Puma SE. The report should consider the organisation’s digital change environment and develop an appropriate strategic response to technological change. The main task requires students to critically review and propose one appropriate digital transformation strategic framework for the selected organisation. The assessment specifies that students should use one framework from McKinsey 4Ds, BCG 3 Stages, Gartner’s 6 Steps, or Cognizant’s 4 Pillars. The selected framework should be applied to the organisation’s digital transformation requirements. Students must also identify digital transformation objectives that support important organisational departments such as operations, ICT, and marketing. Examples and evidence from personal research can be incorporated to strengthen the analysis. The report should demonstrate understanding of management and leadership strategies in organisations experiencing digital change. Students are expected to connect the proposed framework and digital transformation objectives with the factors driving technological transformation within the selected organisation. The assessment also requires appropriate academic structure, including an introduction, Task 1, conclusion, references, and an appendix where necessary. The report has a main-body word limit of 1,000 words, excluding the cover page, table of contents, list of abbreviations, references, and appendices. It should be written in the third person and follow professional academic standards. Sources must be appropriately cited using the Harvard referencing system. The assessment focuses on applying management and leadership knowledge to digital transformation and developing a structured strategic response to digital change.
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Multimodal Sentiment Intelligence Platform for Dynamic Market Insights
This assignment presents the development of a Multimodal Sentiment Intelligence Platform for Dynamic Market Insights. The project addresses the need for real-time market sentiment analysis by combining text and visual data through an AI-driven multimodal approach. Traditional sentiment analysis methods may have limitations when dealing with diverse data modalities, and this project aims to address this gap by integrating multiple artificial intelligence and deep learning techniques. The primary objective is to develop an AI-driven platform capable of performing real-time sentiment analysis and supporting market trend prediction. The proposed system also aims to generate business intelligence insights that can support applications in marketing, finance, and customer service. The project incorporates deep learning, large language models, and multimodal fusion techniques to improve sentiment understanding. For text-based sentiment analysis, the assignment identifies BERT and GPT-2 for sentiment classification. For visual sentiment analysis, YOLOv8 is used for object detection while DeepFace is incorporated for facial emotion recognition. Feature-level and decision-level fusion strategies are applied to combine information from different modalities and improve the overall sentiment analysis process. Retrieval-Augmented Generation (RAG) is also incorporated to provide context-aware sentiment insights. The proposed platform uses Amazon reviews and IMDb reviews as text datasets. For image or video-based multimodal data, the assignment references the CMU-MOSEI dataset and an Amazon-Reddit merged reviews dataset. Overall, the work focuses on combining natural language processing, computer vision, deep learning, large language models, multimodal fusion, and retrieval-augmented generation to create a platform capable of producing dynamic sentiment and market insights.
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Digital Marketing / Marketing Analytics
2,483 words
Digital Marketing Analytics (BS783) — GA4 Performance Analysis and 90-Day Campaign Strategy for the Google Merchandise Store
This Level 7 report positions the writer as a digital marketing consultant engaged by the Google Merchandise Store, a live e-commerce operation. All quantitative work is drawn from the public GA4 Analytics Demo Account, so the analysis rests on real platform data rather than invented figures, and the report is structured in two halves that move from diagnosis to prescription. Part A is research and analysis. It opens by defining and evaluating the role analytics currently plays in shaping the store's digital marketing strategy — what is being measured, what that measurement is used for, and where the strategic gaps sit — grounded in marketing performance-measurement literature rather than description of the interface. The tactical analysis then assesses how effectively the store's digital channels and social content drove traffic and conversions across two defined quarterly reporting periods. Performance reporting follows, comparing the two periods in detail to identify the strongest customer segments, acquisition channels and products, and — more importantly — offering a reasoned explanation for why performance differed between them, distinguishing seasonality and campaign activity from genuine structural change. Part B turns to campaign strategy for a niche lifestyle market segment. A customer persona is developed and supported by the behavioural evidence visible in the GA4 data rather than asserted demographics, and a full journey map traces that persona from awareness through consideration, conversion and retention, identifying the friction points at each stage. Strategic recommendations then propose specific enhancements to channels and content aimed at that audience, each justified against the performance evidence from Part A and against the competitive landscape the store operates in. The final section builds the measurement architecture for the proposed campaign over the following ninety days. It sets goals and performance management parameters, selects the tools and metrics that will track them, defines the reporting cadence and the decision thresholds that trigger optimisation, and places particular weight on measuring return on marketing investment — connecting spend to sales, leads and customer satisfaction rather than to vanity engagement figures. Attribution limitations and the practical constraints of GA4 measurement are acknowledged where they affect confidence in the numbers. Throughout, screenshots and exported GA4 reports evidence the claims made, academic and practitioner sources support the analytical framing, and Harvard referencing is applied consistently. The submission is a single file through Turnitin.
