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Managing the Global Supply Chain and Sustainability
2,500 words
Managing the Global Supply Chain and Sustainability – Individual Company Case Analysis
This assessment is an individual company case analysis for the Level 7 module Managing the Global Supply Chain and Sustainability. Students are required to select a real organisation of any size, type, industry or country and produce a comprehensive analysis of one operations performance objective. The available performance objectives are cost, dependability, flexibility, speed and quality. The report should identify current issues faced by the chosen organisation in relation to the selected objective and critically analyse the factors affecting its performance. Students are expected to apply relevant theories, concepts and tools covered during lectures and seminars to the selected company case. Rather than providing only descriptive explanations of theoretical concepts, the assessment requires students to demonstrate how these concepts can be applied to understand the organisation's operational or supply chain challenges. The analysis should consider how relevant theories, models or tools could contribute to improving the company's performance. The report should also provide a critical discussion of appropriate performance-improvement practices. These may include the use of digital technologies, sustainability practices, supply chain strategies, operational approaches or other relevant methods. Students should relate these practices directly to the findings from their chosen company case and consider their practical implications. Academic evidence and real-world examples should be used to support the analysis, with appropriate attention given to supply chain and operations management literature. The assessment is designed to develop students' ability to understand supply chain strategies, assess their contribution to agility, viability and sustainability, propose practical solutions to modern logistics and supply chain challenges, and evaluate how digital technologies and sustainability practices can improve supply chain performance. The report should therefore demonstrate analysis, application of theory, critical evaluation and evidence-based recommendations rather than simply describing the organisation or its supply chain. The individual report has a maximum length of 2,500 words, excluding references, tables, figures and appendices. It must include a contents page, be spell-checked and follow the specified formatting requirements. The report should use Arial 12-point font, 1.5 line spacing, A4 page size and 2.54 cm margins. Academic sources such as journal articles and books should be used, and all sources cited within the report must appear in a Harvard-style reference list. The report is submitted through Moodle Turnitin as a Word document.
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Global Engineering: Theory and Practice
3,000 words
Global Engineering: Theory and Practice – Aspen Pharmacare Global Expansion and Environmental Sustainability
This assessment for the Global Engineering: Theory and Practice module requires students to prepare a critical report examining the international growth strategies and future sustainability of a global pharmaceutical company. The assessment is based on the published case study “The growth strategies of a global pharmaceutical company: a case study of Aspen Pharmacare Holdings Limited” by Victoria Margaret Hodgon and Muhammad Ehsanul Hoque. The scenario requires students to act from a management perspective, where a pharmaceutical company is considering international expansion while maintaining environmental sustainability as one of its core business values. The report has a maximum length of 3,000 words, excluding the reference list, and must critically address the success and growth strategy of Aspen Pharmacare as presented in the selected research paper. Students are expected to demonstrate understanding of international business and global engineering concepts while evaluating the strategic choices made by the company. The report should not simply reproduce the findings of the source paper; instead, it should critically evaluate the evidence, identify strengths and weaknesses, consider challenges and risks, and use additional published literature to inform management decisions. The first section is a 250-word summary explaining the approach used in the report to describe Aspen Pharmacare’s success and presenting the key findings. The introduction, limited to 500 words, should provide the background context of the global pharmaceutical industry and explain the main growth strategies available to a global pharmaceutical company. This section establishes the context for understanding Aspen’s international development. The main evaluation section accounts for 1,500 words and requires a critical assessment of Aspen Pharmacare’s roadmap to global success, including its growth strategies and strategic planning. Students should examine the different approaches used by the company and consider the benefits, limitations, challenges, risks and strategic trade-offs associated with its growth. The analysis should demonstrate critical strategic evaluation rather than simply describing the company's historical development. The future prospects section, limited to 500 words, focuses particularly on sustainability. Students must identify the key area affecting Aspen’s future sustainability in light of the challenges associated with its growth and consider what additional actions could be taken in environmental sustainability. Students are also required to locate and use published literature beyond the main Aspen Pharmacare paper to inform management about future trends and developments. The report concludes with a 250-word conclusion containing recommendations for management based on the findings of the analysis. The recommendations should be specific, relevant to the company's circumstances and supported by the evidence discussed in the report. Alternative strategic options should also be considered where appropriate. Harvard referencing is mandatory. The report should be professionally presented with appropriate headings, a logical structure, clear and coherent language, correct grammar and punctuation, and effective integration of academic evidence. The assessment is worth 100% of the module and is evaluated on the quality of the summary, introduction, strategic evaluation, future sustainability analysis, conclusions and recommendations, referencing and overall professionalism.
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Problem Solving and Research Methods for Managers
1,000 words
Problem Solving and Research Methods for Managers – Summative Assignment
This module assessment develops students’ ability to apply problem-solving tools, techniques and research methods to an original business problem. The assessment consists of two summative components. Summative 1 comprises two 25-question multiple-choice questionnaires covering the key learning areas taught throughout the module. Summative 2 consists of a video presentation and an individual reflective piece. For the video presentation, students must prepare a 10-to-15-minute presentation demonstrating competent application of the problem-solving tools, techniques and research methods taught during the module. The presentation should address an original problem identified within a business where the student has previously worked or currently works. Where the student does not have relevant work experience, a business that the student is interested in working for in the future may be selected. The presentation should include an appropriate analysis of the problem, application of relevant problem-solving and research methods, conclusions and recommendations. References should be included at the end of the presentation using the Harvard referencing system. The second component is an individual reflective piece of 1,000 words. The reflection should document the student’s learning experience, the outcomes of the experience and how the learning will inform future professional practice. The assessment brief emphasises that reflective writing should go beyond simply describing events. Students are expected to explore their thoughts and emotions and develop deeper self-awareness. The use of one or more reflective or learning frameworks is recommended to structure the reflection. The assessment is designed to assess both practical application and reflective learning. The video presentation should demonstrate the ability to connect theory with a real or relevant business problem and produce appropriate conclusions and recommendations. The reflective component should demonstrate what the student has learned from the experience, how that learning has influenced their understanding and how it can be transferred to future practice. Students must use Harvard referencing. The assessment is submitted through the relevant Moodle/Turnitin or Panopto submission links. The assessment brief states that presentation references are not included in the presentation word count. Students should also ensure that their final submission follows the required file-format and deadline instructions provided on Moodle. The marking rubric places substantial emphasis on the quality of presentation design and delivery, the depth of reflection and learning demonstrated, and clarity of writing, structure, grammar, Harvard referencing and use of visuals. Strong work should therefore demonstrate a clear connection between the business problem, the selected problem-solving and research methods, the resulting analysis, conclusions and recommendations, followed by a genuinely reflective discussion of the learning experience and its implications for future practice.
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Strategic Management in Healthcare
4,000 words
Strategic Management in Healthcare – Strategic Analysis and Strategy Formulation
This individual summative assessment for the Strategic Management in Healthcare module requires students to critically examine strategic management and strategic thinking within a healthcare organisation, hospital, clinical department or healthcare unit of their choice. The assessment is designed to develop students’ ability to apply strategic management concepts and analytical frameworks to a real healthcare setting, evaluate the internal and external factors affecting organisational performance, and formulate appropriate strategies in response to identified operational and competitive challenges. The assessment consists of a 4,000-word report with a 10% tolerance and accounts for 100% of the module assessment. Students must first select an organisation, clinical department or healthcare unit to use as the case setting. The report should begin with a concise introduction to the selected case setting, establishing its organisational context and providing a clear direction for the strategic analysis. The introduction should explain the relevant characteristics of the organisation or healthcare unit and provide sufficient background to understand the strategic issues examined in the report. The report must critically analyse the relevance of strategic management and strategic thinking to the selected healthcare setting. Rather than simply describing strategic management concepts, students are expected to explain why strategic planning and strategic thinking are important for the particular organisation or department and how they can support performance improvement, service development and responses to operational and competitive challenges. A central requirement is the application of three strategic analysis tools or frameworks. The assessment examples include SWOT, PESTLE, Balanced Scorecard, Value Chain, Porter’s Five Forces, Resource Based View and Blue Ocean Strategy. Students must apply at least one appropriate tool at each of three analytical levels: macro-environment analysis, service area or market analysis, and internal environment analysis. Before applying each tool, students should define the framework and critically justify why it is relevant, effective and suitable for the selected healthcare case setting. The analysis should be detailed and evidence-based rather than purely descriptive. Students are then required to develop a hierarchy of strategies based on the findings from the strategic analysis. Strategies should be formulated at three levels and should clearly respond to the issues identified through the external, market or service-area and internal analyses. The report should explain how the proposed strategies could help the healthcare organisation or unit improve performance and address its operational and competitive challenges. The relationship between the analysis, strategic recommendations and expected outcomes should be made explicit. The final component requires students to reflect on the relevance and usefulness of the strategic tools beyond the selected case study and in relation to their current or future professional role. The reflection should consider how the strategic frameworks and analytical approaches could be transferred to other organisational or professional situations. The assessment should use appropriate evidence from sources such as academic journal articles, books, websites, case studies and grey literature. Harvard referencing is required. The marking criteria place emphasis on the quality of the introduction and case context, critical discussion of strategic planning, quantitative and analytical application of three strategic tools, strategy formulation and evaluation, reflection on application relevance, and presentation and referencing. The assessment therefore requires an integrated connection between the healthcare case, strategic analysis, evidence, recommendations and reflection rather than isolated descriptions of strategic frameworks.
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Advanced Research Topics
Generative Adversarial Networks – Building and Applying GANs to Real-World Problems
This individual assessment focuses on building and understanding Generative Adversarial Networks (GANs) and applying them to real-world problems across different domains. The assignment is based on the GAN topics covered in Unit 5 and requires students to implement GAN models, analyse their behaviour, generate synthetic data and critically evaluate the quality of the generated outputs. The assessment consists of two main parts: building and understanding GANs from scratch using synthetic two-dimensional data, followed by applying GANs to three real-world application areas involving medicine, cybersecurity and creative artificial intelligence. Part 1 focuses on developing a practical understanding of GANs through implementation using PyTorch and synthetic 2D data. Students must first reproduce the sine-wave GAN demonstrated in the tutorial. They then create and model a new two-dimensional distribution, selecting from a 2D spiral, a mixture of Gaussians, or a noisy parametric curve defined as y = sin(2x) + 0.3cos(5x) + epsilon. Students must modify the GAN architecture, for example by changing the activation function or layer depth, and visually compare the original data distribution with the generated samples. This section is intended to reinforce understanding of the generator, discriminator, GAN training process and the effect of architectural choices on generated data. Part 2 addresses three real-world GAN applications. The first application concerns optical coherence tomography (OCT) retinal images using the OCTMNIST subset of MedMNIST. Students must explore the dataset, examine class distributions and sample images, build a ConvNet-based Deep Convolutional GAN (DCGAN) using PyTorch or TensorFlow/Keras, train the model and track generator and discriminator losses. Generated retinal images must be compared with real images both visually and quantitatively, using a performance measure such as the Fréchet Inception Distance (FID). An optional extension involves implementing a conditional GAN and demonstrating generation of images for specific classes. The second application focuses on cybersecurity using the preprocessed CICIDS2017 dataset containing DDoS and benign network traffic. Students must combine the relevant data files, explore the dataset and understand its features and class balance. A GAN must be developed to generate synthetic feature vectors rather than images. The model should be trained using benign and DDoS attack data, training loss curves should be monitored, and PCA or t-SNE should be used to visualise and compare real and generated feature distributions. The quality of the synthetic data should then be evaluated. An optional extension involves expanding the analysis to the full CICIDS2017 dataset and examining generalisation across different attack types. The third application focuses on Creative AI using the QuickDraw birthday cake category. Students must explore the birthday cake sketch dataset and implement a ConvNet-based GAN (DCGAN) to generate realistic birthday cake sketches. Generated sketches should be evaluated visually and quantitatively, including comparison with real examples and an appropriate metric such as FID. An optional extension involves generating samples from additional QuickDraw categories and discussing how model performance changes across different classes and levels of sketch complexity. The submission consists of both code and a written report. The code accounts for 60% of the assessment and must complete the required modelling tasks, present generated samples, compare generated and real data, use appropriate functions and include clear annotations so another user can understand the implementation. The report accounts for 40% and must be 6–8 pages. It should explain the analytical steps undertaken, justify the selected approaches, provide brief descriptions of the models used, interpret the results and include appropriate figures, evaluation metrics and academic references. The report should critically discuss why particular network architectures were selected rather than simply providing textbook definitions.
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International Business
2,500 words
Literature Review – International Business Research
This assessment is a 2,500-word individual Literature Review for the International Business module. The assignment requires students to select an international business-related research topic, develop a suitable research question and critically examine existing academic literature relevant to the proposed area of research. The purpose of the assessment is to demonstrate knowledge of the selected subject area, evaluate existing research, identify important debates and inconsistencies, examine research methodologies and theoretical frameworks, and establish a clear research gap that can provide the basis for further investigation. Students must obtain tutor approval for their chosen research topic before completing the Literature Review. The selected topic should have clear relevance to international business and should address an issue that is academically significant and practically relevant. Students are expected to use high-quality academic sources, with particular emphasis on peer-reviewed journal articles. The assessment guidance expects students to critically discuss a minimum of five and a maximum of eight journal articles, although additional academic sources may be used to support the discussion and critique. The Literature Review consists of three main sections: Introduction, Literature Analysis and Conclusion. The Introduction should be approximately 300 words and should introduce the research topic, explain why the issue is important, establish the main research question and demonstrate its relevance to international business. Students should also briefly discuss the potential implications of the proposed research for the advancement of knowledge, theory and practice. The Literature Analysis should be approximately 1,900 words and forms the main part of the assessment. Students should move beyond simply describing individual articles and instead analyse, evaluate and synthesise the literature. For each selected journal article, students should consider its contribution to existing knowledge, theoretical frameworks, research methodology, suggested research gaps and relationship to the proposed research question. The literature should be compared and contrasted to identify areas of agreement, disagreement, methodological differences, theoretical differences and unresolved issues. The analysis should demonstrate critical engagement with the literature rather than providing separate descriptive summaries of each source. The Conclusion should be approximately 300 words and should bring together the major agreements, disagreements and themes identified in the literature. Students should use the conclusion to justify their proposed research by demonstrating how it can address one or more of the research gaps identified through the literature review. The assessment is worth 100% of the final module grade and requires Harvard-style referencing. Students must provide a complete reference list and ensure that sources are appropriately acknowledged throughout the review. The assessment criteria evaluate knowledge of the subject area, analysis of research and methodologies, critical evaluation of literature, communication of complex ideas and professional referencing. High-quality work is expected to demonstrate a well-defined international business topic, strong critical analysis, comparison and synthesis of academic literature, clear identification of a research gap and a logically justified direction for future research.
