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Marketing / Consumer Marketing
1,987 words
MindBand: A Creative Marketing Plan for a Mental Wellbeing Wearable in the UK Smart Device Market
This postgraduate marketing report develops a creative marketing plan for MindBand, a proposed smart wearable designed to support mental wellbeing and emotional regulation in the UK wearable-device market. The assessment responds to a brief requiring students to identify an unmet product need, create an original product concept and apply strategic marketing principles to establish how the innovation could attract consumers in a highly competitive smart-device industry. Reassessment-Individual Assignm… The analysis identifies a potential gap between mainstream fitness-focused wearables and consumers seeking discreet, everyday support for stress, emotional wellbeing and cognitive fatigue. MindBand is positioned as a minimalist wrist-worn device that uses biometric indicators such as heart-rate variability, skin conductance and temperature to identify stress-related patterns and provide context-sensitive interventions. Unlike conventional wearables that primarily display performance data, the concept emphasises behavioural support, simplicity and low-effort interaction. Creative Marketing Plan for a P… Creative Marketing Plan for a P… The marketing plan targets UK professionals aged approximately 30–55, particularly individuals experiencing high cognitive workloads, digital fatigue and work-life pressures. The proposed value proposition focuses on personalised emotional support, discretion and ease of use rather than extensive fitness functionality. This positioning is reinforced through a calm, trust-oriented brand identity intended to distinguish MindBand from performance-led smartwatch and fitness-tracker brands. Creative Marketing Plan for a P… Creative Marketing Plan for a P… The communications strategy adopts a digital-first approach, using educational content, podcasts, professional experts, thought leadership, paid media and customer testimonials to build credibility and awareness. Distribution is primarily direct-to-consumer through e-commerce, supplemented by partnerships with corporate wellbeing programmes and healthcare providers. A premium-value pricing strategy and optional subscription-based services are proposed to support recurring revenue and continued product development. Creative Marketing Plan for a P… The report also considers performance measurement, brand equity, customer retention, privacy, informed consent and responsible use of biometric data. Overall, the work integrates product innovation, consumer behaviour, segmentation, positioning, communications, pricing, distribution and ethical marketing into a coherent smart-wearable marketing proposal.
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Artificial Intelligence / International Business
3,000 words
AI Innovation Consultancy: Evaluating Artificial Intelligence Solutions for Business Problems
This individual consultancy assessment requires students to act as an AI Innovation Consultant and evaluate how artificial intelligence could address a significant real-world business problem. Students select one industry—such as healthcare, retail, FinTech, manufacturing or agriculture—and concentrate on a single clearly defined organisational challenge rather than comparing multiple sectors. Potential issues include long waiting times, high operating costs, fraud and risk, poor customer experience or inefficient supply chains. The report develops a practical AI solution by identifying suitable technologies such as machine learning, natural language processing or computer vision and explaining how they would operate within the chosen organisational context. Students are not required to build an AI system; instead, the emphasis is on demonstrating business-level technical understanding, critical thinking and the ability to assess whether the proposed technology can realistically integrate with existing organisational processes. The analysis considers the capabilities and limitations of AI, technical feasibility, integration requirements and the skills or organisational capabilities required for implementation. Students must also critically examine ethical, legal and social implications, including issues such as algorithmic bias, transparency, accountability, privacy and regulatory obligations such as UK GDPR. Appropriate risk-mitigation measures should be proposed. A substantial element of the report develops the business case for AI adoption. Students evaluate implementation costs and expected benefits, estimate return on investment, identify assumptions and commercial risks, and assess the overall strategic value of the solution to the organisation. The report concludes with clear recommendations, implementation priorities and a final judgement on whether the proposed AI initiative is feasible and worthwhile. The assessment places strong emphasis on critical analysis, technical understanding, business acumen and professional communication. Students are expected to support arguments with credible academic, industry and government evidence and include at least two professional visualisations such as frameworks, diagrams or tables. Harvard referencing is required throughout. Overview word count: approximately 330 words. The brief also allows authorised use of generative AI for idea generation, drafting/structuring and proofreading, provided the student verifies accuracy, references appropriately and submits the required GenAI declaration.