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Data Visualisation / Business Intelligence
2,500 words
Data Visualisation (BS666) — Business Analyst Client Report: Dashboard Development, Tool Evaluation and Accessibility in Power BI and Tableau
This Level 7 resit assessment takes the form of a single client-facing report written from the position of a qualified business analyst. Rather than assembling semester activities, it asks for one sustained piece of analytical writing that carries a business case from raw open data through to a defended set of visualisations and the decisions they support. The report opens by identifying a data source drawn from an approved open repository and setting out the business problem the client faces. Provenance, structure, granularity and known limitations of the dataset are examined honestly at this stage, since every downstream claim rests on them, and all data is referenced in full — including data appearing inside charts, as the brief specifically requires. The main body works through three connected strands of critical evaluation. The first traces the development sequence of the visualisations themselves: how the data was prepared and cleaned, why particular chart types were selected over the alternatives available, how layout and interactivity were arranged for the intended audience, and how the design changed across iterations once weaknesses became visible. At least three completed visualisations are then evaluated individually and critically — what each one reveals, where each falls short, and what a reader could reasonably conclude from it. The second strand compares Power BI and Tableau as working environments for this specific dataset rather than in the abstract. Data connectivity, transformation and calculation capability, visual flexibility, publishing and sharing, licensing and governance are all weighed against what the client actually needs, with the practical friction encountered during the build reported rather than smoothed over. The third strand addresses accessibility and cognitive processing. It examines how each platform handles colour contrast and colour-vision deficiency, text alternatives, keyboard navigation and screen reader support, and then moves into the perceptual side — pre-attentive attributes, data-ink economy, chart junk, working memory limits and how visual encoding choices either reduce or inflate the effort a reader must spend to extract meaning. The argument connects this directly to decision quality in organisations with diverse analytical literacy. Findings are reported at length and translated into concrete business implications, with a conclusion that states what the client should do and on what evidence. The submission follows the prescribed structure throughout: title page, executive summary, contents, introduction, business problem, main evaluative section, findings, conclusion, Harvard reference list and appendices, presented as a single file for Turnitin.
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Data Management / Business Analytics
2,500 words
Data Design Management (BS514) — Data Strategy Consultancy: Relational Database Design, SQL Implementation and Pipeline Transformation
This Level 7 assessment places the writer in the role of a Data Strategy and Analytics Consultant appointed by an organisation operating in a realistic industry sector. The brief is entirely simulated, so the work sets out a defensible set of assumptions about the organisation's data environment before any design begins, and populates the resulting database with synthetic but realistic records. The deliverable is a slide deck carrying full explanatory notes, submitted as a single PDF, and weighted across three connected tasks. Task one establishes the business case. It describes how the chosen organisation currently collects, stores and uses data across customer interactions, sales transactions, operational processes and digital channels, and identifies where that fragmented picture costs the business in efficiency, resource use, retention and decision quality. A SWOT analysis benchmarks the organisation against a named real-world competitor in the same sector, drawing on publicly available market information rather than assertion. The section closes with a critical evaluation of modern relational database advancements — cloud-hosted SQL services, distributed architectures and data warehousing — assessed not in the abstract but against what each would actually change about this organisation's business model. Task two carries the heaviest weighting and is the technical core. Key business entities are identified from the scenario, a current-state data flow diagram traces how data moves from collection points through to storage and reporting with the existing ETL approach made explicit, and a future-state ER diagram is then built with full attributes, primary and foreign keys, relationships and cardinality. The design is normalised to third normal form with the decomposition reasoning shown. Implementation follows in SQL: tables created with appropriate integrity constraints, at least ten realistic sample records inserted per table, and five business questions answered through working queries — highest-performing product or campaign, average conversion by category, workload distribution across staff, accounts with overdue or pending items, and most effective service channel. Outputs accompany every script. A transformation demonstrating query optimisation is included with before-and-after samples so the improvement is evidenced rather than claimed. Task three steps back to the technology decision. Two widely used data processing platforms are compared in tabular form across integration, cleaning, transformation and automation capability, judged specifically against this organisation's constraints, with a reasoned justification for the tool finally selected. The transformed dataset is then used to answer two management-level questions — where investment should be prioritised and how retention might be improved from observed behavioural patterns — each interpreted briefly and tied back to a concrete recommendation. Slide structure follows the prescribed layout, SQL scripts sit in the notes section, and the complete script file is reproduced in the appendix.