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Research Methods
Assignment 3 – Large Language Models: LLM Coding and Report
This individual assessment for the Research Methods module focuses on the application of Large Language Models (LLMs) to a practical data science problem. The assignment is worth 25% of the module and is designed to develop students’ knowledge and understanding of research methods, investigative planning, data analysis, model evaluation and effective technical communication. Students are required to complete both a coding component and a concise written report demonstrating how an LLM has been selected, trained or fine-tuned, applied to a suitable task and evaluated against an appropriate baseline. The assessment begins with familiarisation with relevant literature. Students are expected to investigate the history and development of their chosen problem and examine the methods that have previously been used to address it. The brief provides key papers on common types of LLMs as a starting point for the literature review. Students then select a task that can be addressed through fine-tuning an LLM, with examples including sentiment analysis, fake news detection and topic classification. A publicly available dataset suitable for the selected text-classification problem must also be identified. Suggested sources include Kaggle and Hugging Face Datasets. The data must be appropriately preprocessed, including tokenisation using BERT's tokenizer and division into training and testing sets. Students then fine-tune a pre-trained BERT or BERT-style model using suitable tools such as the Hugging Face Transformers library and PyTorch. Possible model choices include BERT, RoBERTa and T5. The selected model should be appropriate for the specific problem, recognising that different language models may perform differently across tasks. Students are expected to implement a suitable training process using an appropriate optimiser and loss function. Model performance must be evaluated using relevant classification metrics, including accuracy, precision, recall and F1-score. The performance of the selected LLM should also be compared with a baseline model, such as Logistic Regression, Naive Bayes or a pre-trained BERT model. The analysis should explain the results and consider their relevance to the chosen problem. Two main submission components are required. The first is a code notebook, such as a Jupyter or Google Colab notebook, containing annotations explaining the purpose and operation of the relevant code so that another person can understand and reproduce the work. The second is a report of no more than three pages, including appropriate figures, tables and references. The report should cover the motivation and dataset, methodology, model training and evaluation, results and discussion, limitations, conclusion and possible future improvements. The assessment rubric places particular emphasis on coding quality and implementation, model architecture, analysis and interpretation, and report presentation. Strong work should demonstrate well-structured and reusable code, clear explanation of the model architecture and configuration, appropriate evaluation metrics and visualisations, meaningful comparison with relevant literature or baseline models, and critical evaluation of the model's success and possible improvements.
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Business Research Methods
3,500 words
Business Research Methods – Research Methodologies, Research Instrument and Research Dissemination
This individual assessment for the Business Research Methods module requires students to produce a structured report of no more than 3,500 words. The assessment is designed to develop students’ ability to identify and justify a suitable business research problem, critically evaluate research methodologies, design an appropriate research instrument, and consider how potential research findings could be effectively communicated to relevant audiences. The report consists of three interconnected sections that require students to demonstrate critical research skills and an understanding of how research can contribute to knowledge and practical decision-making. Section 1 focuses on comparing two research methodologies. Students select a topic of interest or a business problem relevant to their degree specialism, such as strategy, supply chain management, international business and economics, marketing, finance, or human resources. They first explain and justify the selected research problem and formulate a research question. Students then review relevant academic literature, justify the selection of key readings, and demonstrate the scope of the literature through a visual presentation such as a mind map, Venn diagram, or literature map. The section concludes with a detailed critical evaluation of at least two established research methodologies, considering their application, contribution to theory and research, advantages, limitations, constraints, and potential research gaps. Section 2 focuses on the development of a research instrument. Students design an appropriate instrument for their proposed research, such as an interview guide, survey questionnaire, or another suitable data-collection tool. The instrument is provided in an appendix, while the main report includes a reflection explaining how and why it was designed. Students are not required to collect data for this assessment. The section concludes by evaluating the expected value of the proposed instrument and its potential contribution to answering the research question. Section 3 addresses research dissemination. Students identify the audiences that could benefit from the potential research findings and explain why those audiences are relevant. They also consider appropriate methods of communicating research outcomes, including suitable summaries, media, report design, and potential partners such as public organisations, NGOs, associations, or industry. The assessment requires Harvard referencing and a bibliography containing at least 12 relevant journal articles, including at least six published within the previous 24 months. The report is assessed on the clarity of the research problem, depth of literature analysis, critical evaluation of research methods, quality of the proposed research instrument, understanding of research dissemination, and overall academic presentation.
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Data Science
1,000 words
Individual Project – Image Segmentation
This individual project focuses on image segmentation using a subset of the ADE20K dataset. The assessment requires students to investigate viable image segmentation models, analyse and visualise the provided data, develop appropriate data preprocessing procedures, train a segmentation model and critically evaluate the resulting performance. The assignment is worth 25% of the module assessment and is designed to assess students’ ability to apply research methods to a data science problem, communicate analytical findings effectively and select appropriate methods while understanding their advantages and limitations. Students are required to work with the provided ADE20K dataset and produce a model capable of semantic segmentation for four specified object classes: person, car, book and airplane. The model must identify which regions of an unlabelled image correspond to these four classes. The assignment specifically requires semantic segmentation rather than simply identifying whether an object is present. Treating the task as binary segmentation, where all objects are considered a single foreground class, will result in a significant penalty. The project requires students to investigate different approaches and models, perform exploratory data analysis, develop suitable preprocessing procedures and customise their selected model or models to improve performance. Students may use more than one model, although at least one model must be partially or fully trained by the student. Where multiple models are used, comparison with pretrained models is encouraged. Potential approaches include segmentation architectures such as U-Net and Mask R-CNN, with appropriate model selection justified through relevant literature and experimental evidence. The report should contain at least four core sections: an Introduction incorporating a literature review, Data Description and Exploratory Data Analysis, Methodology, and Results and Discussion. The literature review should cite at least three relevant research papers. The results section should include evaluation of three test images using the developed model and comparison with relevant published literature. Students are expected to provide a critical analysis of their work rather than simply reporting numerical results, explaining the reasons for observed outcomes and considering how modelling and implementation choices affected performance. The assessment also requires a documented Google Colab notebook containing the implemented steps used to train and evaluate the model. The notebook should be accessible to markers and should demonstrate the preprocessing, model development, training and evaluation process. The report must be between 700 and 1,000 words, excluding references and code, and should include code in text format as an appendix rather than screenshots. Figures and tables should have clear captions explaining what they present and their source. Assessment criteria include literature review and context, dataset description and exploratory data analysis, implementation quality, quality of analysis and critical discussion, and report quality and structure. Strong work is expected to demonstrate appropriate model selection, effective preprocessing and augmentation, suitable evaluation metrics such as Intersection over Union (IoU) and Dice score, meaningful visualisation and a critical interpretation of results and limitations.
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Cloud and Big Data Technologies
2,500 words
Cloud and Big Data Technologies – Summative Assessment: Cloud and Big Data Security Application
This summative assessment for the CST4067 Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud and big data security application. The assessment focuses on applying techniques for the complex transformation and processing of data within distributed and cloud-based environments, while considering security, privacy and access-control requirements. Students are expected to develop a practical application and document its design, implementation and evaluation through a technical report and a short demonstration video. Students are given flexibility to select their own project idea, provided that the proposed application is appropriately scoped for the available development period and demonstrates relevant cloud, big data and security technologies. Suggested project areas include data science applications using SQL, MongoDB and cloud storage, privacy-preserving data processing such as Differential Privacy, multi-party authentication, group-based security and access control, Multi-Level Security, Attribute-Based Encryption, and distributed or cloud-based applications incorporating security protocols such as SSH, SSL or IPSEC and access-control mechanisms such as RBAC. The Design and Implementation Document should be no longer than 2,500 words and should explain the major design and implementation aspects of the project. Expected content includes an introduction covering the aims, objectives, project concept, security concepts and cloud technologies used; a requirements specification addressing programme behaviour and security requirements; analysis and design covering protocols, access control and interaction, sequence diagrams or process specifications; implementation details explaining what was achieved and how it was developed; and an evaluation and conclusion discussing successful and unsuccessful aspects, problems encountered and lessons learned. Relevant references, including tutorials, books and academic articles, should also be provided using Harvard or IEEE referencing. The assessment also requires students to submit the implemented Cloud and Big Data Security application together with a highlight demonstration video. The video must be no longer than seven minutes and should demonstrate the main features of the application, including relevant interactions, implementation highlights, security features and, where appropriate, attack scenarios. Assessment is based on the Design and Implementation Document, originality, advanced features, and the effort and quality demonstrated in the application. The assessment specification places particular importance on original development, clear documentation of any tutorials or existing resources used, and evidence that the student understands the technologies implemented. Suggested technologies and project ideas include Google Cloud, Hadoop, Spark, cloud storage, data pipelines, security protocols, access control and privacy-preserving techniques.
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Principles of Management
1,000 words
Principles of Management – Amazon UK Management Report
This formative assessment for the Principles of Management module requires students to produce a 1,000-word management report analysing the management functions, theories, concepts and practices of Amazon UK. Acting as business consultants, students are required to prepare the report for the senior management team of Amazon UK and apply relevant management techniques and concepts covered within the module. The assessment focuses on the application of management knowledge to a contemporary organisation and the evaluation of the global business environment and effective management as a value-adding activity. The selected organisation is Amazon UK Services Ltd, an online retailer serving customers in the United Kingdom and Ireland. The company offers a broad range of consumer products and services, including electronic devices, books, groceries, clothing, home and garden products, Amazon Prime, Amazon Music, Kindle, Alexa, Amazon Pay and other digital and retail services. The assessment brief identifies Amazon UK as the organisation to be examined and recommends substantial independent research to strengthen understanding of its business context and management practices. The first task requires students to critically analyse key management theories, concepts and practices at Amazon UK. The report should explain how management contributes to the organisation's success and adds value, supported by appropriate academic literature, case study materials and independent research. Students are required to apply the four philosophies of the Competing Values Framework to Amazon UK and identify one additional relevant management theory that supports the organisation's management approach. Suggested theories include Scientific Management Theory, Bureaucratic Theory, Administrative Theory, Contingency Theory and Total Quality Management, although students are expected to select the most appropriate theory rather than attempting to apply all of them. The second task focuses on management as a value-adding universal activity and requires students to evaluate the dynamics of the global business environment. Students should consider how Amazon UK's management adapts strategies and methods at global, national and local levels. The assessment recommends applying either the LoNG-PEST framework or Porter's Five Forces to evaluate what Amazon UK should do differently. Students must identify one global factor that could create an opportunity and one that could pose a threat, and then identify areas where management can add value. The report should conclude by summarising the recommended changes Amazon UK should implement to improve its success. The required structure includes a BPP cover sheet, table of contents, optional list of abbreviations, introduction, Task 1, Task 2, references and appendices where required. The main body has a maximum word count of 1,000 words, with the suggested allocation of approximately 100 words for the introduction, 500 words for Task 1 and 500 words for Task 2. The cover page, contents, abbreviations, references and appendices are excluded from the word count. The assignment requires an academic writing style using third person for Tasks 1 and 2, professional and consistent formatting, appropriate citations, and Harvard referencing. The assessment emphasises the application of management theories and evidence to the Amazon UK case study, together with evaluation of management as a value-adding activity at global, national and local levels.
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Managing Customer Experience
1,000 words
Managing Customer Experience – Customer Experience Strategy Report
This assessment for the Managing Customer Experience module requires students to prepare a 1,000-word business report evaluating the customer experience (CX) strategy of one selected banking organisation. Students must choose one company from the organisations provided in the assessment brief and analyse its customer experience strategy within the specific country in which it operates. The report is designed to develop a critical understanding of the importance of customer experience for business success and the ways in which organisations can create a seamless omnichannel customer journey. The assessment provides a choice of six banking organisations: Lloyds Banking Group in the United Kingdom, HDFC Bank in India, Habib Bank Limited (HBL) in Pakistan, Nabil Bank Ltd in Nepal, Prime Bank PLC in Bangladesh, and Access Holdings/Access Bank in Nigeria. Students are required to clearly identify the selected organisation and country in their report and use relevant customer experience concepts, academic literature, research and practical examples to support their analysis. The first major requirement is to explain the concept of customer experience and critically review its importance to the success of the selected banking brand. The report should consider the role of CX, the mutual benefits created for the organisation and its customers, and the impact of customer experience on business performance, including financial performance. Students should also provide specific examples demonstrating how the selected bank has used customer experience to create or strengthen competitive advantage. The second major requirement is to evaluate how the selected bank delivers a seamless omnichannel customer experience. This requires discussion of the organisation's approach to creating a consistent customer journey across relevant channels. Students must develop a detailed customer persona based on research and data analysis, covering the persona's profile, goals, pain points and motivations. They must also create a customer journey map using the recommended class template. The journey map should identify stages, needs, activities, feelings, pain points and opportunities for improvement. The report should evaluate the effectiveness of the selected bank's customer journey and identify areas where the organisation could improve the ease and quality of the customer's experience. The customer persona and customer journey map are important components of the assessment, and the words contained within these visual templates do not count towards the 1,000-word limit. The report must be written in an academic style and in the third person. Harvard referencing is required for academic sources, figures, diagrams and independent research. The main report has a maximum word count of 1,000 words, excluding the cover sheet, title page, table of contents, references and appendices. The assessment therefore combines academic research, customer experience theory, organisational analysis, customer persona development, journey mapping and critical evaluation of omnichannel customer experience.
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Operations and Supply Chain Management
2,000 words
Operations and Supply Chain Management – Individual Reflective Report
This assessment is an individual 2,000-word reflective report for the Operations and Supply Chain Management module at De Montfort University. The report requires students to reflect critically on their experience of completing Assessment One and to demonstrate how their learning developed throughout the project. The assessment focuses on reflective practice, application of relevant theories, critical analysis of team and project experiences, and the development of an action plan for future professional practice. The report begins with an introduction explaining why reflection on practice is important and connecting reflective practice to relevant learning theories where appropriate. Students are required to identify the particular areas they will focus on within the report and explain why these areas are important and interesting to explore. This section establishes the purpose and scope of the reflection and provides a foundation for the critical discussion that follows. The main body should use appropriate sub-headings to organise the discussion into different areas. Students should explain how the team collectively established specific approaches relating to the topics selected in Assessment One. The report should then examine how the approach changed as the project progressed and connect these changes to relevant theories. Both successful and unsuccessful aspects of the project should be critically considered, rather than simply describing what happened. Detailed examples from the situation analysis and problem-solving components of Assessment One should be used to support the arguments and demonstrate meaningful reflection. The report should demonstrate critical understanding of operations and supply chain management issues, including the ability to critically appraise and adapt to tangible, intangible and information-related aspects of the global supply chain. It should also reflect on capacity planning and process design decisions relating to the production of goods and services. The emphasis is on connecting practical project experience with relevant academic concepts and theories. The conclusion should identify the key learning points gained from the project process and the consultancy presentation. Students should reflect on how these experiences have contributed to their knowledge, skills and professional development. The final part of the report should present a clear action plan explaining how the learning gained from the project will be taken forward into the student's future career. The assessment is worth 30% of the module and has a required length of 2,000 words, with an accepted range of 1,800–2,200 words. Assessment criteria include introduction and rationale, application of theory and concepts, critical analysis and evaluation, conclusion and action plan, and academic skills including Harvard referencing, source quality and quantity, structure and presentation.