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Cyber Security / Penetration Testing
Web Application Penetration Testing and Security Vulnerability Assessment Portfolio
This postgraduate cyber security portfolio requires students to conduct a structured penetration test of a controlled web application and document the technical findings in a professional security-testing format. The assessment develops practical competence in identifying, validating and communicating security vulnerabilities while maintaining appropriate legal, ethical and professional boundaries. Assessment Brief CMP-L021 (PG) … Students begin by performing network and service enumeration, identifying open ports and the services running on the target host. They are expected to interpret the security implications of the findings and provide appropriate recommendations to a hypothetical client. The assessment then progresses into web-application vulnerability testing using tools such as a web browser, Burp Suite Community, Nmap and student-developed scripts. Assessment Brief CMP-L021 (PG) … A major part of the portfolio examines common web-security weaknesses including SQL Injection and Cross-Site Scripting. Students must demonstrate how they tested the application, capture relevant requests and responses, and explain the evidence supporting their conclusions. Additional tasks involve application and server reconnaissance, including identification of technologies, server versions, publicly exposed files and other information that may create security risks. Assessment Brief CMP-L021 (PG) … The higher-level reporting component requires students to document significant vulnerabilities using the conventions of a professional penetration-test report. This includes assigning CVSS scores, relating identified weaknesses to the OWASP Top 10 and NIST classifications, and supporting findings with appropriate technical evidence. Assessment Brief CMP-L021 (PG) … Students must also produce an executive summary for a non-technical audience, considering security, privacy, regulatory exposure and budget implications. A vulnerability table linking technical weaknesses with relevant regulatory concerns is also required. Overall, the assessment integrates technical penetration testing with risk communication, vulnerability classification, evidence collection and professional security reporting. Assessment Brief CMP-L021 (PG) … Overview word count: approximately 320 words. AI-use note: AI can be used in this assessment, but any use must be acknowledged and AI-generated outputs must be appropriately cited. Assessment Brief CMP-L021 (PG) …
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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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Data Science / Data Management / Professional Practice
4,500 words
Tesla Data Science Professional Case Study: Data Management, Leadership, Entrepreneurship and Ethics
This composite case-study assessment for The Data Science Professional module requires students to critically analyse Tesla from several interconnected professional perspectives, including data management, artificial intelligence ethics, leadership, organisational development, entrepreneurship and business risk. The coursework is designed to combine technical data-science capability with strategic, ethical and managerial decision-making. a82a1aa6a2e7a3268ad021c4f5b3d44… Part A – Database Design and Distributed Frameworks focuses on data management. Students design an Entity-Relationship model and relational schema for a Tesla-related vehicle-hire business, identifying entities, relationships, cardinalities, identifiers, primary keys and foreign keys. The accompanying guidance specifies entities relating to vehicles, employees, outlets, clients, hire agreements, insurance, faults and employment records. a82a1aa6a2e7a3268ad021c4f5b3d44… 39cdc24cc46a13ddf444b8e7af838eb… The SQL component uses an Online Music database to examine Tesla customer preferences. Students create relational tables using Oracle standard SQL and write queries involving users, music, publishers, categories and download records. Evidence of implementation and query results must be provided using Oracle Live SQL. 39cdc24cc46a13ddf444b8e7af838eb… Part A also requires a critical assessment of the security, privacy and ethical implications of Tesla’s Full Self-Driving technology, connecting technical development with responsible data and AI practice. a82a1aa6a2e7a3268ad021c4f5b3d44… Part B – Leadership and Developing People requires critical evaluation of Tesla’s leadership model, organisational culture and their effects on employees and organisational performance. Students must propose a leadership and people-development strategy capable of supporting the organisation as it expands. a82a1aa6a2e7a3268ad021c4f5b3d44… Part C – Entrepreneurial Practice and Managing Risk examines a proposed Tesla spin-out venture developing innovative low-cost green hydrogen production systems. Students critically assess management support for the venture, propose an evidence-based approach to entrepreneurial risk, develop an entrepreneurial leadership role descriptor, and evaluate how GDPR and AI/data ethics may support or constrain entrepreneurial practice. a82a1aa6a2e7a3268ad021c4f5b3d44… Overall, the assessment integrates technical database design, SQL, data ethics, organisational leadership, entrepreneurship, sustainability and professional decision-making within a single Tesla-focused case study. Overview word count: approximately 360 words. Important: the assessment brief itself explicitly states that it must not be passed to third parties or posted on any website. So for a public Reference Library, use the metadata and your own finished work where permitted, but do not upload the assessment brief/guidance PDFs themselves publicly. a82a1aa6a2e7a3268ad021c4f5b3d44… The AI status is Amber: generative AI may be used for limited inspiring/planning purposes, but usage must be acknowledged with the tool, prompts and relevant evidence; the brief also specifically prohibits using LLMs to generate the Part A(3) essay. a82a1aa6a2e7a3268ad021c4f5b3d44… a82a1aa6a2e7a3268ad021c4f5b3d44…