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Supply Chain Management / Strategic Sourcing
1,800 words
Strategic Sourcing and Supply Chain Resilience Under Global Uncertainty
This postgraduate supply-chain assessment examines how organisations operating in the UK are adapting their strategic sourcing and supply-chain practices in response to sustained global uncertainty. The assignment is situated within an environment shaped by major disruptions including the Covid-19 pandemic, geopolitical conflicts, trade tensions, tariff volatility and Brexit, all of which have challenged the reliability, cost efficiency and resilience of globally distributed supply networks. Students select either a British organisation or a multinational enterprise with significant UK operations and critically analyse its documented supply-chain strategy. Potential areas of investigation include insourcing and outsourcing, offshoring, reshoring and nearshoring, single versus multiple sourcing, supplier selection and monitoring, supplier and customer relationships, inventory management, digitalisation, sustainable sourcing and supply-chain risk management. The assessment requires more than description. Students must provide a critical and theoretically informed evaluation of the organisation's decisions, identifying both opportunities and risks associated with its strategic response. At least one relevant theoretical framework or analytical tool must be applied to assess the company's sourcing or supply-chain decisions. The analysis must also include at least one company practice or decision from 2025 and conclude with a detailed recommendation explaining actionable steps, implementation challenges, expected outcomes and alignment with organisational objectives. Research must draw on a minimum of 10 credible sources, including at least five academic journal articles and five non-academic sources such as news articles, industry reports or corporate publications. At least two of the non-academic sources must have been published during 2025–2026. Students may present the work as either a business report or academic essay, with consistent in-text citation and referencing. Norwich Business School normally requires the Harvard referencing system. Overview word count: approximately 340 words. Note: the document header states PG Coursework 2025–26, but one later line gives a submission deadline of 15 May 2025, which appears inconsistent with the stated academic year. I would therefore use 2025/26 for the library record and avoid publishing the deadline unless it is verified.
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Artificial Intelligence
Developing an Intelligent Chatbot and Expert System for UK Train Services
This postgraduate Advanced Artificial Intelligence group project requires students to design, implement, evaluate and demonstrate an intelligent conversational system for a UK train operating company. The chatbot combines conversational AI, expert-system concepts, predictive modelling and knowledge-based reasoning to support both railway passengers and operational staff. The coursework is worth 70% of the module and is designed to develop practical experience in applying modern AI techniques to realistic service and operational problems. The first task requires the chatbot to interact with passengers, collect journey requirements such as origin, destination, date and travel time, and identify the cheapest available train ticket. Appropriate railway ticket data sources or APIs may be used, with the selected ticket presented together with access to the relevant booking service. The second task extends the system to improve customer service through train-delay prediction. The chatbot gathers information about a passenger's current train, location, delay and destination before passing these data to one or more predictive models. Students process historical railway-performance data, train and compare suitable machine-learning models, evaluate their accuracy and integrate an appropriate model into the chatbot. The third task introduces an expert system for railway contingencies. Students extract rules and operational knowledge from provided contingency and station-disruption documents and construct a knowledge base capable of advising railway staff during events such as partial or complete line blockages. The system should gather details such as event type, location, time and severity, then provide relevant operational guidance, diversion information, alternative services and passenger advice. The overall architecture may include a user interface, NLP/NLU component, knowledge base, reasoning engine, predictive model, database and optional knowledge-acquisition component. Particular emphasis is placed on context-aware dialogue, reliable reasoning, appropriate fallback responses and effective user experience. Assessment outputs include the working chatbot, source code, a live presentation and demonstration, a detailed group technical report, and an individual contribution report. Overview word count: approximately 375 words. AI-use note: pre-trained LLMs may be used as an engine within the system, but they must not be used to generate coursework code. Any use of an LLM within the solution must be clearly justified and explained in the group report.
