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AP Interenship
3,000 words
AP Internship – Reflective Individual Report and Poster Presentation
This assessment for the AP Internship module is designed to encourage students to critically reflect on their experience in an external learning environment and evaluate how their internship has influenced their knowledge, understanding and professional development. The assessment consists of two components: a Reflective Individual Report worth 80% and a Poster Presentation worth 20%. The module is assessed on a Pass/Fail basis, and students are required to achieve 50% or above to pass. The Reflective Individual Report has a suggested word limit of 3,000 words, with an additional 10% permitted. The report should provide a critical account of the student's internship experience and demonstrate how existing disciplinary knowledge was applied in an unfamiliar professional environment. Students are expected to reflect on their learning, personal development, professional skills and contributions during the placement, supported by appropriate examples and academic literature. The report is structured around four main areas. The first is Finding an Internship, which asks students to explain how they secured their internship, including the criteria considered when selecting an opportunity, the types of organisations approached, the level of proactivity demonstrated during the application process, and recommendations for improving future job applications. The second area is Professional Development Activities, where students reflect on formal training or induction provided by the organisation and additional self-development activities undertaken during the internship. This section can also address the student's role, responsibilities, relationships with colleagues and efforts to integrate positively into the workplace. The largest section is the Progress Report, which brings together the main internship activities and requires students to build a portfolio of the major tasks undertaken during their placement. Students should discuss key activities, personal contributions, skills and knowledge gained, and the development of interpersonal and intrapersonal skills through broader work-based engagement. The final section, Reflection of Learning and Development, requires critical reflection on achievements, contributions, strengths and weaknesses, continuous self-development and employability. Areas include decision making, problem solving, communication and influencing, technical skills, collaboration, innovation and proactivity, together with the relevance of internship activities to the student's programme of study and future career. The second component is a 10-minute Poster Presentation worth 20%. The poster should provide an introduction to the internship and company, showcase key internship activities, and communicate the most important or interesting findings from the internship project. The poster should maintain an appropriate balance between visuals and text and follow the template provided through Blackboard.
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Databases and Business Intelligence
Databases and Business Intelligence – KAP Speciality Chocolates Database Coursewor
This individual coursework for the Databases and Business Intelligence module provides practical experience of database systems through the realistic KAP Speciality Chocolates case study. Students are required to design and implement a relational database solution that supports the management of products, stock, suppliers and purchase orders for KAP Speciality Chocolates Ltd. The case study describes a growing chocolate business with a shop and a storeroom that requires a database system to reduce operational errors and improve stock control. The first task requires students to analyse the case study and identify appropriate entities, attributes, relationships, primary keys, foreign keys and dependencies. Students must produce an extended Entity Relationship Diagram showing how the identified elements should be related. The diagram must include participation and cardinality constraints, and students must clearly document the assumptions made and the notation used. A range of modelling notations and software tools may be used, provided that the required database elements are clearly represented. The second task requires students to translate the ER design into a relational database implementation by writing SQL Data Definition Language statements. The SQL must define suitable tables, attributes, data types, primary and foreign keys and relevant constraints. The chosen data types should be appropriate and foreign-key data types must correspond correctly to their related primary keys. The third task focuses on SQL Data Manipulation Language and requires students to create queries addressing a range of business information needs. These include producing a product price list, identifying purchase orders due for delivery on a specified day, generating shop re-stocking lists, changing product cost and selling prices, identifying products with outstanding purchase-order deliveries, producing a re-order list for products below their minimum warehouse stock level, and identifying suppliers that provide multiple cartons. Students must populate their tables with sufficient suitable data to demonstrate that the SQL works and provide evidence of testing through screen captures of SQL statements and results. The fourth task requires students to demonstrate that the database design is normalised to Third Normal Form (3NF). The normalisation process must begin with unnormalised data and show the stages through First Normal Form (1NF), Second Normal Form (2NF) and Third Normal Form (3NF), explaining the changes and reasons at each stage. The final normalised solution must remain consistent with the ER diagram, assumptions and entity and attribute names used in the database implementation. The final submission must combine all tasks into one report written in English, using Arial 12-point font and single spacing. The report must contain the extended ER diagram and assumptions, SQL table definitions, SQL queries with explanations and testing evidence, and a written explanation of the normalisation process. The completed report must be submitted as a single PDF through SurreyLearn by the specified deadline.