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Managing Customer Experience
2,500 words
Customer Experience Strategy: Critical Evaluation of a Global Mobile Phone Brand
This assessment requires a 2,500-word business report critically evaluating the customer experience (CX) strategy of one company operating within the global mobile phone industry. Students must select one company from Samsung, Apple, Huawei or Google and analyse its customer experience strategy within one specific country in which the company operates. The report is written from the perspective of a CX consultant and is intended for the Board of Directors of the selected company. Relevant customer experience concepts, academic literature and practical business applications should be incorporated throughout the report. The first part of the report examines the concept of customer experience and its importance to business success. The analysis should consider the role of CX, the mutual benefits created for the company and its customers, the effect of CX on business and financial performance, and specific examples of how the selected brand has used customer experience to achieve competitive advantage. The second part evaluates how the selected company delivers a seamless omnichannel customer experience. This requires consideration of customer personas and customer journey mapping and their contribution to developing an effective customer strategy. Students must create a detailed customer persona based on research and data analysis, covering the profile, goals, pain points and motivations of the selected customer. A customer journey map should then identify stages, needs, activities, feelings, pain points and opportunities for improvement. The report should evaluate the effectiveness of the company's customer journey and suggest practical improvements. The words contained within the recommended customer persona and customer journey map templates are excluded from the 2,500-word limit. The third part evaluates the effectiveness of the company's use of customer experience metrics to measure the quality of its relationship with customers. Students must discuss three CX metrics covered in the module and critically assess their benefits, limitations and relevance to the selected company. The analysis should explain why these metrics would be useful for measuring the mutually beneficial relationship between the company and its customers. The final part critically reflects on the company's ability to create a customer-centric culture in an increasingly digital environment. This includes evaluating CX leadership and CX governance, as well as one additional critical success factor such as people, CX structure, strategy and process, or innovation. The report should conclude by evaluating the overall quality of the company's relationship with its customers and proposing practical ways to improve customer experience in the era of increasing digitalisation. The assessment addresses four learning outcomes relating to the importance of customer experience, organisational CX performance metrics, seamless omnichannel customer journeys and customer experience strategies in the context of rapid digitalisation. The submission must use Harvard referencing and an academic business-report style.
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Network Systems and Administration
2,500 words
Automated Container Deployment and Administration in the Cloud
This assessment focuses on the practical application of network systems administration, cloud infrastructure and automation technologies. The assignment requires students to design and implement an automated deployment process for a server running a Docker container on a cloud platform such as AWS, Azure or GCP. The assessment develops practical understanding of infrastructure automation, configuration management, containerisation and continuous integration and continuous deployment (CI/CD). The project requires the integration of at least two automation tools from technologies such as Terraform, AWS CloudFormation, Ansible, Azure DevOps, GitHub and GitHub Actions. The first stage involves provisioning the required cloud infrastructure, including a server instance, networking components and appropriate security configurations. Students must provide Terraform scripts, CloudFormation templates or equivalent infrastructure-as-code configurations together with an architecture diagram showing the deployed resources. The second stage focuses on configuration management. Ansible or an equivalent automation tool is used to configure the server environment, including installing and configuring Docker and ensuring that the required services start automatically. Students must provide the relevant playbooks or configuration scripts and a README explaining the overall automation flow. The assessment then requires students to containerise a sample web application using a Dockerfile and automate the deployment of the resulting Docker container to the provisioned cloud server. A CI/CD pipeline must also be implemented using Azure DevOps or another suitable CI/CD platform. The pipeline should automatically build and deploy the Docker container whenever changes are pushed to a version-control repository such as GitHub. The final stage requires comprehensive technical documentation and reflection on the complete automation process. Students must document the different phases of deployment, explain the rationale for the selected tools, discuss alternative solutions and identify the challenges encountered and how they were addressed. The final report must include a detailed architecture diagram, a GitHub repository containing the required scripts and configurations, and a working link to a demonstration video of the end-to-end deployment. The technical report should be approximately 2,000–2,500 words and include a title page, summary, introduction, main content, conclusions, references and appropriate appendices. The report should use Times New Roman 12-point font with 1.5 line spacing, clear headings, diagrams or flowcharts where appropriate, and Harvard referencing. The assessment is evaluated through the demonstration, report quality and reusable deployment artifacts, with the marking scheme allocating 40% to the demo, 50% to report quality and 10% to artifacts.
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International Business Strategy
250 words
Individual Reflection on Case Study Discussion – International Business Strategy
This assignment is an individual reflective assessment for the International Business Strategy module. It focuses on developing students’ ability to critically reflect on their learning experiences and identify how learning from classroom discussions can improve their future academic and professional performance. The assessment is based on a Case Study Discussion undertaken during one of the teaching weeks from Week 5 to Week 8. Students are required to select one of the case studies discussed during the relevant seminar sessions and reflect on their learning from the discussion. The case-study discussions provide an opportunity to explore important issues in the business world, apply concepts introduced through Canvas preparation and face-to-face teaching, and exchange ideas with tutors and peers. The reflection therefore connects theoretical knowledge with practical business situations and encourages students to consider how strategic management concepts can be applied to real-world organisations. The reflective task requires students to explain the topic and case study discussed during their selected week, identify what they learned from the discussion, and consider how the experience helped them develop their ability to apply theories to their chosen company. Students should also reflect on their key takeaways, what went well during the discussion, challenges they encountered, and what they would change if they were to repeat the experience. The reflection should be between 150 and 250 words and should use an appropriate reflective framework, such as Gibbs' Reflective Cycle or Kolb’s Experiential Learning Cycle, to structure the discussion. Harvard referencing should be used where applicable. The assessment develops critical reflection, strategic thinking, international business understanding and the ability to connect academic concepts with practical business cases. The assessment is worth 15% of the module and is submitted through Canvas. The marking criteria consider the quality of reflection, critical analysis, personal learning insights, presentation and structure, intellectual curiosity, referencing, content and discussion.
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Time Series Modelling
Time Series Modelling Case Study – Oil Price Forecasting
This individual Time Series Modelling Case Study focuses on analysing and forecasting oil price data using established time-series techniques and an alternative modelling approach. The assessment requires students to work with daily oil price information covering the period from 2024 to 2026 and investigate the underlying patterns, stationarity and forecasting behaviour of the data. The first part of the assignment involves exploratory data analysis and time-series modelling using an ARMA-based approach. Students are required to create appropriate visualisations of the data, perform exploratory analysis and conduct tests for non-stationarity, including relevant stationarity diagnostics such as ADF, ACF and PACF analysis and differencing where required. An ARMA model must then be defined, with suitable model parameters identified using the AIC likelihood approach. The assessment requires the student to examine possible combinations of model parameters, assess model residuals, evaluate model performance using appropriate metrics such as RMSE, and produce forecasts extending 24 months into the future. Confidence intervals must also be included with the forecasts. The second part requires students to investigate an alternative modelling solution for the same oil-price time-series data. Possible approaches discussed in the assignment include models such as LSTM and Prophet. Students are expected to conduct a literature review relating to the selected alternative model, build and apply the model, tune relevant hyperparameters where appropriate, generate 24-month forecasts, create suitable visualisations and calculate appropriate evaluation metrics. The final part of the assessment requires a 6–8 page report describing the modelling process, forecasts, analysis and inferences. The report should explain the reasoning behind the analytical and modelling choices rather than simply presenting numerical results. Students are expected to critically discuss why particular approaches were selected, how the modelling decisions may have influenced the results, how forecasts compare with subsequently collected real data where available, and what improvements could be made in future work. The assessment evaluates both the technical implementation and the quality of the written analysis. The code component assesses completion of the modelling and forecasting tasks, stationarity testing, the alternative solution, code quality and annotation. The report component assesses discussion of the analysis and inferences, comparison of the modelling approaches, clarity of interpretation, report structure, appropriate use of figures and suitable academic references. The submission consists of a report in PDF or Word format, with the code submitted separately or through an appropriate repository.
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1,000 words
Organisational Strategy and Sustainability: Strategic and Sustainability Analysis of Engineers & Planners Company Ltd
This formative Organisational Strategy and Sustainability assessment requires students to act as a management consultant for BPP Consulting Group and provide a strategic and sustainability evaluation of ENGINEERS & PLANNERS COMPANY LTD for its Board of Directors. The task focuses on how internal capabilities, external market forces, sustainability pressures, ethical practices and responsible leadership shape the organisation’s strategy within an increasingly global business environment. Formative Assessment - Organisa… ENGINEERS & PLANNERS COMPANY LTD is described as a Ghanaian-owned mining and construction contracting company, established in 1997 and headquartered in Accra. Its activities include contract mining, road construction, tailings-dam construction, land reclamation and hard-rock mining, with operations in Ghana and Liberia. The company also owns Dzata Cement, a cement manufacturing facility in Tema with an annual production capacity of approximately 2.3 million metric tons. Formative Assessment - Organisa… Formative Assessment - Organisa… The formative task addresses the first two learning outcomes of the summative assessment. LO1 – Strategic Environmental Analysis requires a brief but critical evaluation of the organisation’s internal resources and capabilities together with its external environment. Students should use one internal and one external strategic framework, with examples including PESTEL or Porter’s Five Forces, identify internal competencies, opportunities and threats, and consider how the organisation might respond to dynamic market forces in regions such as the European Union, Asia or North America. Sources of competitive advantage should also be identified from the internal analysis. Formative Assessment - Organisa… Formative Assessment - Organisa… LO2 – Ethical, Sustainable and Responsible Practices requires students to assess how ENGINEERS & PLANNERS COMPANY LTD could integrate CSR, sustainability or ethical practice into its wider business strategy. The brief recommends applying one framework such as the Triple Bottom Line or Sustainable Supply Chain Management. The discussion should connect the sustainability analysis back to the strategic issues identified under LO1. Formative Assessment - Organisa… Formative Assessment - Organisa… Students are also expected to consider how leadership shapes the organisation’s sustainability response, including engagement with stakeholders such as suppliers, customers and NGOs and the influence of regulatory frameworks such as the UN Sustainable Development Goals and relevant European Union legislation. One specific sustainability initiative should be briefly considered, such as product lifecycle management, eco-design, renewable-energy integration or supply-chain transparency. Formative Assessment - Organisa… The recommended report structure consists of an introduction, LO1 Strategic Environmental Analysis, LO2 Ethical, Sustainable and Responsible Practices, and a conclusion summarising recommended strategic and sustainability improvements. Suggested allocations are approximately 50 words for the introduction, 450 words for LO1, 450 words for LO2 and 50 words for the conclusion. Formative Assessment - Organisa… The total submission limit is 1,000 words, with the main body subject to the word-count restriction. The work must use third-person academic writing, professional formatting, appropriate tables and figures, and consistent Harvard citations. Formative Assessment - Organisa… Higher-quality work is expected to go beyond descriptive use of strategic models by critically evaluating the organisation’s internal and external environment, identifying key drivers of change, considering global and local influences on strategic choices, and linking sustainability and responsible-business practices to stakeholder expectations and regulatory pressures. Formative Assessment - Organisa… Formative Assessment - Organisa…
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Research Methods / Project Management / Computer and Information Sciences
3,000 words
Critical Literature Review in Research Methods and Project Management
This Research Methods and Project Management assessment develops students’ ability to search for, evaluate, critically analyse and synthesise academic literature within a computing or information-science research area. Students work within an allocated group to conduct a structured literature search and critically evaluate research papers relevant to an assigned theme, before using the combined group literature corpus to produce an individual literature review. KF7028 - Assessment 1 - Semeste… Each group member must identify and critically analyse a minimum of five academic papers using the supplied Critical Paper Summary template. These individual summaries are then combined into a single group document and shared so that all team members can use the collective body of research. The quality of this component depends on the relevance and academic quality of the selected papers and the depth of critical evaluation rather than simple description. KF7028 - Assessment 1 - Semeste… The main individual component is a 3,000-word critical literature review, worth 60% of the assessment. Students must use the literature identified and summarised through the group activity and critically discuss the research associated with their allocated topic. The review should synthesise the literature into a coherent discussion rather than presenting disconnected paper summaries, demonstrating criticality, clear structure and effective academic writing. KF7028 - Assessment 1 - Semeste… A further 20% is awarded for an individual research and meeting log, maintained through Blackboard. Students are expected to record their research progress, contribution to the group, approach to collaboration and reflections on the literature-review process. Stronger logs demonstrate detailed evidence of active participation, reflective insight and professional collaboration rather than merely listing completed tasks. KF7028 - Assessment 1 - Semeste… KF7028 - Assessment 1 - Semeste… The assessment directly evaluates the ability to apply project-management principles to a computing-related research activity and to search, evaluate and develop a critical literature review. Wider module outcomes also emphasise research techniques, data and information analysis, professional research practice, ethics, risk, legal issues, societal considerations and sustainability. KF7028 - Assessment 1 - Semeste… The marking structure allocates 20% to the combined critical paper analysis, 20% to the research/meeting log and 60% to the individual literature review. Higher-performing work is expected to use high-quality and directly relevant academic sources, demonstrate strong critical analysis, synthesise evidence across the research theme, maintain a professional academic structure and apply accurate Harvard referencing throughout. KF7028 - Assessment 1 - Semeste… KF7028 - Assessment 1 - Semeste… Important for the public Reference Library: the brief states that ChatGPT or other AI tools must not be used to generate text or fill in assessment-template sections. Any AI use that supports the work or thinking must be declared, referenced and accompanied by a prompt log in an appendix. Therefore, this entry should be used only as a high-level public description of the assessment rather than as directly submissible student content. KF7028 - Assessment 1 - Semeste…
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Artificial Intelligence / Natural Language Processing / Deep Learning
Applying Advanced AI Methods for Analysing Text Documents