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Data Science / Data Management / Leadership and Entrepreneurship
4,500 words
The Data Science Professional: Tesla Data Management, Leadership and Entrepreneurial Practice
This interdisciplinary Data Science Professional assessment uses Tesla as the central organisational case study to examine how data management, leadership, entrepreneurship, technology and ethics interact within a contemporary technology-driven business. Students produce an individual report of 4,500 words equivalent, combining technical data-science competencies with strategic and managerial analysis. The Data Science Professional A… The Data Science Professional A… Part A – Database Design and Distributed Frameworks focuses on data management. Students design an Entity-Relationship model for Tesla-related organisational data, translate the model into a relational schema and justify the database structure. They also work with a specified database to produce Oracle SQL queries for analytical requirements. A further component critically evaluates the security, privacy and ethical implications of Tesla's Full Self-Driving technology, supported by relevant academic evidence. The Data Science Professional A… Part B – Leadership and Developing People requires critical analysis of Tesla's leadership model and organisational culture. Students examine current leadership challenges and their implications for organisational and employee performance before proposing a leadership and people-development strategy designed to support future organisational growth, employee engagement and sustained performance. The Data Science Professional A… Part C – Entrepreneurial Practice and Managing Risk investigates the proposed creation of a Tesla spin-out venture focused on innovative, low-cost green hydrogen production systems. Students critically evaluate management support for the entrepreneurial initiative and recommend an evidence-based course of action. They must also propose a multidimensional approach to mitigating entrepreneurial risks and barriers, critically assess entrepreneurial leadership characteristics, and develop a role descriptor for the person who would lead the new venture. The Data Science Professional A… The final element examines whether GDPR and data or AI ethics constrain or support entrepreneurial practice among employees. Overall, the coursework integrates database modelling, SQL, data governance, leadership development, organisational culture, entrepreneurship, innovation, risk management and ethical decision-making within a single applied case study. The Data Science Professional A… Important for the public Reference Library: the 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. The Data Science Professional A… So publish an original high-level overview like the one above, but do not upload the original assessment brief itself to the public library.
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Artificial Intelligence / Machine Vision / Computer Vision
Artificial Intelligence and Machine Vision: Neural Network-Based Image Processing Application
This postgraduate Artificial Intelligence and Machine Vision coursework requires students to design, implement and critically evaluate a neural network-based image-processing application addressing a real-world problem. Students may select an application such as medical imaging, plant or fruit classification, skin cancer detection, object detection or image segmentation and must demonstrate an appropriate end-to-end machine vision workflow. CN7023 Coursework T2 25-26 (1) The project begins with a clear definition of the selected real-world problem, the objectives of the proposed solution and its potential practical impact. Students are expected to demonstrate creativity in selecting and designing their approach, explain the neural-network or image-processing methods adopted and justify why the selected techniques are suitable for the chosen dataset and application. CN7023 Coursework T2 25-26 (1) A substantial component focuses on simulation and implementation. Students describe the dataset, including its source, size, classes and representative images, before explaining how image data were encoded and preprocessed for use within the chosen neural network. The report must then document the selected network architecture, learning algorithm and procedures used for training, validation and testing. CN7023 Coursework T2 25-26 (1) Model performance must be communicated using quantitative and visual evidence. Required outputs include test-set accuracy, accuracy curves across training, validation and testing, and a confusion matrix supported by appropriate explanation. Students must also critically analyse the results, identify factors affecting model performance and discuss alternative methods or simulation changes that could improve the solution. CN7023 Coursework T2 25-26 (1) The coursework permits several technical routes, including combining image processing with artificial neural networks, deep learning or computer vision, or focusing on one of these approaches independently. Development may be completed using MATLAB or Python. The wider module covers artificial neural networks, CNNs, digital image processing, image restoration, compression, segmentation, classification and ethical, legal, privacy and social issues associated with AI systems. CN7023 Coursework T2 25-26 (1) Module handbook 2526-B (1) Overview word count: approximately 335 words. Important note: the coursework cover page labels the assignment as “Individual Assignment 100%,” but the module handbook clarifies that the coursework report itself contributes 50% of the module, with the remaining marks allocated to MATLAB course completion (20%), lab tasks (15%) and presentation (15%). For the Reference Library, I would use the handbook’s 50% report weighting if you need to record the assessment contribution.