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Global Strategic Management
3,500 words
Global Strategic Management – Linear to Circular Business Model Transformation
This assessment for the Global Strategic Management module examines how organisations can transform from traditional linear business models towards circular business models. Students must select one of four approved organisational cases and use the same case consistently across both summative assessments. The approved cases are SKF, focusing on servitisation and remanufacturing transformation; Sandvik, focusing on closed-loop materials and tool recovery; Volvo Construction Equipment, focusing on design for remanufacturing and lifecycle optimisation; and Husqvarna, focusing on access-based and circular consumer models. The assessment requires students to apply strategic management, sustainability and circular economy concepts to evaluate the transformation of the selected organisation. Assessment A is a Strategic Poster worth 30% of the overall module mark. The poster should visually and analytically map the transformation of the selected organisation from a linear to a circular business model. It contains three main components. First, students must identify and analyse external drivers using PESTEL, explaining the factors creating pressure or opportunities for circular transformation. Second, students must explicitly reconfigure the organisation's value chain, distinguishing between primary activities such as inbound logistics, operations, outbound logistics, marketing and sales, and service or lifecycle activities, and supporting activities such as firm infrastructure, human resource management, technology development and procurement. Third, students must identify one core structural strategic tension created by the transition. This tension should represent a built-in trade-off in which improving one strategic objective may constrain or challenge another. Assessment B is a Strategic Evaluation Report worth 70% of the overall module mark. The report has a word limit of 3,500 words with a 10% tolerance, excluding the table of contents, reference list and appendices. The report requires a comprehensive strategic evaluation of the selected organisation's transition from a linear to a circular business model and should integrate concepts and frameworks covered throughout the module. The first component of the report requires a Business Model Reconfiguration Analysis using the Business Model Canvas. Students should critically analyse how the circular transformation changes the organisation's value proposition, customer segments, customer relationships, channels, key activities, key resources, key partnerships, revenue streams and cost structure. The analysis should explain how value is created, delivered and captured differently under the circular model. The second component evaluates sustainability and legitimacy using an integrated analysis of the Triple Bottom Line, SAFe framework and stakeholder power and interest analysis. Students should assess economic, environmental and social sustainability, evaluate the suitability, acceptability and feasibility of the transformation, and identify stakeholders who can enable or constrain implementation. The third component requires students to analyse the same strategic tension identified in the poster. The tension should be evaluated in greater depth using appropriate strategic, sustainability and stakeholder frameworks, followed by a theoretically grounded and operationally feasible recommendation. The final component requires students to integrate four of their strongest reflective blog posts from the twelve seminar weeks. The reflection should demonstrate intellectual development, critical thinking, engagement with theory, seminar participation and responsiveness to tutor feedback rather than simply describing the content of the seminars. The assessment is expected to demonstrate Level 7 academic standards through integrated framework application, critical evaluation, strategic judgement and advanced reflective insight. Academic sources must be cited using Harvard referencing, with appropriate in-text citations and a complete reference list.