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Data Mining / Data Science
Customer Service Escalation Risk Analytics Using Data Mining and Machine Learning
This advanced Data Mining assessment applies the Knowledge Discovery in Databases (KDD) process to a real-world customer-service analytics scenario. Acting as a Data Scientist, students analyse a historical Customer Service Escalation Risk dataset to help an organisation reduce serious and legal customer escalations by identifying early signs of dissatisfaction, service bottlenecks and operational risk. The findings are intended to support business decisions relating to staffing, employee training, customer-journey improvement and escalation prevention. The dataset contains information covering customer demographics, account characteristics, communication channels, issue categories, operational measures such as wait times, transfers and SLA breaches, behavioural indicators including sentiment and response delays, and commercial variables such as monthly fees and contract value. The target variable contains four escalation outcomes: No escalation, Minor escalation, Serious escalation and Legal escalation. The first stage requires data exploration, visualisation and summary, including examination of variable distributions, dataset structure, descriptive characteristics and potential data-quality issues. Students then perform appropriate data cleaning, transformation, feature engineering and preprocessing. Particular attention must be given to variables that could introduce prediction leakage because of their meaning, timing or reliability. The supervised-learning component requires development and tuning of predictive models using suitable techniques such as k-nearest neighbours, Decision Trees, Support Vector Machines, ensembles or neural networks. Models must be evaluated using appropriate multiclass metrics and compared systematically, with interpretation of influential features and model behaviour. The assessment also requires unsupervised learning. After removing the escalation target, students apply and compare clustering approaches such as K-Means and hierarchical clustering. Appropriate preprocessing, encoding, normalisation or dimensionality reduction may be used, with visualisations such as PCA, t-SNE or scatterplots used to explore cluster structure and its relationship with escalation behaviour. Overall, the project assesses the student's ability to independently design a coherent KDD workflow, justify analytical decisions, compare alternative modelling approaches and communicate actionable findings to both technical and executive audiences. Overview word count: approximately 350 words. AI-use note: the brief permits AI tools only to assist with small, specific code snippets. Any AI-generated code must be clearly acknowledged and cited, while the submitted coding, analysis, interpretation and decision-making must remain the student's own work.
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Machine Learning and Deep Learning
2,000 words
Development and Evaluation of Deep Learning Models for Healthcare Classification
This individual technical assessment focuses on the design, development, analysis and evaluation of a deep learning solution for a healthcare-related classification problem. Students select one of two provided scenarios: Polycystic Ovary Syndrome (PCOS) detection using ultrasound images or heartbeat classification using electrocardiogram (ECG) signals. The objective is to develop an appropriate deep learning approach and demonstrate critical understanding of the complete machine learning workflow, from initial data exploration through to model evaluation and reflection. Students may either design and train a deep learning model from scratch or customise and fine-tune an existing pre-trained architecture. The complete work is presented through a single Jupyter Notebook integrating Python code, technical discussion, results and visualisations. The notebook must clearly define the selected healthcare problem, explain its significance, justify methodological and architectural choices, and critically evaluate the resulting solution. The first stage involves exploratory data analysis and preprocessing, including investigation of class distributions, data imbalance and relevant patterns. Students prepare the data through techniques such as normalisation, augmentation, train-validation-test splitting and appropriate handling of class imbalance. This is followed by model design, training, validation and hyperparameter tuning, with the architecture selected according to the characteristics of the data and classification task. Model performance must then be evaluated using appropriate classification measures, including precision, recall, F1-score, ROC curves and area under the curve (AUC). The developed model should also be compared against suitable benchmark approaches, which may include traditional machine learning algorithms or alternative deep learning architectures. This comparison should identify the relative strengths and limitations of the proposed solution. The final component requires clear visual presentation and critical reflection on the complete modelling process, including limitations, challenges and opportunities for improvement. Importantly, grading prioritises methodological rigour, analytical depth and critical evaluation rather than simply achieving the highest predictive accuracy. Overview word count: approximately 330 words. AI restriction: this brief only permits automated AI tools for spelling and grammar checking. It explicitly prohibits tools such as ChatGPT, Gemini or Copilot from authoring assessment text or code; any permitted AI use must also be acknowledged.