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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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Machine Learning / Cloud Computing
2,000 words
Technical Evaluation and Professional Reflection on a Cloud-Based Machine Learning Fraud Detection Solution
This postgraduate individual assessment critically evaluates a cloud-based machine learning solution for financial fraud detection developed as part of a preceding group project. The scenario concerns a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and strengthen customer security. The individual report requires students to examine both the technical quality of the developed solution and their own professional contribution to the project. NUL - LD7187 -Assessment Brief … The first component, Technical Evaluation, accounts for 50% of the assessment and has a suggested allocation of approximately 1,000 words. Students critically analyse the group solution with particular attention to data preprocessing, machine learning model choices, evaluation metrics and technical limitations. The analysis should go beyond description by explaining how specific modelling and design decisions influenced the final outcomes of the fraud-detection system. NUL - LD7187 -Assessment Brief … The second component, Reflection and Professional Issues, also accounts for 50% and is approximately 1,000 words. Students critically reflect on their personal contribution, teamwork experience, challenges encountered and lessons learned during the project. The discussion also addresses wider professional and ethical considerations associated with AI and cloud-based machine learning, including bias, fairness, sustainability and data privacy. NUL - LD7187 -Assessment Brief … The assessment is designed to demonstrate critical understanding of machine learning methods, cloud-computing architectures, practical development of machine learning solutions and awareness of the social, ethical and sustainability implications of AI technologies. A Seminar Activity Tracker must also be included as an appendix to provide evidence of weekly participation and knowledge development. NUL - LD7187 -Assessment Brief … NUL - LD7187 -Assessment Brief … Higher-level performance requires a comprehensive connection between technical decisions and outcomes alongside deep reflection on teamwork, professional development, ethics, fairness and sustainability. NUL - LD7187 -Assessment Brief … Overview word count: approximately 300 words. AI-use note: the brief permits AI for limited support such as grammar improvement, structure, organising ideas and suggestions. The student's main content, analysis and conclusions must remain their own, and any AI use must be declared. NUL - LD7187 -Assessment Brief …
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Cyber Security
Contextual Risk Assessment and Policy to Address Information Security within Supplier Agreements
This assignment focuses on the development of a contextual risk assessment and an information security policy addressing security requirements within supplier agreements for Heathrow Airport Holdings (LHR). The assessment is an individual postgraduate task worth 60% of the module and requires students to apply information security risk assessment methods, security standards and policy development techniques to a realistic organisational scenario. The assignment is based on a cyber-attack affecting Heathrow and other European airports in September 2025, where disruption to a third-party cloud-based check-in and baggage system affected airport and airline operations. The scenario highlights the security risks associated with interconnected systems, third-party suppliers and dependence on critical digital services. Students are required to assume the role of a new Chief Information Security Officer (CISO) at Heathrow Airport Holdings and investigate the organisation, its environment and the relevant threat landscape. The task requires the development of a clear organisational context, including appropriate assumptions, followed by an asset-based information security risk assessment. The risk assessment should identify and prioritise relevant risks and support the selection of controls needed to manage residual information security risks. The main policy component requires students to develop an “Information Security within Supplier Agreements” policy aligned with the ISO 27000 family. The policy should establish clear security responsibilities between LHR and its suppliers and address the protection of information assets, legal and regulatory requirements, and supplier-related security obligations. Particular attention is required for confidentiality, integrity and availability, together with ISO 27002 controls relating to information security policies and supplier agreements. The assignment also requires consideration of acceptable use of information and other assets, information classification and information labelling. The final submission consists of a cover page, context establishment, an asset-based risk assessment, the supplier information security policy, references and supporting appendices. The context establishment is limited to a maximum of two pages or 1,000 words, while the policy is limited to three pages or 1,500 words. The risk assessment is completed using the supplied template. Students are also required to provide evidence and commentary concerning the development and tailoring of the policy when using an approved AI tool, together with a self-written evaluation addressing strengths, weaknesses, privacy, GDPR and ethical considerations. The assessment is marked across context establishment, asset-based risk assessment, the information security within supplier agreements policy, and presentation, design and references. At least 20 authentic references, including standards and papers accessed through the University library, are required.