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Databases
1,000 words
Assessment #1 – Advanced Databases: NORTHERNTOURS Database Design and Implementation
This assessment for the Advanced Databases module (KL7011) focuses on the analysis, design and implementation of a database system based on the NORTHERNTOURS scenario, a fictitious transport company operating a fleet of luxury coaches across cities, towns and tourist locations in Northern England. The assessment requires students to demonstrate advanced database knowledge through conceptual modelling, logical database design, SQL implementation, data manipulation and the evaluation of alternative database technologies. The assessment addresses learning outcomes relating to the data life cycle, advanced data modelling and database design, as well as professional, legal, ethical, security, sustainability and risk considerations. The first part requires students to develop a conceptual database design for NORTHERNTOURS using Entity-Relationship (ER) or Enhanced Entity-Relationship (EER) modelling. The design should identify relevant entities, relationships, key attributes, primary keys and structural constraints. Students then convert the conceptual model into a logical relational schema using ER/EER-to-relational mapping, identify primary and foreign keys, ensure the relations satisfy Third Normal Form (3NF), select and justify a consistent naming convention, and produce a textual data dictionary containing relevant names, descriptions and constraints. The logical design is subsequently implemented using Oracle 11g, 12c or higher through appropriate SQL DDL statements and database constraints. The second part involves populating selected database relations with self-generated sample data and demonstrating database retrieval capabilities. Students must provide SQL DML statements, relational algebra expressions and SQL queries for specified NORTHERNTOURS business requirements. The solutions must be executed in a live Oracle environment and supported with appropriate output evidence. The third part extends the database analysis by considering object-relational and NoSQL database technologies. Students evaluate which aspects of the NORTHERNTOURS conceptual design could benefit from object-relational implementation, develop and populate a suitable object-relational subset, and demonstrate it through complex queries. They also analyse where NoSQL concepts could provide benefits and discuss design choices supported by representative NoSQL implementation code. Finally, students prepare a concise report for the NORTHERNTOURS managing director addressing sustainability, professional, legal, ethical and security issues, together with diversity, inclusion, cultural, societal and environmental considerations and commercial risk management. The report should use a critical review of relevant literature, systems, developments and standards and follow Harvard referencing conventions.
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Business
850 words
HopeLink Community Support and Food Security Database
This assignment requires students to design, develop and analyse a relational database for HopeLink Community Support and Food Security, a fictitious non-profit organisation working across rural and urban communities to reduce poverty and hunger. HopeLink currently relies on spreadsheets, which have resulted in inefficiencies and reporting errors. The proposed database is intended to improve operational efficiency, transparency and evidence-based reporting while supporting the organisation's work towards United Nations Sustainable Development Goals (SDGs) 1 and 2: No Poverty and Zero Hunger. The database development project involves five main entities: Beneficiary, Donor, Product, Collection and Collection Detail. Students are required to create a data dictionary defining the tables, attributes and appropriate validation rules. They must then use SQL within Microsoft Access to create the required tables through SQL View, rather than using the Access front end. Primary keys and foreign keys must be identified and implemented as part of the database design. Students must also establish relationships between the database entities and enforce referential integrity to maintain accurate information for management reporting and decision-making. Appropriate validation rules, user-friendly error messages, drop-down lists and input masks should be implemented where required. The database must be populated with sufficient realistic data for testing, with minimum requirements of 10 Beneficiary records, 10 Donor records, 20 Product records, 10 Collection records and 20 Collection Detail records. A further component requires students to develop five useful SQL queries in Microsoft Access to support analysis and reporting for HopeLink. The queries must follow specific requirements, including one parameter query, at least three queries containing a WHERE clause, at least one aggregate query and at least two queries involving joins across more than two tables. Students must provide screenshots of the SQL code and resulting outputs, together with an English explanation and a short justification of how each query could support HopeLink's operations and decision-making. The final component is an 850-word maximum business insights report investigating the use of food banks in the UK and considering how initiatives could support SDG 1 and SDG 2. Students are expected to use reliable external data, present relevant graphs and analyse current trends in food-bank usage. This section requires Harvard referencing and should use reliable academic, industry and other appropriate sources. The report is based on SDG 1 and SDG 2 and is not directly linked to the database developed for the assignment. Overall, the assignment assesses students' ability to apply data-modelling techniques, database design principles, SQL and data analytics to support organisational operations and strategic decision-making. It combines practical database development in Microsoft Access with data analysis and a business-focused evaluation of food-bank trends and sustainability goals.