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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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Machine Learning / Data Mining / Text Mining
Machine Learning Analysis of Classification Models and Text Mining on Furniture Review Data
This technical machine-learning report demonstrates the practical application of predictive modelling and text mining using WEKA. The work is divided into two major tasks. The first evaluates and compares Support Vector Machine and Decision Tree classification models, while the second applies text-mining techniques to furniture-review data and compares multiple classifiers after preprocessing, feature selection and class balancing. The first task uses the Screenshots.arff dataset to investigate the performance of libSVM and J48 Decision Tree classifiers. A 70% training and 30% testing split is applied, and the models are manually tuned to examine how different parameter settings affect predictive performance. For libSVM, an RBF kernel is used while different gamma and cost values are tested through grid-search-style experimentation. The report identifies gamma 0.03 and cost 2 as the strongest tested combination, producing approximately 91.67% accuracy on the test split. The J48 model is also optimised by adjusting the confidence factor used for pruning. Several confidence-factor values are examined, with 0.09 producing the strongest reported result of 80% accuracy. The optimised SVM and J48 models are then compared using five-fold cross-validation, where libSVM achieves 89.75% accuracy compared with 81.25% for J48. The second task focuses on text mining of Furniture Reviews. Text preprocessing includes TF-IDF term weighting, stopword removal, stemming, conversion to lowercase and word-count generation. The resulting textual dataset is transformed into a numerical feature representation suitable for machine-learning classification. Dimensionality reduction is performed using InfoGainAttributeEval with Ranker, selecting the 900 most informative attributes. The dataset is then balanced using WEKA techniques including Resample and SpreadSubsample to reduce class bias before classification. Finally, three classifiers—Naive Bayes, libSVM and J48—are evaluated on the balanced text dataset. The reported accuracies are 90.52% for Naive Bayes, 58.62% for libSVM and 78.45% for J48. The analysis concludes that Naive Bayes performs strongest for the processed furniture-review dataset, while the wider exercise demonstrates the importance of preprocessing, parameter tuning, feature selection, class balancing and appropriate model evaluation in producing reliable classification results. Important: this upload appears to be the completed student report, not the actual assessment guideline. Because the document does not state the university, module name, academic level, academic year, required word count or prescribed referencing style, I would leave those fields as Not specified rather than guessing.
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Cyber Security / Penetration Testing
2,400 words
Grey-Box Penetration Testing: Vulnerability Assessment, Exploitation and Mitigation
This technical cyber-security project presents an authorised grey-box penetration test conducted within a controlled virtual laboratory environment. The objective is to assess the security posture of a deliberately vulnerable target system, identify weaknesses in exposed network services, demonstrate how those weaknesses could be exploited, evaluate their security and organisational impact, and recommend appropriate mitigation measures. The assessment follows a practical penetration-testing workflow supported by technical evidence, screenshots, activity records and academic research. The project begins with laboratory configuration, network discovery, service enumeration and vulnerability analysis. Tools including Kali Linux, Metasploitable, VMware, Nmap, Netcat and Metasploit are used across the testing lifecycle. Identified services are mapped to known vulnerabilities before controlled exploitation is undertaken and the resulting access is documented. The activity log records the progression from environment setup and network scanning through vulnerability identification, exploitation, evidence collection and final reporting. Five principal attack vectors are examined. These include the vsftpd 2.3.4 FTP backdoor, Samba username-map-script exploitation, an UnrealIRCd backdoor, insecure Java Remote Method Invocation and a misconfigured DistCC service. The practical demonstrations show how vulnerable or incorrectly configured services can permit unauthorised command execution and, in several cases, privileged shell access. For each vulnerability, the report explains the weakness, exploitation process, observed result, security impact and proposed mitigation. Recommended controls include patching or upgrading obsolete services, disabling unnecessary services, implementing firewall restrictions, strengthening authentication and input validation, restricting access to authorised systems, applying least privilege and monitoring suspicious activity. The project also incorporates group management and reflective practice. Team members perform specialised roles covering laboratory configuration, reconnaissance, vulnerability analysis, exploitation and documentation. Individual reflection considers technical performance, teamwork, evidence management and future skills development, demonstrating how structured collaboration contributes to an effective penetration-testing engagement. Important: unlike the earlier assignment briefs, these uploads appear to be completed student/project materials rather than the official 7COM1068 assessment brief. Therefore I would not invent the university, academic level or academic year. If you upload the actual 7COM1068 assignment guideline, I can fill those fields exactly.