This Advanced Artificial Intelligence coursework requires students to implement and evaluate natural language processing, natural language understanding and neural-network techniques for analysing text documents. The assessment uses a supplied social-media dataset containing more than 89,000 posts linked to 947 news headlines, with each post labelled as real or fake through a combination of headline ground truth and majority-vote annotation. Each social-media post is treated as an individual text document for classification and topic-analysis purposes. CMP_6059B_7059B_2025_26_CW1-pre… The first major task focuses on identifying fake text documents. Students must preprocess the text using appropriate NLP techniques, transform documents into numerical feature representations and experiment with alternative preprocessing approaches to determine which performs best. A separate unseen test set must be reserved to evaluate generalisation, while the remaining data is used for training both shallow and deep neural-network classifiers. Students are expected to explain and justify how the data is split. CMP_6059B_7059B_2025_26_CW1-pre… Students first design a multi-layer perceptron (MLP) capable of predicting whether documents are real or fake. The architecture must be justified in terms of input dimensions, number of layers, neuron counts, activation functions and outputs. A second deep-learning neural network must then be developed for the same classification problem, with justification of the chosen network structure, layer types, activations and other configuration decisions. CMP_6059B_7059B_2025_26_CW1-pre… For both networks, students train baseline models and select three hyperparameters considered most important for improving performance. These hyperparameters must be tuned systematically, with visuals prepared to show the experimentation process and resulting performance changes. Appropriate evaluation metrics are then used to compare the trained models. The strongest MLP and deep-learning models must be saved so that they can be loaded and tested on unseen data during the final demonstration without retraining. CMP_6059B_7059B_2025_26_CW1-pre… The second task focuses on topic discovery using NLP and NLU techniques. Students perform syntactic preprocessing such as tokenisation, stop-word removal and lemmatisation or stemming, and experiment with at least two different text-representation approaches. Suggested methods include Bag of Words, TF-IDF, LDA, word vectors and word embeddings. Students must interpret the discovered topics and explain how those topics relate to document content, linked news headlines and class labels. The best topic-discovery model or models must also be saved for live analysis during the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… The assessment is completed through a bench demonstration, supported by a maximum of seven PowerPoint slides. The slides should document the system design, model-improvement process, performance evaluation and discussion of results for both fake-document classification and topic discovery. Students also submit a ZIP file containing only their Python source files. The demonstration lasts up to 15 minutes, consisting of approximately 10 minutes for presentation and technical demonstration followed by 5 minutes for questions and transitions. CMP_6059B_7059B_2025_26_CW1-pre… The marking scheme allocates 45% to fake-document identification, including descriptive analysis, preprocessing, MLP design and deep-learning design; 35% to topic discovery, including preprocessing, model development and interpretation; and 20% to the structure, organisation, professionalism and Q&A quality of the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… Important for the public Reference Library: the brief explicitly states that the use of Large Language Models or generative AI to produce any part of the submission is strictly prohibited, including code, data processing, testing, writing or PowerPoint content. Therefore, this entry should remain only a high-level public description of the assessment and should not be presented as material intended for direct student submission. CMP_6059B_7059B_2025_26_CW1-pre…
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Network Systems and Administration / Cloud Computing / DevOps
2,500 words
Automated Container Deployment and Administration in the Cloud
This practical Network Systems and Administration assessment requires students to design and implement an automated cloud deployment workflow using modern DevOps and infrastructure automation technologies. The overall objective is to deploy a cloud-hosted server running a Docker container while integrating at least two automation tools such as Terraform, AWS CloudFormation, Ansible, Azure DevOps, GitHub Actions or GitHub. Students may use a cloud platform such as AWS, Microsoft Azure or Google Cloud Platform. B9IS121 Network Systems and Adm… The first stage focuses on cloud infrastructure provisioning. Students automate the creation of a server instance, such as an AWS EC2 instance or Azure virtual machine, and configure supporting infrastructure including networking and security groups. Infrastructure must be provisioned through tools such as Terraform, GitHub Actions or AWS CloudFormation. The required outputs include reusable infrastructure scripts or templates and an architecture diagram showing the deployed cloud resources. B9IS121 Network Systems and Adm… The second stage addresses configuration management. Students use Ansible or an equivalent automation platform to configure the provisioned server, install Docker and ensure Docker starts automatically when the server boots. Deliverables include Ansible playbooks or equivalent configuration scripts together with a README file explaining the automation workflow. B9IS121 Network Systems and Adm… The third stage requires students to containerise and deploy a web application. A Dockerfile must be created for either an existing or newly developed sample application, and deployment of the resulting container must be automated on the provisioned server. The required artefacts include the Dockerfile, deployment scripts and application repository. B9IS121 Network Systems and Adm… Students must also create a CI/CD pipeline using Azure DevOps or another suitable CI/CD platform. The pipeline should automatically build and deploy the Docker container whenever new code is pushed to a version-control repository such as GitHub. Students must provide the relevant pipeline configuration, normally in YAML, and demonstrate the pipeline operating successfully. B9IS121 Network Systems and Adm… Documentation is an important part of the assessment. Each student produces an independent PDF report explaining every stage of the automation process, challenges encountered and how those challenges were resolved. A 10-minute walkthrough video must demonstrate the complete end-to-end deployment, and the report must contain a detailed architecture diagram. B9IS121 Network Systems and Adm… The technical report should include a title page, summary, introduction, main technical content, conclusion, references and appropriate appendices. Students are expected to justify the tools selected and critically discuss alternative solutions, advantages and limitations. The report is expected to contain approximately 2,000–2,500 words and should use clear headings, diagrams or flowcharts where relevant, and academic or professional sources such as journal articles, whitepapers, DevOps documentation and industry reports. B9IS121 Network Systems and Adm… B9IS121 Network Systems and Adm… The marking scheme allocates 40% to the demonstration, 50% to report quality and 10% to technical artefacts. Higher-performing submissions are expected to show a working deployment, clear justification of technical actions, strong understanding of automation tools, professional report structure, consistent referencing and reusable scripts that reproduce the same deployment results. B9IS121 Network Systems and Adm… The final submission includes a comprehensive PDF report of approximately 5–8 pages, a detailed README, and links to the relevant scripts, configurations and deployment evidence. B9IS121 Network Systems and Adm…
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International Human Resource Management / International Business
800 words
Comparative Analysis of Cultural Factors Shaping Human Resource Staffing Strategies in International Enterprises
This individual international human resource management assessment examines how cultural and institutional differences influence recruitment and staffing decisions in international enterprises. Students take the role of an HR representative for VetDopomoga, a Ukraine-based international veterinary clinic chain planning to expand into either the United Kingdom or India. The final output is a concise academic poster of up to two pages and approximately 800 words ±10%, supported by appropriate visual material. Assignment 1_The Comparative An… The first component requires comparison of the United Kingdom and India with Ukraine. Students examine relevant cultural characteristics, including communication style, hierarchy and power distance, individualism versus collectivism and approaches to time, alongside institutional factors such as labour-market conditions and employment or regulatory environments. This comparison provides the contextual basis for evaluating how recruitment practices may need to change across markets. Assignment 1_The Comparative An… Students must then apply an appropriate international HR staffing strategy, choosing from ethnocentric, polycentric, regiocentric or geocentric approaches. Based on their analysis, they must recommend which market VetDopomoga should enter first and which should follow, providing clear evidence-based justification. Assignment 1_The Comparative An… A central theoretical component requires application of one cultural framework, selected from the Cultural Intelligence framework, Erin Meyer’s Culture Map, the GLOBE Project or Hofstede’s Cultural Dimensions Theory. Students focus on two dimensions from their selected framework and use them to evaluate the benefits and challenges associated with recruitment practices in Ukraine, the United Kingdom and India. Assignment 1_The Comparative An… The poster must also examine a recent cultural, economic or political event affecting the three national contexts and explain its implications for international recruitment and staffing. Examples suggested in the brief include changing social attitudes following COVID-19, economic shifts affecting demand for specialist labour and post-Brexit employment or migration policies. Assignment 1_The Comparative An… Students additionally research best-practice recruitment examples from international veterinary enterprises operating in the UK and India and identify practices VetDopomoga could adopt or adapt in each market. The final recommendation must identify the preferred first expansion destination and provide three evidence-based reasons supporting the decision. Assignment 1_The Comparative An… Assessment criteria place strong emphasis on content and findings, theoretical application, critical analysis, source quality and Harvard referencing, as well as poster presentation and structure. High-quality work is expected to go beyond description by applying cultural theory directly to HRM practices and critically evaluating the implications of cultural and institutional differences for international recruitment decisions. Assignment 1_The Comparative An… Assignment 1_The Comparative An…
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Data Science / Time Series Analysis / Machine Learning
Time Series Modelling Case Study: Oil Price Forecasting with ARMA and Alternative Models
This Time Series Modelling Case Study requires students to analyse real-world oil-price time-series data and develop forecasting models capable of predicting future values. The assessment combines traditional statistical time-series techniques with an alternative forecasting approach, requiring students to demonstrate practical modelling skills, critical research engagement and evidence-based interpretation of forecasting results. The coursework is completed individually and contributes 40% of the assessment. assing,,, (1) The assessment is divided into three main parts. Part 1 focuses on developing an ARMA-based forecasting model using daily oil-price data covering approximately 2024 to 2026. Students begin with exploratory data analysis and initial visualisation before testing whether the time series is stationary. Where necessary, appropriate transformations or differencing must be applied to obtain stationarity. Students then define an ARMA model and identify suitable p, d and q parameters using an AIC-based model-selection procedure across the parameter ranges specified in the brief. assing,,, (1) Model adequacy must be assessed using diagnostic analysis. Students inspect residuals, generate additional ACF plots, examine residual distributions and evaluate prediction performance using appropriate metrics such as RMSE. The selected model is then used to forecast oil prices 24 months into the future, with appropriate confidence intervals added to communicate forecast uncertainty. assing,,, (1) Part 2 requires students to research and implement an alternative forecasting approach. Suggested examples include LSTM and Prophet, although another appropriate model may be proposed. Students conduct a literature review supporting the alternative method, build and where relevant hyperparameter-tune the model, generate another 24-month forecast, visualise predictions and confidence intervals, and calculate suitable evaluation metrics. This component is intended to demonstrate independent research and the ability to propose an alternative solution rather than relying only on the conventional ARMA approach. assing,,, (1) Part 3 consists of a 6–8 page technical report explaining the modelling process, forecasting results and resulting inferences. The report should provide a critical analysis rather than simply reproducing numerical outputs. Students are expected to explain why results occurred, justify modelling choices, evaluate how those choices influenced performance, compare forecasts with subsequently observed real data where possible, and construct a coherent narrative supported by plots, images, summary statistics and academic literature. Future improvements to the modelling approach should also be critically discussed. assing,,, (1) Submission consists of both the report and working code. The code may be submitted directly or through an accessible Colab or GitHub repository and must reproduce all models, figures and numerical results presented in the report. The assessment allocates 60% of the marks to code and 40% to the report. Within the coding component, modelling and forecasting completion accounts for 40 marks and code quality and annotation for 20 marks. The report is assessed on analysis and inference, methodological justification, comparison of the two modelling approaches, presentation quality, figures and use of appropriate references. assing,,, (1) Key technical expectations include appropriate testing for stationarity, use of methods such as ADF, ACF, PACF and differencing, systematic model selection, forecasting, evaluation and clear comparison between the traditional ARMA model and the chosen alternative approach. Higher-quality work is expected to interpret what the forecasts mean, identify potential improvements and demonstrate sound technical communication rather than merely reporting model outputs. assing,,, (1) Important for the public Reference Library: the brief explicitly states that students must not use generative AI to write the report, and the rubric indicates that AI text-generation use may result in zero marks for the whole assignment. Therefore, the public entry should remain a high-level description of the assessment rather than material intended for direct submission. assing,,, (1)
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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 / Artificial Intelligence / Generative Modelling
Generative Modelling Case Study: GANs for Medical Imaging, Cybersecurity and Creative AI
This Generative Modelling Case Study requires students to design, implement and evaluate Generative Adversarial Networks (GANs) across a range of synthetic and real-world applications. The coursework develops both theoretical understanding and practical deep-learning skills, with emphasis on building models, evaluating generated data and critically interpreting model performance. The assessment addresses research understanding, originality, future development and scholarly communication in data science. Generative modelling case study… Part 1 – Building and Understanding GANs from Scratch focuses on fundamental GAN concepts using synthetic two-dimensional data. Students first reproduce a sine-wave GAN from the tutorial and then create a second synthetic distribution using either a 2D spiral, a mixture of Gaussians or a noisy parametric curve. They must modify aspects of the GAN architecture, such as activation functions or network depth, and visually compare generated samples with the original data distribution. Generative modelling case study… Part 2 – Real-World GAN Applications extends the work across three application domains. The first application uses the BloodMNIST subset of MedMNIST to train a DCGAN that generates synthetic blood-cell microscope images. Students explore the dataset, analyse class distributions, train the model, monitor generator and discriminator losses and compare real and generated images using both visual inspection and quantitative measures such as the Fréchet Inception Distance (FID). An optional extension involves implementing a class-conditioned GAN capable of generating images from specific categories. Generative modelling case study… The second application addresses cybersecurity using the CICIDS 2017 intrusion-detection dataset. Students construct a GAN that generates synthetic network-traffic feature vectors rather than images. The model is trained using benign and denial-of-service traffic, and generated samples are compared with real traffic using dimensionality-reduction techniques such as PCA or t-SNE. Students must evaluate how closely the synthetic traffic reflects the distribution of genuine network data. An extension allows analysis of the full CICIDS dataset and evaluation across different attack types. Generative modelling case study… The third application explores Creative AI using the Google QuickDraw pizza category. Students implement another DCGAN to generate artificial pizza sketches, track training behaviour across epochs, and compare generated sketches with genuine examples using both visual inspection and quantitative metrics such as FID. Extension work may examine additional QuickDraw categories and investigate how model performance changes with class and sketch complexity. Generative modelling case study… Submission consists of a 6–8 page report together with working code. The report should explain the analysis undertaken, justify modelling decisions, describe the network architectures, interpret the results and incorporate suitable figures, evaluation metrics and references. The accompanying code must reproduce the figures, models and numerical results reported and must execute successfully when tested. Generative modelling case study… The marking scheme places 60% of the marks on code and 40% on the report. Within the coding component, 40 marks relate to completing the GAN modelling tasks and 20 marks assess code quality, modularity and annotation. The report is assessed on discussion and interpretation of the analysis, justification of architectural choices, results presentation, document quality, figures and appropriate academic references. Generative modelling case study… Important for the public Reference Library: the brief explicitly states that students must not use generative AI to write the report, and the rubric states that AI-generated report text can result in zero marks for the whole assignment. Therefore, use this entry only as a high-level public description of the assessment and do not present generated report content as something students can submit directly. Generative modelling case study… Generative modelling case study…
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Software Engineering / Software Measurement and Process Models
2,000 words
Software Measurement, Goal–Question–Metric Analysis and Process Model Evaluation for DETO
This Measures and Models in Software Engineering coursework uses the Doolan Executive Taxi Service Online (DETO) case study to assess the application of software measurement techniques, Goal–Question–Metric analysis and software process modelling. The assignment requires students to connect quantitative software-engineering measures with practical organisational objectives and to critically evaluate suitable development-process approaches for a safety- and reliability-sensitive software system. 7COM1024_MMSE_ Def_CW_Brief 202… Task 1 focuses on software measurement using Unadjusted Function Points. Students analyse part of the DETO web-based email attachment system and identify relevant functional elements, including user inputs, reports to users, prompts and responses, interfaces to external systems or files, and internal logical files. Appropriate weights must then be selected to calculate the unadjusted functional-point value. 7COM1024_MMSE_ Def_CW_Brief 202… Task 2 requires application of the Goal–Question–Metric (GQM) approach to evaluate the effectiveness of the DETO website. Students identify the object under measurement and define the purpose, quality focus, viewpoint and context. They then develop two measurable organisational goals, formulate relevant questions for each goal and identify metrics that could be collected to evaluate website performance and support the company's priority of attracting new customers. 7COM1024_MMSE_ Def_CW_Brief 202… Task 3 moves from software measurement to software process models. DETO is considering a sophisticated on-board navigation system for its next generation of vehicles, where fast development, high quality, reliability and integration with the existing website are strategically important. Students select two software process models and critically evaluate their suitability for the project. The discussion should compare their distinguishing characteristics against alternative approaches, identify development challenges, evaluate project risks and pitfalls, and propose appropriate risk-mitigation strategies. 7COM1024_MMSE_ Def_CW_Brief 202… The main discussion for Task 3 is limited to 2,000 words ±10% and must use the Harvard referencing system, with at least 10 sources expected. The brief specifically requires original prose and prohibits bullet points, lecture materials and diagrams for this section. 7COM1024_MMSE_ Def_CW_Brief 202… Overall, the assessment integrates functional software measurement, GQM-based metrics, process-model evaluation, software quality, reliability, systems integration and project risk management within a realistic software-engineering business scenario.