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Data Mining
Data Mining – Classification, Model Optimisation and Evaluation
This individual Data Mining assignment requires students to apply the techniques covered in the module using the WEKA data mining platform. The assessment is worth 40% and focuses on practical application of machine learning and data mining methods, requiring students to configure algorithms, analyse datasets, optimise model parameters, evaluate classification performance and explain their technical choices and results. The assignment also assesses the ability to critically evaluate different algorithms and models of data mining. The assignment includes several tasks covering different stages of the data mining process. Students are required to work with supplied datasets and use appropriate preprocessing and classification techniques. The datasets include a balanced screenshot dataset containing processed screenshots classified into categories such as "Okay" and "Bad", where the objective is to train a model capable of identifying inappropriate content. The data has been processed using PCA to provide a smaller four-dimensional representation while protecting privacy and reducing the size of the data. Another supplied dataset concerns furniture reviews, containing positive ("pos") and negative ("neg") written feedback, with the objective of building a model that can determine whether a furniture review belongs to either class. The assessment evaluates students' ability to understand and describe the datasets, including the number of instances, number of columns, data types and relevant statistical information. For text-based data, students must apply appropriate vectorisation and describe the resulting dataset characteristics. Students must also consider class imbalance and apply an appropriate method where necessary, explaining how the chosen approach affects the distribution of instances between the classes. A significant component of the assignment involves classification algorithms and parameter optimisation. The assessment requires students to work with algorithms including Naive Bayes, LibSVM and J48. Students must investigate appropriate parameters and perform parameter searches or fine grid searches to identify suitable configurations. They must explain the selected parameters, their impact on the model and the reasoning behind the chosen values. Model performance must be evaluated using appropriate validation techniques, including cross-validation. Students are required to compare the algorithms using results such as overall accuracy and confusion matrices. The assignment expects students to identify an appropriate or best-performing algorithm in the context of the dataset and to provide a clear explanation of the comparison rather than simply reporting numerical results. The rubric places emphasis on accurate configuration, clear explanation of parameter choices, dataset analysis, class-balance treatment, parameter optimisation, cross-validation and critical comparison of algorithm strengths and weaknesses. High-quality work should explain both the technical process and the implications of the results, with results presented clearly through appropriate tables, confusion matrices and graphical outputs where required. The submission must be a single PDF document containing the report and must not exceed 10 pages. Students are instructed to include their student ID at the beginning of the report but not their name or other identifying details so that marking remains anonymous. Screenshots are specifically required to demonstrate use of the student's ID number as the random seed; other WEKA results should be presented in the student's own tables or result formats. The brief also states that no research beyond the material covered in the module is required and therefore no citations or reference list are required. The assignment explicitly prohibits the use of Generative AI tools for creating content and prohibits using GenAI tools or proofreading services for proofreading. Students are expected to complete the practical work themselves and explain their own technical choices and results.
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Security of Emerging Connected Systems
1,500 words
CW1: Policy and Legal Aspects Report – IoT System
This 1,500-word report for the Security of Emerging Connected Systems module examines the legal and security implications of a proposed Internet of Things (IoT) system designed for consumers to monitor food intake and bodyweight. The coursework requires students to provide an initial investigation of the potential legal pitfalls associated with the proposed product and identify appropriate solutions or mitigation measures. The report is worth 5 credits and is assessed as an individual written report. The proposed IoT system consists of several connected components. A smartphone application allows users to scan barcodes of processed food to record calorie and nutritional information against their health record. A kitchen scale communicates with the phone application to record the weight of ingredients used in home-cooked meals. A bathroom scale records the user's weight and provides light and sound reminders to encourage regular measurements. A UK-based server stores the collected information and generates individual user reports. The main purpose of the report is to ensure that the company understands the UK and international laws that may apply to the proposed system. Students must identify potential legal issues and recommend appropriate mitigation through technology, organisational policy, licensing arrangements or user agreements. The system definition is deliberately broad, so students should not make unsupported assumptions about its design. Where several options have different legal implications, the report should compare the relevant alternatives and explain their implications rather than presenting only one solution. The initial product is intended for UK residents, while the company is considering future expansion into the United States. Consequently, the report should focus primarily on UK law but also include a short section discussing legal aspects that may need to be reconsidered when entering the US market. The report is intended for company executives and may subsequently be provided to the R&D department. Therefore, high-level outcomes should be communicated early, while useful links to technical information such as encryption schemes, protocols and frameworks may be provided without extensive technical explanations in the main report. The assessment places 50% of the marks on understanding and coverage of UK and US law, 40% on technical recommendations and 10% on report presentation. Strong submissions are expected to provide comprehensive coverage of relevant legislation, connect legal issues with the wider security context, analyse technical recommendations for both regions, identify differences between UK and US requirements and support arguments with appropriate citations and a wide range of sources. The assignment learning outcomes focus on critically evaluating the role of security policy in protecting information assets and proposing appropriate policies for internet-based technologies. They also require students to demonstrate an understanding of key legislation relating to information security and how legislation influences organisational security policy. The final report should therefore combine legal analysis with practical security recommendations, addressing the proposed IoT system from both UK and US perspectives while remaining suitable for both technical staff and non-technical management.