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Global Supply Chain Management
1,800 words
Suez Canal Blockage: A Case Study of Global Supply Chain Disruption
This individual report examines the challenges faced by global supply chains as a result of the Suez Canal blockage and critically evaluates the factors that affected supply chain performance during the disruption. The assignment uses the Suez Canal blockage as a case study to explore the wider principles and practices of global supply chain management, with particular attention to supply chain disruption, risk management, resilience, logistics, supplier relationships and integrated supply chain performance. The report requires students to conduct preliminary research into the Suez Canal blockage and use relevant academic and practical sources to identify the major challenges created by the disruption. The case study focuses on the six-day stranding of the Ever Given and the resulting disruption to international supply chains and global trade. The assessment considers how a disruption at a strategically important global trade route can create wider consequences for organisations, logistics networks and supply chain operations. The analysis should identify the key factors that influenced supply chain performance and critically evaluate the effectiveness of the solutions implemented to resolve the disruption. Relevant global supply chain management theories, models and concepts should be clearly defined and applied to the case rather than simply described. The report should demonstrate an understanding of fundamental supply chain principles and consider how evolving trends and global issues can affect supply chain operations. The assignment also encourages consideration of important supply chain management areas covered within the unit, including risk management, resilience, outsourcing, supplier planning and selection, relationship management, supply chain integration, demand management, order management and customer service. These concepts should be connected to the Suez Canal blockage to provide a critical assessment of the challenges and possible approaches to improving supply chain resilience. The report must be presented in a formal and logically coherent report format and should provide evidence of critical reasoning through a wide range of relevant academic and practical sources. The conclusion should draw together the main findings, while recommendations should propose realistic approaches for addressing the supply chain issues identified in the case. The assessment is worth 30% of the overall unit mark and requires an individual report of 1,800 words, with a permitted variation of ±10%. The report is assessed across four main areas: use of theory and frameworks (25%), analysis and evaluation (30%), conclusions and recommendations (20%), and structure, presentation and referencing (25%). The marking criteria emphasise the application of relevant theoretical concepts, critical evaluation of academic and practical sources, a clear connection between theory and practice, realistic recommendations, logical structure, and appropriate in-text citations and referencing.
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Global Supply Chain Management
4,200 words
Global Supply Chain Management – Gatwick Airport Northern Runway Expansion
This individual report examines several strategic and operational aspects of the global supply chain associated with the Gatwick Airport Northern Runway expansion. The assessment requires students to investigate the organisation and its current global supply chain challenges, while applying relevant supply chain management theories, frameworks and models to analyse its operations and develop strategic recommendations. The report begins by providing a background of the organisation, with particular attention to its current global supply chain issues and strategic supply chain relationships. Key areas of investigation include risk management, supply chain resilience, outsourcing, supplier planning and selection, supply chain integration, network design, leadership, benchmarking, decision-making, and the application of lean and agile approaches. These areas provide the basis for understanding how the organisation can manage its supply chain effectively within a complex and changing global environment. A critical evaluation of external factors affecting the organisation's global supply chain is also required through the use of a PESTEL analysis. This enables consideration of the political, economic, social, technological, environmental and legal factors that may influence supply chain performance, risks, costs and strategic decisions. The report must then develop key strategic recommendations aimed at optimising the organisation's global supply chain. The recommendations should focus on reducing costs, supporting sustainability, improving operational efficiency and enhancing supply chain resilience. Each recommendation should be supported by appropriate supply chain management theories and models, such as agile supply chain approaches, lean supply chain principles and global sourcing strategies. Students are expected to provide a clear rationale for their recommendations using theoretical insights and relevant academic and professional literature. The assessment places emphasis on critical reasoning, wider reading and the ability to connect supply chain theory with practical organisational issues. A logically structured, professional and business-like report is required, supported by appropriate evidence and Harvard referencing. The marking criteria assess the use of theory and frameworks, critical analysis and evaluation, conclusions and recommendations, and the overall structure, presentation and referencing of the report. The assignment is worth 70% of the unit assessment and has a required length of approximately 4,200 words, with a permitted variation of ±10%. The report should demonstrate an understanding of global supply chain management practices and the ability to apply relevant theories and frameworks to analyse supply chain challenges and develop realistic strategic recommendations.