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Operations Management / Business Simulation / Process Improvement
3,500 words
Operations and Simulation Analysis: Sam’s Sandwich Shop
This operations-management portfolio uses the Sam’s Sandwich Shop case study to examine service-process performance, simulation modelling, resource planning and operational improvement within a busy takeaway food environment. The case concerns a small sandwich outlet located in the departure lounge of Heathrow Airport Terminal 5, where rising terminal capacity is expected to increase customer demand over a five-month period. Students investigate how the store’s current sandwich assembly and service process performs under this growing demand and identify opportunities for operational improvement. MGT4924 Assignment instructions… The assessment consists of three related tasks. Task 1 – Arena Simulation Model requires students to develop a valid and functioning discrete-event simulation of the current system using Arena Simulation software. The model must accurately represent customer arrivals, ordering and payment, movement between the service counter and sandwich assembly area, preparation of meat, cheese and vegetarian sandwiches, resource constraints, queues and the possibility of sandwiches being rejected and remade. A commentary of up to 500 words must be included within the model to explain its main components and operation. MGT4924 Assignment instructions… MGT4924 Assignment instructions… The case provides defined operational resources, including three servers, two cash registers, one meat counter, one cheese counter and one vegetarian counter. The same server completes the full customer-service cycle, and customers join a single FIFO queue when both a server and cash register are not immediately available. The simulation must run for one week, equivalent to 168 hours, using multiple replications. MGT4924 Assignment instructions… Task 2 – Simulation Report requires a professional 1,000-word report analysing the system’s performance across the five-month period using results generated from the simulation study. Students must conduct experiments to determine suitable resource levels for each month and provide justified recommendations concerning the number of resources required for optimal operational performance. Tables and figures must be created independently in Excel rather than copied directly from Arena outputs. MGT4924 Assignment instructions… Task 3 – Operations Report requires a 2,000-word critical report examining two operations-management concepts in the context of Sam’s Sandwich Shop and the wider takeaway-food sector. Students analyse how the selected concepts are or could be implemented, identify associated operational problems and impacts, evaluate potential solutions, and consider relevant tools, techniques, methods and emerging technologies that could improve performance and competitive advantage. Two detailed and justified recommendations must be provided for each selected concept. MGT4924 Assignment instructions… Overall, the portfolio integrates discrete-event simulation, process analysis, resource optimisation, operational performance measurement and evidence-based operations-management decision making. Strong submissions are expected to move beyond simply reporting simulation outputs by critically analysing performance, evaluating experimental results, presenting high-quality data visualisations and producing logical, well-justified operational recommendations. MGT4924 Assignment instructions…
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Software Engineering / Enterprise Systems Development
3,000 words
Enterprise Software Engineering Development: ParkaLot Parking Management System Analysis, Design and Prototype
This Enterprise Software Engineering Development assessment is based on the ParkaLot Group, a fictional operator of multi-storey parking garages across major UK cities. The organisation currently relies on fragmented manual processes, basic barrier-based vehicle counting, local spreadsheets, on-site payment and inconsistent customer access arrangements. Students act as software engineering consultants and are required to analyse these operational weaknesses and design a centralised enterprise parking management system capable of supporting reservations, customer accounts, vehicle identification, billing, real-time occupancy monitoring, dynamic pricing and management reporting. COMP1471 CW 2526 The proposed ParkaLot platform is intended to integrate customers, parking spaces, reservations and billing across the garage network. Planned functionality includes online registration and reservation, recurring and corporate parking arrangements, licence-plate recognition, automated space allocation, sensor-based occupancy tracking, centralised monthly billing, electronic payments, promotional pricing and predictive overbooking. The system must also provide dashboards and historical reporting to assist management with capacity planning, staffing, pricing and operational decision-making. COMP1471 CW 2526 The coursework is completed in two main development phases. Phase 1 – Structured Analysis and Design requires an Entity Relationship Diagram representing the logical data model, a Data Flow Diagram including a Level 0 context diagram, and implementation of a prototype database. Phase 2 – Object-Oriented Development extends the system using object-oriented analysis, design principles and UML, with substantial business and user-interface functionality implemented using suitable OO technologies. COMP1471 CW 2526 The final report contains analysis of the 5 Ps of software engineering: Problem, Process, Project, Product and People. Students discuss the business problem and commercial risks, justify the development methodology followed, define resources and budget, document project artefacts and requirements, and identify the main stakeholders involved in the project. The technical design section includes the ERD, DFD, UML use-case model, at least three sequence diagrams, a detailed class diagram and discussion of design patterns. COMP1471 CW 2526 Students must additionally submit a functioning prototype that reflects the design and participate in acceptance testing and a live demonstration. Individual students are questioned on both theoretical and technical aspects of the submitted system. The assessment also evaluates group contribution, peer and self-assessment, personal reflection, research quality, communication and professional teamwork. COMP1471 CW 2526 The weighting places substantial emphasis on technical design and implementation: the UML design is worth 24 marks, the software prototype 15 marks, design patterns 6 marks, and acceptance testing/demonstration 25 marks. This makes the coursework strongly focused on demonstrating the relationship between requirements analysis, software architecture, UML modelling, implementation quality and working enterprise-system functionality. COMP1471 CW 2526
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Data Mining / Data Science
Customer Service Escalation Risk Analytics Using Data Mining and Machine Learning
This advanced Data Mining assessment applies the Knowledge Discovery in Databases (KDD) process to a realistic customer-service risk scenario. Acting as a Data Scientist, students analyse a historical Customer Service Escalation Risk dataset to help an organisation identify early indicators of dissatisfaction and operational bottlenecks that may lead to serious or legal customer escalations. The resulting analysis is intended to support strategic decisions relating to staffing, employee training, customer-journey improvement and escalation prevention. CMP-7023B_Assessement_2 (2) The dataset incorporates customer demographics, account characteristics, communication channels, issue categories, operational measures such as waiting times, transfers and SLA breaches, behavioural indicators including sentiment and response delays, and commercial variables such as monthly fees and contract value. The target variable, escalation_level, contains four categories: No escalation, Minor escalation, Serious escalation and Legal escalation. CMP-7023B_Assessement_2 (2) Students begin with data exploration and visualisation, producing appropriate descriptive statistics and identifying patterns, distributions and potential data-quality concerns. They then perform data cleansing, transformation, feature engineering and preprocessing. Variables that may introduce leakage or unreliable predictions because of their meaning, timing or quality must be critically assessed and justified. CMP-7023B_Assessement_2 (2) The supervised-learning stage requires students to develop, tune and compare predictive models using techniques such as k-nearest neighbours, Decision Trees, Support Vector Machines, ensemble methods or neural networks. Appropriate multiclass evaluation metrics must be used, alongside interpretation of influential variables and model behaviour. CMP-7023B_Assessement_2 (2) The assessment also includes unsupervised learning, requiring comparison of clustering methods such as K-Means and hierarchical clustering after removal of the target variable. Students may apply encoding, normalisation and dimensionality-reduction methods such as PCA or t-SNE and must interpret how the resulting clusters relate to escalation behaviour. CMP-7023B_Assessement_2 (2) Overall, the project assesses independent analytical judgement, modelling justification, comparative evaluation and clear communication of actionable findings for both technical and executive audiences. CMP-7023B_Assessement_2 (2) Overview word count: approximately 340 words. AI-use note: AI tools may only assist with small, specific code snippets. Any AI-generated code must be clearly acknowledged and cited, while the analysis, coding decisions, interpretation and final evaluation must remain the student's own work. CMP-7023B_Assessement_2 (2)
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Software Engineering / Object-Oriented Programming / Java Development
Java Group Exercise Booking and Management System for Furzefield Leisure Centre
This software-development coursework requires students to design and implement a Java-based booking and management system for Furzefield Leisure Centre (FLC). The proposed system manages member bookings for group exercise lessons delivered on Saturdays and Sundays. Exercise types can include Yoga, Zumba, Aquacise, Box Fit and Body Blitz, with each lesson accommodating a maximum of four members and carrying an exercise-specific price. 7COM1025 Coursework Explanation… The application must enable members to search the lesson timetable either by day or exercise type and make bookings subject to capacity constraints. Members may book multiple lessons, but duplicate bookings for the same lesson are prohibited. Existing bookings can be changed when capacity is available in the replacement lesson or cancelled before attendance. Successful changes must retain the existing booking ID while releasing the place held in the original lesson. 7COM1025 Coursework Explanation… 7COM1025 Coursework Explanation… After attending a lesson, members can provide a written review and a numerical rating from 1 to 5. Booking statuses should distinguish booked, changed, attended and cancelled reservations. The system must also generate management reports showing attendance and average lesson ratings and identify the exercise type producing the highest income, while listing income generated by each exercise category. 7COM1025 Coursework Explanation… 7COM1025 Coursework Explanation… Students must design at least eight weekends of timetable data, equivalent to 48 lessons, covering at least four exercise types. The program should be self-contained and does not require an external database or security protocol. 7COM1025 Coursework Explanation… The software-engineering component requires a UML class diagram, Java implementation, JUnit testing, version-control evidence and consideration of design principles, design patterns and refactoring. Students submit a PDF report containing the UML diagram and repository evidence, a ZIP containing source code, tests and an executable JAR, and a screen-recording demonstrating the final system. 7COM1025 Coursework Explanation… 7COM1025 Coursework Explanation… Overall, the coursework assesses practical object-oriented software development through system modelling, implementation, validation and reflective documentation. The marking allocates 40 marks to system functionality, 30 to design and implementation, 10 to UML, 10 to JUnit testing, 5 to version control and 5 to report quality. 7COM1025 Coursework Explanation… Note: the uploaded material identifies the code 7COM1025 but does not state the formal module title. I would therefore keep Module name = Not specified rather than inventing one.
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Computer Networks / Network Security / Cloud and Software Defined Networking
3,500 words
Network Systems and Security: Ad Hoc, Cloud and Software Defined Networking Emulation
This Network Systems and Security coursework requires students to design, implement and critically evaluate a series of practical network-emulation environments covering wireless Ad Hoc networking, cloud services and Software Defined Networking (SDN). The project combines Python-based network configuration with practical connectivity testing, cloud deployment, controller-based networking and theoretical evaluation of contemporary network-security technologies. The first task involves designing an Ad Hoc wireless network representing an emergency-response scenario. Students configure at least three wireless stations using Mininet-WiFi, assign appropriate network parameters and demonstrate connectivity through ICMP communication. The task requires discussion of the design and implementation together with the Python script used to configure the emulated environment. 7COM1076+ref+def+CW+2025+26+ The second task focuses on cloud-service emulation. Students develop a simple static website, deploy it through Render.com and GitHub, and access the hosted service from a Mininet-emulated network. Required evidence includes the Python implementation, cloud configuration screenshots, commands used to provide internet connectivity, webpage access through an xterm environment and the associated HTML code. 7COM1076+ref+def+CW+2025+26+ The third practical component examines Software Defined Networking using the ONOS controller. Students construct an emulated topology containing hosts, servers and programmable switches, demonstrate complete ICMP connectivity and perform a TCP transmission lasting 600 seconds. Evidence must include the network-emulation script, ONOS graphical interface and connectivity results. 7COM1076+ref+def+CW+2025+26+ The final analytical section critically evaluates whether Software Defined Networking and Network Functions Virtualisation (NFV) complement one another and examines security algorithms used within cloud computing. Students compare two selected cryptographic approaches, evaluating their respective advantages and disadvantages. 7COM1076+ref+def+CW+2025+26+ Overall, the coursework integrates network modelling, wireless networking, cloud deployment, SDN control, Python scripting, connectivity testing and security analysis. The marking scheme gives substantial weight to system modelling, cloud and SDN implementation, ICMP/TCP functionality, technical analysis and overall report quality. 7COM1076+ref+def+CW+2025+26+
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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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Applied Data Science
500 words
Critique of a Data Science Book and Selected Chapter
This assignment requires students to critically evaluate a selected data science book and a specific chapter from that book. Students must choose one book from the list provided in the assignment, read the preface or introduction to understand the intended audience, and then select a chapter that is relevant to their interests, existing knowledge and learning objectives. The available books cover a range of data science and analytical subjects, including practical time series analysis, machine learning with Python, Python-based data science, technical analysis, Bayesian statistics and artificial intelligence applications. The main purpose of the assignment is to develop the student's ability to engage critically with technical literature rather than simply summarising its content. The critique should identify the selected book and its intended audience, clearly state the chosen chapter and explain the reasons for selecting it. Students are expected to consider what they hoped to learn from the selected material and then critically assess whether the chapter achieved these objectives. The assessment should consider the clarity, usefulness and accessibility of the material, as well as the extent to which it contributes to the student's understanding of data science concepts. Students should also discuss additional knowledge they would like to gain from the book and identify particular aspects that were either helpful or less useful. This may include the quality of explanations, examples, technical depth, practical applications, organisation of material and relevance to the student's existing knowledge. The critique should demonstrate engagement with the selected chapter and provide reasoned observations rather than simply describing what the author has written. The final submission is a 500-word critique with a permitted variation of plus or minus 10 percent, meaning the expected range is approximately 450–550 words. The text must be written as a continuous narrative and should not use subheadings for the individual assessment points. The headline should follow the format “Critique of <book title> by <book author>”, with the student's name and student ID as the subtitle. The assignment assesses both technical presentation and content, including grammar, writing style, word count, completeness, breadth and depth of the book assessment, critical analysis and evidence of engagement with the selected material. Students must submit text that can be processed by Turnitin.