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Digital Data Acquisition, Recovery and Analysis
1,500 words
Digital Data Acquisition, Recovery and Analysis – Autonomous Vehicle Forensics
This individual coursework for the Digital Data Acquisition, Recovery and Analysis module requires students to produce a 1,500-word technical research paper critically investigating autonomous vehicle (AV) forensics. The assignment focuses on the challenges, methodologies and tools involved in extracting, preserving, analysing and interpreting digital evidence from autonomous vehicles and their associated systems. The work specifically considers how forensic evidence can be used to reconstruct events and establish accountability following accidents, security breaches or system malfunctions. The assignment examines the increasing importance of digital evidence generated by intelligent transportation systems and smart vehicles. Students are expected to consider data produced by different sources within an autonomous vehicle ecosystem, including LiDAR, radar and GPS sensors, vehicle control units and connected infrastructure. The report should explore how these different forms of information can be collected and forensically preserved while maintaining their evidential value for subsequent investigation and analysis. The coursework requires a detailed technical analysis rather than a general overview of autonomous vehicles or digital forensics. Students are expected to engage with academic literature, industry frameworks and technical examples, while providing critical insights into the subject. The report should analyse the practical challenges associated with AV forensics and critically examine the tools and methodologies that can be applied to obtain and interpret evidence from autonomous vehicle environments. Legal, ethical and privacy considerations form an important part of the assignment. The report should examine issues surrounding the use of autonomous vehicle evidence, including legal responsibility, regulatory considerations, privacy implications and ethical challenges associated with collecting and analysing potentially sensitive vehicle and user data. These considerations should be connected to the wider forensic investigation process and the reliability and admissibility of digital evidence. The required report should follow an academic research-paper structure, including a cover page, abstract, keywords, table of contents, clearly organised sections and subsections, references and an appendix where required. The brief requires APA referencing and a reference list at the end of the paper. Students are expected to use their own words and critically analyse the literature rather than simply summarising existing research. The assessment evaluates five equally weighted areas: structure and presentation with supporting references; balance, objectivity, critical evaluation and original insight; identification of AV-forensics challenges and quality of analysis; analysis of tools and methodologies used in AV forensics; and understanding of AV forensics together with its legal, professional and ethical considerations. Each area contributes 20% to the assessment.
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Programming for Data Science
4,000 words
Programming for Data Science – Individual Portfolio
This individual portfolio assessment for the Programming for Data Science module at Coventry University consists of four tasks designed to assess programming, debugging, data science, data visualisation, data protection and data ethics skills. The assessment carries 20 credits and has a total value of 4,000 words equivalent, excluding the reference list and output. Students are required to submit one clearly organised report containing all four tasks, with each task beginning on a new page. Python code, outputs and relevant plots must be included directly within the report. Task 1 focuses on analysing, critiquing and debugging Python code. Students are required to identify syntax errors, logical errors, style and readability issues in a supplied program, determine what the program is intended to calculate, and make appropriate corrections. Students must test the program using varying values, explain the changes made, and improve its overall readability and annotation so that an unfamiliar user can understand it. The task also requires students to investigate computational efficiency by measuring execution time for different input limits and identifying more efficient coding or logical approaches. Task 2 requires students to design, build and test a simple Python implementation of the Blackjack card game. The program should simulate a single-player game against a computer-controlled dealer, allow the player to choose between hitting and standing, automatically simulate the dealer's turn, and offer the option to play additional rounds. Complex rules such as splitting and betting are excluded, and students must not implement a Python class or graphical visualisation. The submission must include the Python code and the output from five games, with sufficient storytelling in the output to allow the code to be tested from the results shown. Task 3 assesses the student's ability to critically assess, select and apply data science tools. Students work with animal lifespan information from the AnAge database and use Python, including pandas and appropriate graphical libraries, to explore and communicate insights. The task involves summarising the number of animal species represented within each animal Class and producing plots of maximum longevity against adult weight for the four Classes with the most represented species. Students must discuss whether smaller or larger animals live longer, identify extreme outliers, compare trends between animal groups and consider implications for ageing research. Task 4 examines data protection and data ethics using the Cancer Genome Atlas (TCGA) as a case study. Students must explain how a potential data breach could occur, identify the personal and sensitive information that could be compromised, and discuss consequences for patient confidentiality, institutional reputation, participation in future research and possible legal or public relations responses. A second part considers a hypothetical UK database and requires discussion of GDPR and the UK Government Data Ethics Framework, including informed consent, anonymisation, transparency, ethical governance, privacy and public trust in biomedical research. The assessment assesses two module learning outcomes. MLO2 focuses on designing, building, testing, adapting and critiquing small programs in a high-level programming language and is assessed through Tasks 1 and 2. MLO3 focuses on critically assessing, selecting and applying data science tools, libraries or algorithms throughout the data science project lifecycle and is assessed through Tasks 3 and 4. The assignment requires APA referencing and asks students to provide in-text citations and reference lists where relevant. The brief also classifies the assessment as “Amber” for Generative AI: AI tools may be used for inspiration but not to generate answers or analyse datasets. Any permitted use must be clearly acknowledged, documented and cited using APA style.