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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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4,200 words
Global Supply Chain Strategy for the Gatwick Airport Northern Runway Expansion
This individual Global Supply Chain Management assessment requires students to prepare a professional strategic report on the supply chain implications of the Gatwick Airport Northern Runway expansion. The task focuses on applying contemporary supply chain theories, models and analytical techniques to a complex infrastructure development context, with particular attention to strategic relationships, operational efficiency, risk and resilience. Assignment Brief 2 - MBA013 Glo… The report begins by establishing the background to the organisation and examining its current global supply chain context. Students are expected to consider issues including strategic supplier and customer relationships, risk management, resilience, outsourcing, supplier planning and selection, integration, supply chain networks, leadership, benchmarking, decision-making, lean principles and agile supply chains. These areas provide the basis for evaluating how supply chain performance can be improved in a major airport expansion project. Assignment Brief 2 - MBA013 Glo… A key analytical component requires a PESTEL analysis to critically assess how external political, economic, social, technological, environmental and legal factors may affect the organisation’s global supply chain. The purpose is not only to identify external pressures but to evaluate how those factors may influence sourcing, supplier management, cost, risk exposure, resilience and long-term strategic decision-making. Assignment Brief 2 - MBA013 Glo… The final part of the report develops strategic recommendations aimed at optimising supply chain performance. Recommendations should focus on reducing costs, supporting sustainability, improving operational efficiency and strengthening resilience. Students are expected to justify these proposals through explicit application of relevant theories and models such as lean supply chain, agile supply chain and global sourcing strategies, supported by appropriate academic and practical evidence. Assignment Brief 2 - MBA013 Glo… The marking criteria place substantial emphasis on theoretical application, critical analysis and the connection between theory and practice. The rubric allocates 25% to theory and frameworks, 30% to analysis and evaluation, 20% to conclusions and recommendations, and 25% to structure, presentation and referencing. Assignment Brief 2 - MBA013 Glo… Assignment Brief 2 - MBA013 Glo… Overall, the assessment develops competence in strategic supply chain analysis, infrastructure logistics, risk and resilience, supplier management, lean and agile operations, sustainability and evidence-based recommendation development. Overview word count: approximately 350 words. Important: the brief explicitly states that the report must be original and that students should not use AI to produce the work. Assignment Brief 2 - MBA013 Glo…
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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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Cyber Security / Applied Cryptography / Secure Systems Design
2,500 words
Secure Property Contract Exchange: Cryptographic Protocol Design, Threat Modelling and Post-Quantum Readiness
This Applied Theory of Cyber Security and Secure Design coursework places students in the role of a cyber security consultant engaged by Hackit & Run LLP, a legal firm specialising in UK and international property transactions. The firm wishes to implement a secure digital system for handling, exchanging and legally signing property contracts. Students must design and evaluate a secure communication protocol supporting interactions between the buyer’s solicitor, the seller’s solicitor and the buyer while addressing both first-time communications and previously established secure relationships. 11b17febb9eeb4570f76f6ca95a831a… Section A – Cryptographic Protocol Design, worth 45%, requires a complete secure communication protocol. Students must explain how trust is initially established, how later communications can be simplified without weakening confidentiality, integrity or availability, and how the buyer can digitally sign a contract in a manner enforceable under UK law. The design must justify specific cryptographic algorithms for functions such as key exchange, bulk encryption, digital signatures and hashing. The protocol must be presented through both a sequence diagram showing message flows and cryptographic operations and pseudocode explaining the key algorithmic stages. 11b17febb9eeb4570f76f6ca95a831a… Section B – Threat Modelling, worth 20%, requires a focused analysis using the STRIDE methodology. Students identify three realistic threats from different STRIDE categories and analyse the attack vector, asset at risk and potential effect on the legal transaction. Each threat must then be connected back to specific protocol defences, with residual risks acknowledged where controls cannot provide complete mitigation. The guidance encourages consideration of issues such as social engineering, insider threats, key-management failures and availability risks in addition to purely cryptographic attacks. 