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Environmental Sustainable Engineering
3,000 words
Environmental Sustainable Construction & Logistic Sources – EG7037
This coursework assesses Environmental Sustainable Engineering and Logistic Sources through a large-scale construction project scenario. The student is appointed as the project manager for a £320 million design-and-build project involving the development of a new shopping centre in central London. The project is planned over a 48-month period and is presented as a flagship initiative focused on environmentally friendly and sustainable construction practices, with government support for its sustainability commitments. The project manager is responsible for protecting the client's interests while ensuring that project standards, schedules, budgets, procurement requirements and quality expectations are achieved. The assignment is structured around environmental sustainability, construction logistics, procurement, renewable energy, health and safety, and sustainable design. Part A requires investigation of the environmental, economic, historical and cultural factors that could contribute to significant project costs and delays. It also requires discussion of four renewable or sustainable energy sources that could be used within the project, together with justification for each selected energy source. These activities require consideration of the project's location, sustainability objectives and practical construction requirements. Part B focuses on supply chain management and health and safety. Students must outline the supply chain management and procurement process for the material resources required for the construction project. This includes consideration of how appropriate materials and suppliers can be sourced and managed. The section also requires identification of key health and safety considerations during the production phase and discussion of the associated risks. Part C focuses on sustainable design systems. Students must discuss the need for implementing passive or active systems during the design stage and identify appropriate tools or techniques that can support their application. The potential environmental impacts of these systems must also be considered. The assignment therefore requires students to connect sustainable design decisions with environmental performance and the wider objectives of the construction project. The completed coursework should be approximately 3,000 words, excluding appendices and labelled diagrams or sketches. Students are expected to provide a well-researched and clearly structured written account using appropriate textbooks and other relevant sources. Images, photographs and diagrams should include captions and be cross-referenced within the text. The submission should include a conclusion and a properly cited bibliography or reference list. References must follow the Cite Them Right requirements. The coursework is submitted electronically through the designated Turnitin link.
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Integrated Communications
2,000 words
CW2 Communications Engagement Report – Client Stream
This assignment requires students to produce an individual 2,000-word Communications Engagement Report in the form of an engagement communications campaign for Stream. Stream is a collaboration between 16 UK water companies, supported by industry and civil society partners including the Open Data Institute, with the aim of unlocking the potential of water-sector data to benefit customers, society and the environment. The organisation has established data-sharing infrastructure designed to enable data to be used to generate insights, support innovation, improve decision-making and contribute to greater transparency across the water sector. The central focus of the assignment is to develop a communications campaign that attracts and engages the research and academic community. Stream has identified postgraduate and postdoctoral students, together with relevant university faculty leads, as an important priority audience because of their potential to transform published water-sector data into valuable research insights, innovative products and services. Relevant academic departments may include computer science, engineering, mathematics, environmental science, social science, town planning and business development. Students are expected to consider how this audience can be reached, segmented and prioritised according to its interest and willingness to engage with Stream. The proposed campaign should build awareness of Stream, communicate the value of accessing and using water-sector data, and encourage longer-term engagement between Stream, universities and students. Existing communication channels include LinkedIn, the Stream website and communications distributed through delivery partners such as the Open Data Institute and Aiimi, as well as Ofwat. Key campaign opportunities include Open Data Day in March and Open October in mid-October. These events should be considered as important campaign moments while also being integrated into a longer-term communications programme. The report should include an introduction and background to Stream, including brand history, market position, competitive analysis, the need for an engagement communications campaign and relevant market research. It should establish short-, medium- and long-term communication objectives with clear timeframes, define the target audience, and apply an appropriate theoretical framework to develop the communication strategy. Students must provide four recommended communication pieces supported by detailed rationale, together with a Gantt chart showing campaign timings and a clear campaign measurement approach. The report should conclude with a summary of the overall proposal. The assessment requires critical application of communication theories and concepts, research evidence and relevant literature. Students must demonstrate analysis, evaluation and justification rather than simply describing communication activities. A minimum of 15 high-quality references is required, including at least eight journal articles, using Cite Them Right Harvard referencing. The report is assessed on presentation and structure, intellectual curiosity and referencing, content and application, campaign integration, discussion, conclusion, recommendations, campaign timeframes and measurement.
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Digital Marketing
Three Weekly Digital Marketing Tutorial Challenges
This assessment consists of three weekly digital marketing tutorial challenges designed to develop practical skills in digital marketing, consumer insights, content creation, inclusive communication, ethical storytelling and campaign design. The assessment accounts for 45% of the module and requires students to apply digital marketing theory and consumer insight techniques in real-world marketing contexts. The activities are completed through tutorial-based learning, where students participate in discussions, receive tutor guidance and develop their work before submitting it through Canvas. The assessment focuses on applying consumer insights, data analytics tools and digital marketing strategies to create meaningful and responsible customer experiences. Challenge 1, titled “Are You Being Inclusive?” – Brand Audit & Persona for Ethical Reach, focuses on ethical digital auditing and inclusive persona design. Students select a brand from their home country and evaluate the inclusiveness and accessibility of its digital marketing across its website, social media and paid advertising. They then develop two to three detailed customer personas representing underserved or marginalised customer groups. Students also use AI analytics tools such as Google Trends, SparkToro or simulated AI insights to identify audience behaviours and preferences. The challenge is linked to SDG 10 – Reduced Inequalities and includes consideration of the ethics of using AI in digital marketing and its effect on the credibility of consumer insights. Challenge 2, titled “Don't Just Capture Attention – Respect It” – Campaign Idea Pitch, focuses on sustainable branding and growth. Students research the client's existing branding, examine opportunities to expand or sustain its audience and use consumer insight tools such as Google Trends, reviews and surveys to understand public perceptions. Students then create a visual mini-campaign promoting sustainable and transparent brand messaging, including social media content and AI-generated visuals. The campaign is connected to SDG 16 – Peace, Justice and Strong Institutions. Challenge 3, titled “Create, Educate, Disseminate & Evaluate”, develops digital social marketing and consumer engagement. Students extend the campaign developed in Challenge 2 into an interactive digital event that promotes positive behavioural change and educates audiences about a social issue. The activity must consider audience interests, increase interactivity and engagement, and provide evidence of positive engagement. This challenge is linked to SDG 4 – Quality Education. Overall, the assessment develops students' ability to combine digital marketing strategy, consumer insights, ethical considerations, sustainability and creative campaign design in practical marketing activities.
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Data Mining
Data Mining – Classification, Model Optimisation and Evaluation
This individual Data Mining assignment requires students to apply the techniques covered in the module using the WEKA data mining platform. The assessment is worth 40% and focuses on practical application of machine learning and data mining methods, requiring students to configure algorithms, analyse datasets, optimise model parameters, evaluate classification performance and explain their technical choices and results. The assignment also assesses the ability to critically evaluate different algorithms and models of data mining. The assignment includes several tasks covering different stages of the data mining process. Students are required to work with supplied datasets and use appropriate preprocessing and classification techniques. The datasets include a balanced screenshot dataset containing processed screenshots classified into categories such as "Okay" and "Bad", where the objective is to train a model capable of identifying inappropriate content. The data has been processed using PCA to provide a smaller four-dimensional representation while protecting privacy and reducing the size of the data. Another supplied dataset concerns furniture reviews, containing positive ("pos") and negative ("neg") written feedback, with the objective of building a model that can determine whether a furniture review belongs to either class. The assessment evaluates students' ability to understand and describe the datasets, including the number of instances, number of columns, data types and relevant statistical information. For text-based data, students must apply appropriate vectorisation and describe the resulting dataset characteristics. Students must also consider class imbalance and apply an appropriate method where necessary, explaining how the chosen approach affects the distribution of instances between the classes. A significant component of the assignment involves classification algorithms and parameter optimisation. The assessment requires students to work with algorithms including Naive Bayes, LibSVM and J48. Students must investigate appropriate parameters and perform parameter searches or fine grid searches to identify suitable configurations. They must explain the selected parameters, their impact on the model and the reasoning behind the chosen values. Model performance must be evaluated using appropriate validation techniques, including cross-validation. Students are required to compare the algorithms using results such as overall accuracy and confusion matrices. The assignment expects students to identify an appropriate or best-performing algorithm in the context of the dataset and to provide a clear explanation of the comparison rather than simply reporting numerical results. The rubric places emphasis on accurate configuration, clear explanation of parameter choices, dataset analysis, class-balance treatment, parameter optimisation, cross-validation and critical comparison of algorithm strengths and weaknesses. High-quality work should explain both the technical process and the implications of the results, with results presented clearly through appropriate tables, confusion matrices and graphical outputs where required. The submission must be a single PDF document containing the report and must not exceed 10 pages. Students are instructed to include their student ID at the beginning of the report but not their name or other identifying details so that marking remains anonymous. Screenshots are specifically required to demonstrate use of the student's ID number as the random seed; other WEKA results should be presented in the student's own tables or result formats. The brief also states that no research beyond the material covered in the module is required and therefore no citations or reference list are required. The assignment explicitly prohibits the use of Generative AI tools for creating content and prohibits using GenAI tools or proofreading services for proofreading. Students are expected to complete the practical work themselves and explain their own technical choices and results.
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7COM1076
4,000 words
7COM1076 Network Design, Modelling and Evaluation Coursework
This 7COM1076 coursework requires students to design, model, emulate, test and evaluate networking environments for a new department building at the University of Hertfordshire. The assessment combines practical network modelling with theoretical analysis and evaluation, covering wireless and mobile networking, cloud networking, Software Defined Networking (SDN), network applications and contemporary networking technologies. The assignment is designed to develop practical knowledge through Python-based network emulation and critical understanding through academic research. Task 1 focuses on wireless and mobile networking. Students must use Python to emulate a building containing three WiFi access points and two user stations, identified as UE1 and UE2. The access points are connected using a physical linear topology, while the stations use Class C private IP addresses. The task requires configuration of SSIDs, passwords, channels, ranges and coordinates, with WPA2 encryption and standalone fail mode. The stations must also be configured with mobility, following specified movement sequences and speed ranges. Students must discuss the design and implementation, complete the configuration and mobility tables, provide the Python script used with the Mininet API, and include screenshots demonstrating mobility, access-point association and successful ping connectivity. Task 2 examines cloud services using Mininet and Render.com. Students must emulate a network containing three switches and two hosts and deploy a simple static website using Render.com. Host H1 must access the deployed webpage through the xterm environment. Deliverables include a discussion of the design and implementation, the Python script, evidence of configuring Render.com with the GitHub repository, commands used to connect H1 to the internet and access the webpage, screenshots of the webpage through xterm, and the HTML code of the website. Task 3 focuses on Software Defined Networking and connectivity. Students must emulate an environment containing ten hosts and three servers using a linear topology, with an ONOS controller enabled for control-plane programmability. The task requires configuration of assigned IP and MAC addresses, demonstration of connectivity between hosts and servers, and a UDP transmission using a duration of 600 seconds and bandwidth of 100 Mbps. Students must provide a design discussion, Python emulation script, ONOS GUI screenshot, full ping connectivity evidence and the required UDP transmission results. Task 4 is the analysis and evaluation component. Students must critically discuss three contemporary networking issues using academic sources. These include the challenges faced by a small-to-medium organisation providing services to the UK National Health Service when migrating from on-premises infrastructure to cloud computing; the opportunities and challenges of incorporating Software Defined Networking into future optical networks; and whether WiFi and 5G should coexist to provide ubiquitous services to users. The final report must use 12-point font, normal margins and Harvard referencing in accordance with University of Hertfordshire guidelines. The required report length is 4,000 words with a permitted variation of plus or minus 10%, excluding the title page, contents page, references and appendix, with a maximum page limit of 25 pages. The recommended structure includes an introduction, discussions and results for Tasks 1–3, the three Task 4 analysis sections, conclusion, references and an appendix containing the Task 1, Task 2 and Task 3 code. The assessment covers wireless and mobile networking, cloud and SDN networking, network applications, analysis and evaluation, MCQ tests and overall report quality. The marking allocation includes WiFi networking, mobility and ICMP, cloud configuration, SDN networking, cloud web page, UDP, three analysis sections, two MCQ tests and quality of the report.
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Programming for Data Science
2,500 words
Individual Coursework – Programming for Data Science
This Individual Coursework assignment for the Programming for Data Science module requires students to demonstrate practical programming skills and the ability to critically select and apply Python data science tools and libraries. The assessment is worth 20 credits and has a word count of 2,500 words plus 10%, excluding the reference list and output. Students are required to submit one clearly organised report covering two tasks and their individual subtasks. Task 1 focuses on designing, building, testing, explaining, adapting and critiquing a Python program. Students are required to implement a dice-based football match simulation for two players using at least two Python functions. The implementation should follow the logic of the physical dice game, demonstrate good Python coding style, and use functions designed with high cohesion and low coupling. The report must include the full Python code and carefully selected output from a sample match that demonstrates the progression of the game and changes in the score. Students must not implement a Python class or graphical visualisation. The second part of Task 1 requires students to explain how they designed their implementation, including assumptions, incremental development and testing. They must then modify their program to estimate at least two performance measures relevant to a football manager. The coursework asks students to investigate how these measures change when a restrictive shot clock is introduced and to model a realistic “Hail Mary” shot when the shot clock is close to expiring. Students must provide modified Python code, clearly identify the changes made, include selected output for checking the logic, and provide a robust conclusion based on comparison of the results. Task 2 focuses on critically assessing, selecting and applying Python data science libraries and algorithms. Students must describe an applied data science problem involving unstructured data such as image, audio, video or text. They must provide a specific example based on the context of a Coventry University fresher and explain how the problem could be solved manually. Students then select two Python libraries, justify their selection, compare their capabilities, apply both libraries to the chosen problem, and provide the relevant Python code and output. The final part of Task 2 requires a critical assessment of the selected Python libraries using the student's experience and additional sources. Factors may include coding difficulty, adaptability, level of control and quality of the resulting solution. Students must make a reasoned assessment of the suitability of the libraries for their chosen application and support their discussion with appropriate references. The assignment assesses three module learning outcomes: understanding essential programming concepts relevant to data science; designing, building, testing, explaining, adapting and critiquing small programs in a high-level programming language; and critically assessing, selecting and applying data science tools, libraries or algorithms for different applications and tasks. The submission must be provided as a single Microsoft Word or PDF report, organised by subtask, with each task starting on a new page. Python code, relevant output and plots must be included directly in the report. The brief requires APA referencing and states that sources should be cited in-text with a reference list for each task where relevant.