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Machine Learning / Data Science
Linear Regression and Stability of the Moore–Penrose Pseudoinverse Using Python
This machine learning practical and assessment activity develops an understanding of linear regression, Ordinary Least Squares and the Moore–Penrose pseudoinverse using Python. The work progresses from generating synthetic regression datasets to implementing regression algorithms manually, applying established machine learning libraries, analysing real datasets and evaluating the stability of estimated regression coefficients. The laboratory component begins with the generation of synthetic linear regression data using NumPy, including explanatory variables, random noise and an outcome variable. Students then implement simple linear regression without relying on machine learning libraries, using the least-squares solution to estimate the intercept and slope. The resulting observations and fitted regression line are visualised using Matplotlib. The work is subsequently extended to multiple linear regression, where several independent variables are used and coefficients are first calculated manually before the same problem is solved using Scikit-learn. The laboratory also introduces application of regression to the Scikit-learn Diabetes dataset, including feature and target standardisation, model fitting, prediction, correlation analysis and interpretation of regression coefficients. It also highlights the importance of residual analysis when assessing whether a linear model is appropriate. The associated weekly challenge focuses on the stability of linear regression solutions estimated using the Moore–Penrose pseudoinverse. Using a house-price dataset containing variables such as property size, number of bedrooms, distance from the city centre and property age, students construct the design matrix, standardise features and the response variable, and calculate regression coefficients using the pseudoinverse. Students then investigate model robustness by repeatedly fitting the regression model to random subsamples of different sizes and analysing the mean and standard deviation of each coefficient. Tables, boxplots or error-bar visualisations can be used to compare coefficient variability. The final discussion considers which variables are most influential, which coefficients are most stable, how sample size affects stability and whether coefficient interpretation remains reliable across different samples. The final work is submitted as a single PDF exported from Jupyter Notebook or Google Colab, combining documented Python code, experimental results, plots and written interpretation in a professionally organised notebook. Overview word count: approximately 360 wor
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Business and Management
34,000 words
Examining the Role of Cross-Cultural Communication in Enhancing Team Efficiency: Evidence from McDonald’s Multicultural Workforce in London
This dissertation examines the role of cross-cultural communication in enhancing team efficiency within McDonald’s multicultural workforce, with the study framed around the challenges and opportunities created by culturally diverse working environments. The research considers how differences in language, communication styles, cultural expectations and workplace behaviour can influence employee collaboration, productivity and organisational effectiveness. The study aims to assess cross-cultural communication within multicultural teams, analyse its relationship with team success, and identify cultural factors that can support stronger workplace collaboration. The research adopts a qualitative secondary-research methodology under an interpretivist and inductive approach. Evidence is drawn from peer-reviewed academic literature, McDonald’s annual and diversity reports, and relevant hospitality-sector studies. The collected evidence is examined through thematic analysis to identify recurring patterns relating to communication barriers, workforce productivity, multicultural collaboration and inclusive leadership. The analysis is organised around four principal themes: cross-cultural diversity and workforce productivity; communication barriers and cultural challenges; diversity management and inclusive leadership; and HR practices, employee motivation and organisational performance. The discussion considers issues such as language barriers, cultural adjustment, misunderstandings, workplace conflict, leadership representation, employee inclusion, recruitment, training, digital HR systems and the use of AI-enabled workforce technologies. The study finds that workforce diversity alone does not automatically generate higher productivity. Instead, the effectiveness of multicultural teams depends substantially on the communication structures, inclusive leadership practices and HR support systems used by the organisation. Effective cross-cultural communication can strengthen coordination, customer responsiveness, employee engagement and operational efficiency, while poorly managed communication differences can contribute to delays, misunderstanding, stress and conflict. The report concludes with recommendations for continued cross-cultural communication training, inclusive leadership development, conflict-resolution initiatives and multilingual communication support. It also recognises the limitation of relying on secondary evidence and identifies primary research with employees and managers as a potential direction for future research. Overview word count: approximately 350 words.
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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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Management / Digital Transformation and Leadership
1,500 words
Leading Through Digital Change: Digital Transformation Report and Future Technology Poster
This Masters-level assessment for the Leading Through Digital Change module examines how organisations can respond strategically and effectively to rapid technological and digital transformation. Students take the role of a Digital Transformation Manager for one selected international organisation and prepare a professional Digital Transformation Report accompanied by an A4 digital poster. The purpose is to evaluate the organisation's current digital context and recommend changes that can strengthen competitive advantage and create sustainable business value. The first component requires critical evaluation and recommendation of one appropriate digital transformation strategic framework. Students may apply frameworks such as the McKinsey 4Ds, BCG Three Stages, Gartner's Six Steps or Cognizant's Four Pillars. The analysis should establish clear digital transformation objectives relevant to organisational functions such as operations, ICT and marketing, while using organisational evidence, academic research and practical examples to justify the proposed strategic direction. The second component is an academic poster evaluating two disruptive technologies or techniques expected to affect the chosen organisation, its industry, employment and the labour market over the next five years. Potential technologies include Artificial Intelligence and Machine Learning, 5G connectivity, the Internet of Things, robotics, drone delivery, blockchain, augmented reality and virtual reality. The poster should combine academic literature with real-world examples to demonstrate the likely opportunities, challenges and wider organisational implications of technological disruption. The final component focuses on digital leadership. Students analyse and recommend two suitable leadership approaches for managing and supporting digital transformation. Relevant approaches may include agile leadership, ethical-tech leadership, people-oriented leadership, hyperaware agile leadership and Goleman's leadership styles. Overall, the assessment integrates digital strategy, innovation, emerging technologies and leadership. The wider module also covers digital transformation strategies, data-driven decision-making, leadership in the digital age, artificial intelligence in contemporary business, digital risk management and planning for the future. Reference style: Harvard. Main report word limit: 1,500 words. Poster: A4 size with no specified word count.