11b17febb9eeb4570f76f6ca95a831a… Section C – Security Evaluation Against Standards, worth 15%, requires students to evaluate the proposed system against a recognised cybersecurity standard or framework. Options include ISO/IEC 27001:2022, Common Criteria (ISO/IEC 15408) and the OWASP Application Security Verification Standard. Students select three or four directly relevant controls or requirements, assess whether the proposed design satisfies them, identify gaps and recommend specific improvements. 11b17febb9eeb4570f76f6ca95a831a… Section D – Post-Quantum Readiness and Critical Reflection, worth 15%, examines how a future quantum-capable adversary could affect the protocol. Students identify vulnerable cryptographic components, discuss the NIST Post-Quantum Cryptography standardisation programme, and examine replacement algorithms such as ML-KEM for key establishment and ML-DSA for digital signatures. They must also evaluate a hybrid migration strategy combining classical and post-quantum algorithms, considering performance overhead, backward compatibility and the legal admissibility of post-quantum digital signatures. 11b17febb9eeb4570f76f6ca95a831a… The remaining 5% evaluates professional report quality, logical structure, technical language, integration of diagrams and consistent CUHarvard referencing. Higher-quality work is expected to demonstrate a sophisticated trust model, clear traceability between threats and controls, precise standards mapping, practical security recommendations and well-evidenced analysis of post-quantum migration. 11b17febb9eeb4570f76f6ca95a831a… Important for the public Reference Library: the brief states that the assessment document is intended only for Coventry University Group students and must not be passed to third parties or posted on any website. Therefore, publish only an original high-level description such as the overview above; do not upload or reproduce the original assignment brief publicly. 11b17febb9eeb4570f76f6ca95a831a…
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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 Analytics
Airline Passenger, Ticket Price and Revenue Analysis Using Python
This applied Data Science project requires students to use Python to analyse passenger demand, ticket prices and airline revenues across the period 2021–2023. Two datasets are provided: one containing daily passenger counts and revenue or average-price information for 2021 and 2022, and another containing a representative sample of individual journeys made during 2023. The datasets may include domestic, European and intercontinental routes as well as standard/economy and business/club travel classes. 202526_SmallProject_semB Students must develop Python code that reads the assigned datasets and produces a series of analytical visualisations. A core requirement is to plot daily passenger numbers for economy and business classes across all three years, combining scatter plots with smoothed trend lines. The smoothing must be calculated using the first eight terms of a Fourier series through the supplied scrfft.py module. 202526_SmallProject_semB Further analysis examines changes in average ticket prices, including monthly comparisons across 2021, 2022 and 2023, together with annual average prices that incorporate revenue generated from additional services such as seat reservations and extra baggage. 202526_SmallProject_semB A fourth analysis is personalised according to the final digit of the student's ID. Depending on the allocated task, students may investigate weekly journey distributions, seasonal travel patterns, price-versus-distance relationships, business and economy class behaviour, additional-service revenues, weekend journeys or linear relationships between selected variables. Three additional numerical values, labelled X, Y and Z, must also be calculated according to the assigned scenario. 202526_SmallProject_semB The final report must include four figures, relevant mathematical formulae and a critical discussion of the findings. Students are expected to interpret trends in passenger numbers, prices, revenues, seasonal patterns and airline profitability rather than simply presenting calculations. The report is limited to five A4 pages, while the Python source code and report are submitted separately. 202526_SmallProject_semB Overview word count: approximately 330 words. Note: the university name is not printed directly in the visible assignment heading, but the submission instructions use a herts.ac.uk module-leader address and StudyNet/Canvas, which identifies the institution as the University of Hertfordshire. 202526_SmallProject_semB
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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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Statistical Programming / Data Science / Business Analytics
Statistical Programming with R: Data Analysis, Probability, Regression and Business Decision-Making