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Digital Security Risk and Audit
2,000 words
Digital Security Risk and Audit – Information Security Audit of ABC Air
This individual coursework for the Digital Security Risk and Audit module requires students to prepare a 2,000-word information security audit report based on a case study involving ABC Air, a small aircraft service company responsible for aircraft maintenance for civil operators. The company records information including aircraft flying hours, servicing time, engineers' man-hours and related maintenance activities. An external contractor has also provided a report outlining a possible solution for ABC Air. Students are required to assess the information security risks associated with the scenario, complete an information security audit and produce a professional report, clearly identifying any assumptions made during the analysis. The assessment requires students to examine risk assessment, identification and analysis as part of the audit process. A suitable auditing approach must be selected and justified, with students considering either a general risk-based approach or a specific control-based approach. The report should explain why the selected approach is appropriate for the ABC Air scenario and demonstrate how it can be applied to the organisation's information security environment. The coursework also requires consideration of potential cyber attacks and their use within an integrated fault event analysis. Students must identify relevant information security threats and examine how an attack could affect the organisation and its information assets. The report should further identify appropriate standards, best practices or guidelines that could be used to mitigate information security breaches. These should be critically evaluated, including discussion of their advantages and disadvantages rather than simply being listed. The assessment develops students' ability to apply information security governance and audit practices within legal, ethical and professional contexts. It also requires consideration of recognised industry frameworks such as COBIT and international standards including the ISO 27000 series. Students are expected to perform systematic risk assessment and analysis, critically evaluate information assurance reference models, and select appropriate information security audit strategies for complex real-world scenarios. The marking criteria place particular emphasis on the quality of the information security audit, identification and adoption of appropriate international standards and frameworks, and critical evaluation of the benefits and limitations of security audit frameworks. The assessment allocates 20% to identifying and applying an appropriate audit approach, 50% to completing the conceptual information security audit and assurance, and 30% to interpreting and critically evaluating information assurance reference models. The report is an individual assessment and must be submitted as a PDF through Aula/Turnitin. The brief states that APA referencing should be used for the work and that all sources and any AI tools used must be acknowledged.
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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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Software Development Project
Software Development Project – Group Presentation, Demo Video and Peer Assessment
This group-based assessment for the Software Development Project module at Coventry University requires a team of five students to design, develop and demonstrate a full-stack software product of their choice. The project must use technologies or platforms agreed by the team and requires students to contribute to different aspects of the software development process, including design, implementation and testing. The assessment is worth 20 credits and is submitted as a 15-minute MP4 presentation and demonstration video, with a permitted variation of plus or minus 10%. The main component is a group presentation and demo video. Students must evaluate the key Agile techniques applied during development and project management, supported by evidence of using appropriate software tools. The presentation should demonstrate how Agile practices were applied to the group project and should address techniques such as user stories with story-point estimation, story mapping, sprint planning, task boards, work breakdown and burndown charts. The assessment also requires evaluation of project risks and the social, professional, legal and ethical issues associated with the software project. A second major component is the live demonstration of the software features developed by the group. Students must demonstrate the most significant or innovative features implemented using their selected technologies and platforms. The demonstration should show how the software meets business requirements and provide a clear rationale for the development decisions. The marking criteria assess the quality, complexity and creativity of the software, including front-end, computation and back-end development, live data inputs and, where applicable, Create, Read, Update and Delete (CRUD) operations. The assessment also includes an individual peer-assessment component. Each student must rate every other team member's contribution using a whole-number score from 0 to 10, with 10 representing the highest contribution. The peer assessment accounts for 10% of the overall assessment, while the group presentation accounts for 60% and the software features demonstration accounts for 30%. The module learning outcomes focus on evaluating and applying appropriate software development approaches such as Agile, applying current technologies and platforms to meet business requirements, evaluating software solutions against quality metrics, and evaluating commercial risks alongside professional, social, legal and ethical considerations. The brief also places the assessment in the Amber AI category, meaning AI may be used to assist, but any AI tools used during the research process must be acknowledged and relevant AI-generated information must be cited and referenced using Coventry University APA style.
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International Marketing and Brand Management
6,000 words
International Marketing and Brand Management – International Brand Expansion Project
This individual project report for the International Marketing and Brand Management module focuses on analysing a selected brand and developing a strategy for its expansion into a new international market. Students must select a brand that is currently available in a limited number of countries or within a particular region and that has a physical market offering. The completed case study must be between 4,000 and 6,000 words, with 6,000 words specified as the maximum. The first part of the assignment focuses on Brand Analysis. Students are required to introduce the selected brand and examine its brand portfolio and geographical availability. The analysis must also consider the brand’s elements and evaluate its brand equity using Keller’s Customer-Based Brand Equity (CBBE) model. Students must additionally identify and analyse an appropriate brand mantra. These elements are intended to provide a comprehensive understanding of the brand’s existing market position and identity. The second part examines potential international markets for the selected brand. Students must conduct SLEPT analyses of three culturally distinct markets that the brand could potentially enter. Each market must also be assessed through competitor analysis. This section requires students to consider the wider market environment and competitive conditions when evaluating potential international expansion opportunities. The third part develops the Strategy for Market Entry. Students must select and justify one new international market for the brand to enter and recommend an appropriate market entry strategy. The report must also provide recommendations regarding standardisation or adaptation using a marketing mix framework. In addition, students must identify the intended Points of Parity (POP) and Points of Differentiation (POD) that would support the brand’s positioning in the selected international market. The assignment requires students to integrate relevant academic models and theories into the brand case rather than simply describing them. Sources used to support models, theories, claims and arguments must be appropriately referenced using Harvard referencing, both through in-text citations and in the final reference list. Students are also encouraged to use appropriately referenced images and figures to illustrate their case study. Assessment weighting is divided equally across the three substantive sections, with Brand Analysis accounting for 30%, International Market Analyses for 30%, and Strategy for Market Entry for 30%. The remaining 10% assesses presentation, including the range and quality of sources, Harvard referencing, clarity of writing and presentation, and quality of discussion.
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Entrepreneurship / Business Management / Enterprise
5,000 words
Enterprise Business Start-up: Innovative Business Plan, Fundraising Strategy and Entrepreneurial Reflection
This Enterprise Business Start-up assessment requires students to generate an innovative and sustainable business idea and develop it into a comprehensive start-up plan capable of guiding venture creation, attracting customers and supporting external fundraising. The module is assessed through two connected components: a 10-minute business pitch presented to an audience representing angel investors and venture capitalists, followed by a detailed individual business-plan portfolio. Assessment Brief Feb-May 2026 L… The principal written component is a 4,000-word detailed business plan. It begins with an executive summary before examining the proposed business idea, identified market gap, market research and competitor landscape. Students must then define appropriate customer profiles and segmentation, explain the development of the proposed product or service, and construct suitable marketing and communications strategies for reaching the intended market. Assessment Brief Feb-May 2026 L… A substantial part of the business plan focuses on the venture's financial and commercial viability. Students are expected to develop costing, pricing, sales and revenue projections alongside an appropriate business model. They must also identify the founding team, explain relevant roles and demonstrate the core competencies required to launch and develop the business successfully. Financials and business models receive particularly strong emphasis within the marking criteria. Assessment Brief Feb-May 2026 L… The written portfolio also includes a 1,000-word reflective component. Half of this section focuses on the student's fundraising strategy, including suitable funding sources and how potential investors or funders would be targeted, approached and secured. The other half critically reflects on the student's entrepreneurial tendency, personal experience and future skills development. Students are specifically required to complete the General Measure of Enterprising Tendency (GET) self-assessment and incorporate the results into their reflection. Assessment Brief Feb-May 2026 L… Assessment Brief Feb-May 2026 L… Overall, the assessment integrates entrepreneurial opportunity recognition, market analysis, customer segmentation, product development, marketing, start-up finance, business modelling, team capability, fundraising and reflective entrepreneurial development. The accompanying pitch additionally assesses visual quality, creativity, clarity of message, presentation skills, preparedness and professionalism. Assessment Brief Feb-May 2026 L… Assessment Brief Feb-May 2026 L… Important: the document does not explicitly specify a referencing style or academic level, so I would keep those fields as Not specified rather than assume Harvard or Masters.
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Principles of Management
1,000 words
Management Practices and Value Creation at Tesco
This 1,000-word formative assessment for the MSc Management programme at BPP University examines the management functions, theories and practices of Tesco. The assignment requires a business consultant perspective, with the report addressed to Tesco’s senior management team. It focuses on two learning outcomes: applying key management theories, concepts and practices to contemporary organisations, and evaluating the dynamics of the global business environment and the role of effective management as a value-adding activity at global, national and local levels. The case study provides background on Tesco, including its position in the UK grocery market, employment scale, store network and historical development. Tesco was founded by Jack Cohen in 1919, opened its first store in 1929, introduced self-service stores in 1948 and expanded rapidly through acquisitions and diversification. Its activities developed beyond grocery retailing into areas including non-food products, fuel retailing, financial services through Tesco Bank, and mobile and home entertainment. Task 1 focuses on key management theories, concepts and practices. The assignment requires discussion of how Tesco’s management practices contribute to organisational success and create value. Students must consider the four philosophies of the Competing Values Framework and identify one additional relevant management theory that supports Tesco’s management approach. Suggested theories include Scientific Management Theory, Bureaucratic Theory, Administrative Theory, Contingency Theory and Total Quality Management, although the brief states that the list is not exhaustive. Task 2 examines management as a value-adding universal activity. Students are required to evaluate the global business environment and discuss how Tesco’s management has adapted its strategies and methods at global, national and local levels. The brief suggests using the LoNG-PEST framework or Porter’s Five Forces, identifying one global factor that can create an opportunity and one that can pose a threat to Tesco, and explaining where management can add value to the organisation. The report structure includes a BPP University administration cover sheet, table of contents, optional list of abbreviations, an optional 100-word introduction, Task 1 of approximately 500 words, Task 2 of approximately 500 words, references and optional appendices. The main body is limited to 1,000 words, excluding the cover page, contents, abbreviations, references and appendices. Harvard referencing is required throughout the report.
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Effective Marketing Management
470 words
Bright Network Academy – Learn New Skills
This assignment, titled “Bright Network Academy – Learn New Skills,” is part of the Effective Marketing Management module and focuses on developing contemporary skills that support marketing effectiveness and future career development. The assignment introduces Bright Network Academy as a free e-learning platform providing expert advice and learning opportunities designed to help students prepare for the world of work and strengthen their graduate applications. Students are required to select and complete a minimum of two Bright Network Academy courses, provided that each selected course is at least 35–40 minutes long. Alternatively, students may choose shorter courses of approximately 15–20 or 20–30 minutes, in which case four courses must be completed. After completing the selected courses, students are required to download and submit the corresponding certificates as evidence of completion. The main written component is a one-page A4 statement of approximately 500 words. In this statement, students must explain their choice of the selected short courses and justify their relevance to the Effective Marketing Management module as well as their broader relevance to future career development in marketing. The assignment therefore requires students to reflect on how the skills and knowledge gained from the selected courses can contribute to their professional development. The available Bright Network Academy courses cover areas such as transferable skills, leadership skills, commercial awareness, and entering the marketing profession. Students are expected to consider how their chosen courses relate to contemporary marketing skills and their future professional goals. The final submission consists of a single file beginning with the approximately 500-word statement on the first page, followed by the completed course certificates on subsequent pages. The grading criteria allocate 25% to the first certificate, 25% to the second certificate, and 50% to the quality of the written statement. The assignment therefore combines evidence of course completion with reflective writing about the relevance of the selected learning activities to marketing and career development.
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Sustainable Development / Resource Management
6,000 words
Resource-Related Challenges in a Selected Country (STREAM) — Term Paper: India's Energy Sector, Coal Dependence and the Transition to Solar and Storage
This first-semester term paper for the STREAM programme takes a single country and a single resource sector and examines the challenges that arise where the two meet. India was selected for its energy sector — a case where scale, growth rate and an entrenched coal base make the tension between development need and environmental limit unusually sharp. The paper is written as an academic scientific text of roughly twenty pages, structured across the four sections the brief specifies and supported throughout by peer-reviewed literature, institutional reports and official statistical sources. The opening section establishes the country context: geographical position and climatic variation, population size and distribution, the shape and growth trajectory of the economy, development status against recognised indicators, and why the energy sector in particular is decisive for the country's near-term development path. The second section analyses the current status of the resource itself. Domestic coal reserves and their geographical concentration are set against renewable potential, particularly solar irradiance across the western and southern states. Production and consumption figures are traced over recent years, the import dependency for crude oil, gas and coking coal is quantified, and the major pressures are identified — demand growth outpacing capacity addition, the financial condition of distribution utilities, grid integration limits for variable generation, and the storage gap that constrains how far solar can displace baseload. The third section covers infrastructure and value chains. It describes the generation fleet, transmission and distribution network, and the logistics moving coal from pithead to plant, then follows the value chains attached to the resource — mining, power generation, equipment manufacturing and the growing domestic solar module and cell industry. Key stakeholders are mapped across central and state government, regulators, public and private generators, distribution companies, industrial consumers and the mining workforce whose livelihoods a transition directly affects. The final section addresses environmental problems and their connections to resource use. Ambient air quality and its public health burden, the water demand of thermal generation in already water-stressed basins, land degradation and displacement around mining regions, ash management and greenhouse gas emissions are each examined as consequences of the existing energy system rather than as separate issues. The section closes on future developments and risks: the plausible trajectories for renewable capacity and storage deployment, the stranded asset question for recently built thermal plants, and what a socially just transition would require of policy.
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Data Visualisation / Business Intelligence
2,500 words
Data Visualisation (BS666) — Business Analyst Client Report: Dashboard Development, Tool Evaluation and Accessibility in Power BI and Tableau
This Level 7 resit assessment takes the form of a single client-facing report written from the position of a qualified business analyst. Rather than assembling semester activities, it asks for one sustained piece of analytical writing that carries a business case from raw open data through to a defended set of visualisations and the decisions they support. The report opens by identifying a data source drawn from an approved open repository and setting out the business problem the client faces. Provenance, structure, granularity and known limitations of the dataset are examined honestly at this stage, since every downstream claim rests on them, and all data is referenced in full — including data appearing inside charts, as the brief specifically requires. The main body works through three connected strands of critical evaluation. The first traces the development sequence of the visualisations themselves: how the data was prepared and cleaned, why particular chart types were selected over the alternatives available, how layout and interactivity were arranged for the intended audience, and how the design changed across iterations once weaknesses became visible. At least three completed visualisations are then evaluated individually and critically — what each one reveals, where each falls short, and what a reader could reasonably conclude from it. The second strand compares Power BI and Tableau as working environments for this specific dataset rather than in the abstract. Data connectivity, transformation and calculation capability, visual flexibility, publishing and sharing, licensing and governance are all weighed against what the client actually needs, with the practical friction encountered during the build reported rather than smoothed over. The third strand addresses accessibility and cognitive processing. It examines how each platform handles colour contrast and colour-vision deficiency, text alternatives, keyboard navigation and screen reader support, and then moves into the perceptual side — pre-attentive attributes, data-ink economy, chart junk, working memory limits and how visual encoding choices either reduce or inflate the effort a reader must spend to extract meaning. The argument connects this directly to decision quality in organisations with diverse analytical literacy. Findings are reported at length and translated into concrete business implications, with a conclusion that states what the client should do and on what evidence. The submission follows the prescribed structure throughout: title page, executive summary, contents, introduction, business problem, main evaluative section, findings, conclusion, Harvard reference list and appendices, presented as a single file for Turnitin.