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Organisational Strategy and Sustainability
2,473 words
Strategic Evaluation of Hindustan Unilever Limited (HUL): Organisational Strategy and Sustainability
This 2,473-word MSc Management coursework for BPP University’s Organisational Strategy and Sustainability module examines the strategic, sustainability and leadership dimensions of Hindustan Unilever Limited (HUL). The report applies the module’s learning outcomes through four main areas: strategic environmental analysis, ethical and sustainable practice, evaluation of strategic options, and personal leadership and sustainability reflection. The assignment is structured around PESTEL and VRIO for strategic analysis, the Triple Bottom Line for sustainability evaluation, the Ansoff Matrix for strategic options, and Emotional Intelligence and Transformational Leadership for personal reflection. The first section evaluates HUL’s external and internal environment. PESTEL analysis considers political and legal requirements, economic pressures, social changes, technological developments and environmental risks. The report highlights issues including packaging regulation, extended producer responsibility, data privacy, commodity and energy volatility, rural–urban differences, AI-enabled operations, digital traceability, climate risk, water stress and packaging circularity. VRIO analysis then evaluates HUL’s purposeful brands and portfolio breadth, rural route-to-market, digital operations and analytics, and supplier development and sustainability governance as sources of competitive advantage. The second section assesses HUL’s ethical, sustainable and responsible practices through the Triple Bottom Line framework of People, Planet and Profit. The analysis considers workforce and community priorities, customer responsibility, renewable energy, emissions, water stewardship, waste and packaging, as well as productivity, efficiency, risk mitigation and sustainable growth. A specific packaging circularity transition is proposed, including design-for-recycling, post-consumer recycled content, lightweighting, refill and reuse pilots, collection partnerships and consumer education. The third section evaluates four strategic options using the Ansoff Matrix: urban refill and concentrate formats, rural assisted commerce through “Shakti 2.0”, an ingredient traceability platform, and precision revenue-growth management in quick-commerce. Each option is considered in relation to strategic fit, VRIO capabilities, risks, implementation actions, ownership and KPIs. The final section provides a first-person reflection on leadership and sustainability. It uses Goleman’s Emotional Intelligence and Transformational Leadership to examine learning from the strategic and sustainability analysis, including self-awareness, self-regulation, empathy, collaboration and evidence-based decision-making. It also presents a 90-day leadership development plan focused on decision briefs, stakeholder pre-mortems and a personal Triple Bottom Line checklist.