This Statistical Programming assessment evaluates students' ability to apply statistical techniques and R programming to practical data-science and business decision-making problems. The individual assessment combines descriptive statistics, data preparation, visualisation, probability, regression, correlation and sampling, requiring students to demonstrate both conceptual statistical understanding and practical implementation in RStudio. The module learning outcomes emphasise the application of statistical methods to large and real-world datasets, critical evaluation of analytical techniques and awareness of legal, cultural and ethical issues associated with data analysis and dissemination. KL7012 - Statistical Programmin… The early tasks examine fundamental statistical reasoning. Students interpret weight-loss data comparing exercise classes with gym-only workouts using sample size, mean, mode and standard deviation, and explain an appropriate method for dealing with missing data, including its advantages and disadvantages. KL7012 - Statistical Programmin… A substantial practical component uses a cystic fibrosis dataset containing variables such as age, sex, height, weight, body-mass-related measurements, forced expiratory volume, residual volume, functional residual capacity, total lung capacity and maximum expiratory pressure. Students import the data into an R data frame, generate descriptive summaries and interpret the results. They then use scatterplots to investigate relationships between variables and sex-stratified boxplots to identify possible outliers. KL7012 - Statistical Programmin… The assessment also covers major probability models. Students apply probability concepts to healthcare survival, helpdesk email arrivals and fuel-demand scenarios, while also discussing how changing assumptions or real-world conditions can affect interpretation. These exercises assess understanding of statistical distributions and their application to operational and managerial decision-making. KL7012 - Statistical Programmin… Further analytical tasks examine linear regression and correlation. Students analyse the relationship between temperature and converted sugar in a chemical process, use a regression model to estimate the expected response at a specified temperature, and interpret relevant summary statistics. They also calculate and evaluate the suitability of a correlation coefficient for examining the relationship between advertising activity and product purchases. KL7012 - Statistical Programmin… The final and most substantial task involves a real-world M1 traffic-speed investigation for a manufacturing organisation. Students must design an appropriate sampling strategy, collect data from the specified Traffic England source, conduct statistical analysis in RStudio and develop evidence-based conclusions. The statistical report for this task is limited to 1,500 words and should include sampling methodology, collected data, statistical analysis, results, conclusions and relevant background research, supported by appropriate graphs, tables and charts. Raw data and RStudio calculations must be included in an appendix. KL7012 - Statistical Programmin… Overall, the assessment integrates statistical theory with R-based practical analysis, covering descriptive statistics, probability, visualisation, missing-data treatment, regression, correlation, sampling and critical interpretation of results in healthcare, operational and business contexts.
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Data Warehousing and Big Data
Data Warehousing and Big Data – Inventory Management Data Warehouse
This individual coursework for CS7079 Data Warehousing and Big Data requires students to design, implement and test a data warehouse based on a business case scenario and then export and migrate data to a Big Data platform for further processing. The assessment focuses on the inventory management business process of ABC Consumer Electronics Outlet Ltd, a multi-channel consumer electronics retailer operating from six stores around London and conducting online business across the UK and Europe. The company manages more than 10,000 products across approximately ten categories and more than 200 brands. The company already uses cloud-based and business applications including Vend, Linnworks and Xerox, which generate large volumes of transactional data. However, these datasets are stored separately by the individual applications, making it difficult for managers to produce integrated reports and perform analysis to support business decision-making. A business analyst has therefore recommended the development of a data warehouse capable of meeting the company's reporting and analytical requirements. For this coursework, the solution is focused specifically on the inventory management business process. The inventory management case involves three main business activities: sending purchase orders to suppliers when stock reaches minimum levels, receiving purchase orders and storing stock in appropriate locations, and controlling and maintaining stock levels including adding new products and adjusting existing stock. The required analysis includes daily stock levels for the previous month, weekly identification of products at minimum stock levels, analysis of stock levels by brand, product type and supplier, daily and weekly sent and received stock orders, and analysis of received stock orders by supplier and month. Students must analyse and design a dimensional data model that represents these business activities. This includes defining the grain of three central fact tables, identifying appropriate dimensions, defining dimension attributes and fact measures, and producing simple star schemas showing the relationships