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Human Resource Management / People Practice
5,500 words
Essentials of People Practice: Recruitment, Employment Relations, Performance, Reward and Learning
This comprehensive CIPD Level 3 assessment examines the principal operational areas of people practice across the employee lifecycle. The unit covers recruitment and selection, employment legislation and employee relations, wellbeing and inclusion, performance management, reward, learning and development, and the practical application of people-profession knowledge. The assessment uses the fictional insurer Jemijo, which employs more than 2,500 people across office, home and hybrid working arrangements. Much of its workforce operates within customer-service and insurance call-centre environments, including a 24/7 emergency claims service. The first task addresses recruitment and selection. Learners examine the employee lifecycle, job analysis, job descriptions, person specifications, recruitment through corporate websites and commercial job boards, structured interviews, assessment centres and appropriate recruitment records. It also includes the use and critical review of AI-generated appointment and non-appointment letters. Written responses for the main questions total approximately 1,500 words. A separate practical task requires learners to devise selection criteria, shortlist candidates and conduct a recorded one-to-one simulated interview for a People Assistant vacancy. Further written tasks examine employment law, working time, employee wellbeing, discrimination, diversity and inclusion, and fair dismissal. This section is approximately 1,250 words. Performance and reward topics then cover objective setting, motivation, continuous performance reviews, total reward, non-financial reward and equitable pay, with approximately 1,500 words allocated. The final component addresses learning and development, including induction and training benefits, learning needs, face-to-face and blended learning, coaching, mentoring, accessibility and evaluation of training effectiveness. Approximately another 1,250 words is allocated to this section. This produces approximately 5,500 assessed written words, alongside additional practical evidence that is excluded from the formal word count. Important for your portal: I would not select “Masters” just because the assignment-type options say “MS”. These are explicitly CIPD Level 3 Foundation Certificate assessments, so Academic level = Not specified is the accurate choice with the options your system currently provides.
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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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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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Computer Science / Database Systems
4,000 words
Advanced Databases (KL7011) — NORTHERNTOURS Coach Travel Database: EER Design, Oracle Implementation, Object-Relational and NoSQL Extensions
This piece of work addresses a four-part Masters-level assessment in Advanced Databases built around NORTHERNTOURS, a fictitious coach travel operator running services across cities, towns and tourist sites in the North East of England. The company sells tickets through a network of independent travel agents, each currently working from a paper-based sales book, while NORTHERNTOURS itself maintains separate paper records for routes, schedules, seat availability, vehicles and drivers. The brief asks for a single computer-based system capable of replacing both, tracking every agent transaction while giving the company control over ticket issue and seat allocation. Part one covers the conceptual and logical design. An enhanced entity-relationship model was produced covering agents, agent employees, customers, tickets, routes, stops, schedules, vehicles, drivers and the meal provision recorded at each stop, with key attributes, primary keys and full structural constraints shown. Because the scenario does not name identifiers for most entity types, appropriate surrogate and natural keys were devised and justified. The diagram was then mapped to a logical relational schema, normalised to third normal form, with a documented naming convention applied consistently across relations, attributes and keys, and every element recorded in a text-based data dictionary giving names, data types, descriptions and constraints. Part two moves to implementation in Oracle. A full DDL script creates the relations with primary and foreign keys and a substantial set of check constraints — key format patterns, positive seat counts and fare values, date ordering on schedules. Sample data populates the relevant tables, and two retrieval problems are answered twice over, once in relational algebra and once in SQL: schedules between Newcastle and Berwick-upon-Tweed with seven or more seats free in the coming fortnight, and the agent with the highest ticket sales across a defined month. Spooled session output evidences each script running. Part three revisits the conceptual design to argue where object-relational features earn their place — nested route-and-stop structures and composite address and contact types being the clearest candidates — implemented using Oracle object types, VARRAYs and nested tables, and demonstrated through two multi-join aggregate queries. A parallel discussion identifies the schedule and availability workload as a fit for document-oriented NoSQL storage, with representative code and a reasoned account of the denormalisation trade-offs involved. Part four is a report to the managing director covering sustainability, professional, legal, ethical and security obligations, alongside diversity, inclusion, cultural and environmental matters, commercial risk evaluation and mitigation, supported throughout by current literature and published standards.