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Technical Evaluation and Professional Reflection on a Cloud-Based Machine Learning Fraud Detection Solution
This individual technical evaluative report forms the reflective and critical component of the Machine Learning on Cloud module. It builds directly upon a group project involving the development of a machine learning solution for financial fraud detection. The report requires each student to critically evaluate the technical decisions made within the group solution while also reflecting on their individual contribution, teamwork experience and professional development. The first component, Technical Evaluation, accounts for 50% of the individual assessment and has a suggested allocation of approximately 1,000 words. Students critically examine the group’s machine learning solution, including the approaches adopted for data preprocessing, model selection and evaluation. They are expected to discuss the suitability of the chosen techniques and metrics, identify limitations and explain how individual technical decisions affected the final performance and outcome of the fraud-detection solution. The second component, Reflection and Professional Issues, also accounts for 50% and has a suggested allocation of approximately 1,000 words. Students reflect critically on their personal contribution to the project and their experience of working within a team. The discussion addresses challenges encountered, lessons learned and how the experience contributed to technical and professional development. Professional and ethical considerations form an important part of the reflection. Relevant issues include algorithmic bias, fairness, sustainability and data privacy, particularly in relation to machine learning applications within financial services and cloud environments. Students are also required to attach their Seminar Activity Tracker as an appendix, providing evidence of weekly participation and knowledge development. The assessment therefore combines technical critique with reflective practice, requiring students to demonstrate that they understand not only how a machine learning solution was developed, but also why specific technical decisions were made, their consequences, the limitations of the resulting system and the wider ethical and professional implications of deploying AI on cloud infrastructure. Overview word count: approximately 315 words. The brief also states that AI may assist with areas such as grammar, structure, organisation of ideas and suggestions, but the main content, analysis and conclusions must be the student's own work, and AI use must be declared.
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Machine Learning / Cloud Computing
3,984 words
Machine Learning on Cloud: Fraud Detection Model Design, Training and Evaluation
This postgraduate group project focuses on the design, development and critical evaluation of a cloud-based machine learning solution for financial fraud detection. The scenario involves a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and improve customer security. Students are required to analyse an appropriate dataset, develop a machine learning solution, evaluate its effectiveness and critically consider the suitability of cloud technologies for deployment. The project begins with a cloud feasibility study, requiring critical comparison of at least two major machine learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Evaluation criteria include performance, scalability, cost, compliance, integration and vendor lock-in, followed by a justified recommendation. Students then conduct exploratory data analysis to identify patterns, anomalies and correlations using appropriate visualisations such as heatmaps, histograms and boxplots. A substantial component addresses data preprocessing and class imbalance. Students are expected to clean and transform the data, apply scaling and encoding, perform feature engineering and investigate approaches such as SMOTE, undersampling and cost-sensitive learning. Each preprocessing choice must be justified in terms of its potential impact on model performance. Students must select and train at least two machine learning models, with suggested approaches including Logistic Regression, Random Forest, XGBoost and Neural Networks. Model development incorporates cross-validation and hyperparameter tuning. Evaluation uses fraud-relevant measures including Precision, Recall, F1 score, AUC and precision-recall curves, supported by confusion matrices, ROC curves and feature-importance visualisations. The project concludes with critical consideration of professional and ethical issues in cloud-based AI, including bias, fairness, transparency, data privacy and sustainability. The overall assessment therefore integrates cloud-platform evaluation, machine learning development, imbalanced classification, model evaluation and responsible AI practice. Overview word count: approximately 340 words.
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Cloud Computing / Big Data Technologies / Cyber Security
2,500 words
Cloud and Big Data Security Application: Design, Implementation and Evaluation
This assessment for the Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud-based or distributed data application. The project focuses on practical solutions involving the complex transformation, processing, storage and security of big data within cloud environments. Students are expected to demonstrate how distributed data can be organised in the cloud, how data pipelines can be used to access or process distributed databases, and how appropriate security controls can be incorporated into the resulting architecture. Students have considerable freedom when selecting their application. Suggested project directions include developing a data-science solution using SQL or MongoDB with cloud storage and an appropriate security policy; implementing privacy-preserving distributed processing using techniques such as Differential Privacy; creating multi-party authentication and group-based access-control mechanisms; or designing Multi-Level Security, Attribute-Based Encryption or Role-Based Access Control solutions. Projects may also examine distributed or cloud applications using security protocols such as SSH, SSL or IPsec. Creativity and originality are explicitly encouraged. The written component is a Design and Implementation Document of no more than approximately 2,500 words. It should present the project aims and objectives, application concept, cloud and security technologies, functional and security requirements, architecture and design decisions, protocols, access-control mechanisms, implementation process, achievements, problems encountered and overall evaluation. Relevant diagrams, such as interaction or sequence diagrams, may be used to explain system behaviour and architecture. The assessment also requires submission of the functioning Cloud and Big Data Security application and a 7-minute highlight demonstration video. The video should demonstrate the application's major features, implementation details, security functionality and, where appropriate, attack scenarios. Assessment places strong emphasis on the quality of the design and implementation documentation, originality, use of advanced features, and the overall effort and technical quality of the completed application. Students are therefore expected to demonstrate independent development rather than simply reproduce an existing tutorial.
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