between fact tables and dimensions. The design must be justified according to the available data sources and the reporting and analysis requirements. The implementation stage requires students to create the relational database using Microsoft SQL Server Management Studio. Students must create the database, dimension tables and fact tables, establish appropriate primary-key and foreign-key constraints, and demonstrate implementation and testing through SQL commands and their results. Test data must then be populated into the data warehouse. The Big Data component requires migration of test data from the data warehouse to an Apache Hadoop platform using the Hortonworks Data Platform. Students must export the data warehouse data to an external data file, migrate the file into Apache HDFS, create a suitable data structure for loading the data into Hive, and demonstrate Apache Pig for manipulating the loaded data. Implementation and testing of the Big Data storage environment must also be demonstrated through commands and results. The coursework concludes with a personal reflective section in which students discuss what they have learned throughout the overall coursework and the challenges encountered during the process. The final submission is expected to be a well-written, structured and well-presented report combining data warehouse design, database implementation, testing, Big Data migration and reflective learning.
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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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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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Big Data Analytics / Data Analytics
4,000 words
Big Data Analytics Using Python and Business Intelligence with Tableau
This individual Big Data Analytics assessment requires students to critically analyse data using programming languages, statistical techniques, data visualisation methods and business intelligence software. The assessment combines practical data analytics using Python with interactive business intelligence and dashboard development using Tableau, requiring evidence of technical implementation, research, critical appraisal and justification of the selected analytical approaches. The first section, worth 70%, is based on a dataset containing accidental drug-related deaths recorded in Connecticut between 2012 and 2024. The dataset contains 12,964 observations and 49 variables. Students are required to conduct exploratory data analysis using Python, develop three to four research questions, formulate null and alternative hypotheses, and apply appropriate statistical methods. The analytical process also requires an evaluation of alternative technologies and methodological approaches, supported by relevant research. Students must justify their selected methodology and present a clear workflow diagram. The solution-development stage involves data preprocessing, descriptive statistical analysis, visualisation, answering the research questions and conducting statistical significance testing to determine whether the null hypothesis should be accepted or rejected. Evidence of coding and a link to working code are also required. The final Python component requires evaluation of the findings, consideration of limitations and recommendations for future development using emerging technologies. The second section, worth 30%, focuses on Business Intelligence using Tableau and uses a historical Olympic Games dataset containing 271,116 rows and 15 columns. Students analyse relationships between medals and host cities, athlete age and medal type, season and medal counts, and sex and medal type. The section culminates in an interactive Tableau dashboard containing at least four interconnected sheets, where changes to relevant parameters are reflected across the dashboard. Overall, the assessment develops practical competence in Python-based analytics, statistical reasoning, research-question development, hypothesis testing, data visualisation and interactive business intelligence dashboard design. Overview word count: approximately 350 words. AI note: the brief permits AI for limited support such as grammar, structure, organisation of ideas and suggestions, but states that the main content, analysis and conclusions must remain the student's own work. AI use must also be declared.
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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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Database Design and Implementation for KAP Speciality Chocolates Ltd
Design and implement a relational database system for KAP Speciality Chocolates Ltd based on the given case study. The assignment requires creating an Extended ER Diagram, developing SQL database structures with appropriate constraints, writing SQL queries for business requirements, populating the database with sample data, and demonstrating normalisation from Unnormalised Form (UNF) to Third Normal Form (3NF). Expected Deliverables: One PDF report containing: Extended ER Diagram with entities, attributes, relationships, keys and constraints SQL DDL statements for database implementation SQL DML queries with testing evidence/screenshots Normalisation process from UNF to 3NF
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