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Engineering and Professional Practice
3,000 words
Internship Reflective Report and Poster Presentation — Professional Practice in an Industry Placement
An internship module assessment asks students to convert a period of workplace experience into evidenced academic reflection. It is not a report on what the host organisation does; it is an account of what changed in the student's own understanding of their discipline as a result of working outside the taught programme. The written component typically runs to around three thousand words and is built in weighted sections. An opening section covers how the placement was secured — the reasoning behind the choice, the search and application process, and what that process itself taught the student. The bulk of the marks then sit on the reflection proper: what the student actually did, how existing disciplinary knowledge held up when applied in an unfamiliar setting, and where gaps appeared. A final section addresses personal and professional development, supported by specific examples rather than general claims of having "grown in confidence." The second component is a short poster presentation summarising the placement and its learning, delivered live. Posters are assessed on professional presentation, clarity, correct referencing and balance — students commonly over-fill them with text that a viewer cannot read at presentation distance. Formatting requirements on this type of assessment are unusually prescriptive and carry marks: a cover page with name, student ID, tutor name and declared word count; a table of contents; page numbers; captions on every figure and table; specified font, size and line spacing; numbered headings; and a strict file-naming convention. Marks are lost here for no reason other than inattention. Note also that in-text citations and quotations usually count toward the word limit even when tables and references do not, and markers may simply stop reading once the limit is exceeded by more than the allowed margin. The defining challenge of reflective assessment at this level is criticality. Rubrics consistently distinguish description from evaluation: recounting tasks performed scores at the lower bands, while analysing why something was difficult, what it revealed about a gap in preparation, and what will be done differently scores at the top. Reflective frameworks give this structure, but they must organise genuine experience rather than substitute for it. This assessment is inherently personal — the content derives entirely from the student's own placement. Our support is therefore confined to guidance: explaining what distinguishes descriptive from critical reflection with worked examples, showing how a reflective framework structures a section, advising on poster layout and information density, checking formatting and referencing against the specification, and reviewing a student's own completed draft against the published rubric.
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Operations and Supply Chain Management
1,800 words
Strategic Sourcing and Supply Chain Resilience Under Global Uncertainty — A Firm-Level Critical Evaluation
This type of postgraduate coursework asks students to take a single real firm — a British company, or a multinational with substantial UK operations — and critically evaluate how its sourcing and supply chain strategy has adapted to a decade of sustained disruption: the pandemic, geopolitical conflict, tariff volatility, and departure from the European single market. The analytical scope is broad but the depth expectation is narrow. Students choose one or two themes rather than surveying all of them: insourcing versus outsourcing and location decisions; single versus multi-sourcing across supplier segments or regions; how supplier selection and monitoring criteria have shifted toward reliability and geographic proximity; alignment between sourcing decisions and wider corporate objectives; supplier and customer relationship management under cross-border friction; inventory tactics such as stockpiling and safety stock repositioning; digital adoption for visibility and compliance; sustainable sourcing under cost pressure; or risk management around currency, customs and compliance. The evidence base is deliberately mixed. Alongside academic literature, students are expected to draw on annual reports, shareholder briefings, company statements, practitioner journals and recent news coverage — with a minimum spread of credible sources across both categories, and some of the non-academic material drawn from the most recent year so the analysis reflects current firm behaviour rather than historical commentary. At least one theoretical framework from the module or the wider literature must be applied to the firm's decisions, and at least one recent documented practice must be examined with specific sourcing. Two requirements distinguish strong submissions. The first is criticality: the brief explicitly separates evaluation from description, and marks weight critical analysis most heavily. Reporting what a firm did is not the task; assessing whether it was the right response, and what opportunities and risks it created, is. The second is the recommendation — at least one specific, actionable proposal with steps, anticipated obstacles, expected outcomes, and a clear line back to the firm's own stated goals. A generic suggestion to "diversify suppliers" fails this test; a costed, sequenced proposal grounded in the firm's actual constraints does not. Word-count conventions are stricter than students often expect: in-text references, tables, illustrations and front matter typically count, while appendices and the reference list do not. Our support on assessments of this type is guidance-based. Typical areas of help include: narrowing a firm and theme so the analysis fits the word limit, explaining the difference between description and critical evaluation with worked examples, clarifying how a theoretical framework should structure an argument rather than sit decoratively in a paragraph, checking source mix and Harvard referencing consistency, and reviewing a completed draft against the published assessment weightings.
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