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Data Visualisation / Business Intelligence
2,500 words
Data Visualisation (BS664) — Portfolio: Tableau and Power BI Dashboard Development for Evidence-Based Decision-Making
This Level 7 portfolio accumulates work across a full semester of data visualisation study and is submitted as one continuous document combining reflective writing, tool evaluation and an applied dashboard project. The emphasis throughout falls on interpretative rigour — showing not just that a visualisation was built, but why each design decision serves the decision-maker who has to act on it. The first activity documents sustained critical participation across the module's weekly discussion topics. Five or more substantive responses are evidenced with dated screenshots, each paired with the corresponding workshop date, and each grounded in both academic literature and practitioner sources rather than opinion alone. Live URLs to the Power BI and Tableau dashboards built during the module are included, and the activity closes with a consolidated review of how understanding developed across the units. The second activity covers formal training completion in both platforms, with certificates and badges reproduced, and then turns to accessibility. It examines the visualisations produced during that training against accessibility principles — colour contrast and colour-blind safe palettes, text alternatives, chart type legibility, cognitive load — and argues why accessibility is not a compliance afterthought but a determinant of whether a dashboard actually informs decisions inside a global organisation with a diverse user base. The third activity is a structured comparison of Tableau and Power BI, conducted through the evaluative frameworks introduced in workshops rather than a feature checklist. Benefits and limitations are weighed across data connectivity, calculation capability, visual flexibility, licensing and cost, collaboration and governance, and organisational fit. The section ends with a reasoned recommendation of one platform for the applied work that follows, with the trade-offs of that choice acknowledged openly. The fourth and largest activity is the applied project. A dataset is selected from an approved open source and its provenance, quality and limitations discussed. The target business, its stakeholder groups and the varying analytical literacy of the intended audience are analysed, because these determine what the dashboard must show and how plainly. The report then walks through the full development sequence — data preparation and cleaning, chart selection and justification, layout and interactivity, iteration in response to identified weaknesses — with screenshots evidencing each stage. Findings are reported and translated into specific, evidence-based decisions the business could take on the strength of them. The submission follows the prescribed structure with title page, contents, introduction, business problem, main analysis, findings, Harvard reference list and appendices, presented as a single file.
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Data Management / Business Analytics
2,500 words
Data Design Management (BS514) — Data Strategy Consultancy: Relational Database Design, SQL Implementation and Pipeline Transformation
This Level 7 assessment places the writer in the role of a Data Strategy and Analytics Consultant appointed by an organisation operating in a realistic industry sector. The brief is entirely simulated, so the work sets out a defensible set of assumptions about the organisation's data environment before any design begins, and populates the resulting database with synthetic but realistic records. The deliverable is a slide deck carrying full explanatory notes, submitted as a single PDF, and weighted across three connected tasks. Task one establishes the business case. It describes how the chosen organisation currently collects, stores and uses data across customer interactions, sales transactions, operational processes and digital channels, and identifies where that fragmented picture costs the business in efficiency, resource use, retention and decision quality. A SWOT analysis benchmarks the organisation against a named real-world competitor in the same sector, drawing on publicly available market information rather than assertion. The section closes with a critical evaluation of modern relational database advancements — cloud-hosted SQL services, distributed architectures and data warehousing — assessed not in the abstract but against what each would actually change about this organisation's business model. Task two carries the heaviest weighting and is the technical core. Key business entities are identified from the scenario, a current-state data flow diagram traces how data moves from collection points through to storage and reporting with the existing ETL approach made explicit, and a future-state ER diagram is then built with full attributes, primary and foreign keys, relationships and cardinality. The design is normalised to third normal form with the decomposition reasoning shown. Implementation follows in SQL: tables created with appropriate integrity constraints, at least ten realistic sample records inserted per table, and five business questions answered through working queries — highest-performing product or campaign, average conversion by category, workload distribution across staff, accounts with overdue or pending items, and most effective service channel. Outputs accompany every script. A transformation demonstrating query optimisation is included with before-and-after samples so the improvement is evidenced rather than claimed. Task three steps back to the technology decision. Two widely used data processing platforms are compared in tabular form across integration, cleaning, transformation and automation capability, judged specifically against this organisation's constraints, with a reasoned justification for the tool finally selected. The transformed dataset is then used to answer two management-level questions — where investment should be prioritised and how retention might be improved from observed behavioural patterns — each interpreted briefly and tied back to a concrete recommendation. Slide structure follows the prescribed layout, SQL scripts sit in the notes section, and the complete script file is reproduced in the appendix.
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Data Mining / Data Science
Customer Service Escalation Risk Analytics Using Data Mining and Machine Learning
This advanced Data Mining assessment applies the Knowledge Discovery in Databases (KDD) process to a real-world customer-service analytics scenario. Acting as a Data Scientist, students analyse a historical Customer Service Escalation Risk dataset to help an organisation reduce serious and legal customer escalations by identifying early signs of dissatisfaction, service bottlenecks and operational risk. The findings are intended to support business decisions relating to staffing, employee training, customer-journey improvement and escalation prevention. The dataset contains information covering customer demographics, account characteristics, communication channels, issue categories, operational measures such as wait times, transfers and SLA breaches, behavioural indicators including sentiment and response delays, and commercial variables such as monthly fees and contract value. The target variable contains four escalation outcomes: No escalation, Minor escalation, Serious escalation and Legal escalation. The first stage requires data exploration, visualisation and summary, including examination of variable distributions, dataset structure, descriptive characteristics and potential data-quality issues. Students then perform appropriate data cleaning, transformation, feature engineering and preprocessing. Particular attention must be given to variables that could introduce prediction leakage because of their meaning, timing or reliability. The supervised-learning component requires development and tuning of predictive models using suitable techniques such as k-nearest neighbours, Decision Trees, Support Vector Machines, ensembles or neural networks. Models must be evaluated using appropriate multiclass metrics and compared systematically, with interpretation of influential features and model behaviour. The assessment also requires unsupervised learning. After removing the escalation target, students apply and compare clustering approaches such as K-Means and hierarchical clustering. Appropriate preprocessing, encoding, normalisation or dimensionality reduction may be used, with visualisations such as PCA, t-SNE or scatterplots used to explore cluster structure and its relationship with escalation behaviour. Overall, the project assesses the student's ability to independently design a coherent KDD workflow, justify analytical decisions, compare alternative modelling approaches and communicate actionable findings to both technical and executive audiences. Overview word count: approximately 350 words. AI-use note: the brief permits AI tools only to assist with small, specific code snippets. Any AI-generated code must be clearly acknowledged and cited, while the submitted coding, analysis, interpretation and decision-making must remain the student's own work.
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Strategy and Innovation
Strategic Analysis and Innovation Strategy for a UK Organisation
This postgraduate Strategy and Innovation assessment requires students to conduct a detailed strategic analysis of an allocated organisation and present the findings through a series of professionally designed information dashboards. The purpose of the analysis is to establish the organisation's current strategic position and use the resulting evidence to recommend an appropriate and justifiable innovation strategy. The work is expected to demonstrate master's-level evaluation, interpretation and synthesis rather than descriptive or checklist-based analysis. The first major analytical area concerns the organisation's external environment. Students evaluate the broader business environment, analyse the firm's industry and undertake competitor analysis, including consideration of the organisation's strategic group. Analysis should concentrate on the most consequential environmental factors and integrate general and industry-level evidence rather than simply listing factors. Competitor evaluation can examine issues such as products and services, market commonality, positioning, distinctive value propositions, first-mover advantage and breakthrough innovation. The internal analysis examines the firm's resources, capabilities and sources of competitive advantage. Appropriate analytical techniques should be used to assess competitive resources and key capabilities, with the internal value chain helping to identify factors capable of supporting competitive advantage. Students must also evaluate the organisation's business-level strategy and consider whether its resources and capabilities support its competitive positioning. Corporate-level analysis considers the firm's degree of diversification, internationalisation strategy, growth through acquisitions, mergers or organic development, and any cooperative strategies. These elements should be evaluated against the organisation's capabilities, resources and competitive context. The analysis concludes by interpreting the evidence to establish the firm's overall strategic position and recommending an innovation strategy or approach to innovation. Presentation quality is also important: the report should use an effective combination of text, charts, diagrams and other graphical forms, with professional formatting and accurate referencing. Overview word count: approximately 330 words.
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Cloud Computing / Big Data Technologies / Cyber Security
2,500 words
Cloud and Big Data Security Application: Design, Implementation and Evaluation
This assessment for the Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud-based or distributed data application. The project focuses on practical solutions involving the complex transformation, processing, storage and security of big data within cloud environments. Students are expected to demonstrate how distributed data can be organised in the cloud, how data pipelines can be used to access or process distributed databases, and how appropriate security controls can be incorporated into the resulting architecture. Students have considerable freedom when selecting their application. Suggested project directions include developing a data-science solution using SQL or MongoDB with cloud storage and an appropriate security policy; implementing privacy-preserving distributed processing using techniques such as Differential Privacy; creating multi-party authentication and group-based access-control mechanisms; or designing Multi-Level Security, Attribute-Based Encryption or Role-Based Access Control solutions. Projects may also examine distributed or cloud applications using security protocols such as SSH, SSL or IPsec. Creativity and originality are explicitly encouraged. The written component is a Design and Implementation Document of no more than approximately 2,500 words. It should present the project aims and objectives, application concept, cloud and security technologies, functional and security requirements, architecture and design decisions, protocols, access-control mechanisms, implementation process, achievements, problems encountered and overall evaluation. Relevant diagrams, such as interaction or sequence diagrams, may be used to explain system behaviour and architecture. The assessment also requires submission of the functioning Cloud and Big Data Security application and a 7-minute highlight demonstration video. The video should demonstrate the application's major features, implementation details, security functionality and, where appropriate, attack scenarios. Assessment places strong emphasis on the quality of the design and implementation documentation, originality, use of advanced features, and the overall effort and technical quality of the completed application. Students are therefore expected to demonstrate independent development rather than simply reproduce an existing tutorial.
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Computer Science / Algorithms and Optimisation
Genetic Algorithms for the Balanced Spanning Tree Problem
This technical research work investigates the Balanced Spanning Tree Problem, an optimisation problem that seeks to construct a spanning tree capable of balancing two competing network objectives: the low overall cost associated with a Minimum Spanning Tree and the short source-to-destination distances provided by a Shortest Path Tree. For an undirected, weighted and connected graph with a designated root vertex, a balanced spanning tree is defined using two parameters, α and β. The first limits the distance between the root and each vertex relative to the corresponding shortest path in the original graph, while the second limits the total tree weight relative to the Minimum Spanning Tree. Finding an optimal balanced spanning tree is computationally challenging because determining whether a graph contains an (α, β)-balanced spanning tree is an NP-complete problem. The research therefore proposes genetic algorithms as heuristic optimisation techniques for two variants of the problem: minimising β while α is fixed, and minimising α while β is fixed. The proposed genetic algorithm represents individual spanning trees as chromosomes composed of graph edges. An initial population of valid spanning trees is generated before evolutionary operations are repeatedly applied. The approach incorporates chromosome selection, crossover, mutation, fitness evaluation and stopping criteria. Four selection strategies are examined: Random Selection, Roulette Wheel Selection, Stochastic Universal Sampling and Tournament Selection. The fitness function is based on the relationship between the Minimum Spanning Tree weight and the total weight of the candidate chromosome. Experimental evaluation is performed using randomly generated weighted graphs containing 6, 10, 15 and 20 vertices. The experiments investigate different values of the balancing parameters, selection mechanisms and population sizes. The implementation uses a population size of 30, a maximum of 300 generations, crossover probability of 0.9 and mutation probability of 0.01 in the principal experiments. The reported results show that the genetic approach can generate high-quality balanced spanning trees and, for the tested instances, produced solutions matching the corresponding optimal balanced spanning trees. The study also examines how balancing parameters and population size influence execution time and convergence, demonstrating the practical use of evolutionary computation for complex graph-optimisation problems.
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Management / Business Management
5,000 words
Principles of Management: AstraZeneca Management Analysis and Skills Development Report
This Masters-level Principles of Management assignment examines the management practices, organisational environment and managerial functions of AstraZeneca plc. The assessment is structured as a 5,000-word management report in which the student acts as a business consultant and critically evaluates how management theories, concepts and practices contribute to organisational performance and value creation. The work requires the application of relevant academic literature alongside case-study evidence and independent research on AstraZeneca. The first part of the assessment considers key management theories and practices. Students are expected to examine how management contributes to AstraZeneca's success, apply an appropriate dimension of the Competing Values Framework, and select an additional relevant management theory. Suggested theoretical perspectives include Scientific Management, Bureaucratic Theory, Administrative Theory, Contingency Theory and Total Quality Management, although students are encouraged to select theories most appropriate to the organisation rather than applying every available model. The assignment also evaluates AstraZeneca within the global business environment. Frameworks such as LoNG-PEST or Porter's Five Forces may be used to analyse global, national and local influences, including significant opportunities and threats. Students then critically analyse an internal organisational challenge and consider how the core management functions of planning, organising, leading and controlling can be applied to address it. Supporting analytical approaches may include Value Chain analysis, VRIO and stakeholder analysis. A further component focuses on personal and professional development. Students complete a Personal SWOT analysis and construct a Skills Development Plan linked to future career aims. This is followed by a 500-word reflective statement examining personal management competencies, including self-management, problem-solving and decision-making. An appropriate reflective framework, such as Borton, Kolb or GROW, is applied to structure the reflection. Overall, the assessment integrates management theory, organisational analysis, global business considerations, managerial decision-making and reflective professional development. It is designed to demonstrate Level 7 critical analysis and the practical application of management concepts to a contemporary multinational organisation. The wider module covers management theories, stakeholder management, global management, planning and decision-making, human resource management, leadership, operations and finance for managers
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People Practice / Human Resource Management
2,000 words
Core Behaviours for People Professionals: Ethics, Professional Values and CPD
This CIPD Level 3 assessment focuses on the core behaviours expected of people professionals, with particular emphasis on ethical practice, professional values, inclusive behaviour and continuing professional development. The unit considers behaviours that people professionals should apply consistently, including when working in unfamiliar or challenging situations, to support ethical, inclusive and professionally responsible workplaces. The assessment is structured around a job-application scenario for an HR/L&D Assistant position at Nuvascare Ltd, a home-based care organisation employing more than 200 care professionals. Nuvascare emphasises honesty, kindness, openness, teamwork, learning and ethical professional behaviour, and candidates are therefore required to demonstrate how their own experience aligns with these expectations. Learners respond to five areas of assessment. They must explain ethical principles and professional values and provide an example demonstrating how a personal or professional value has influenced behaviour. They must also provide examples of complying with relevant regulation and law in a professional or educational context. The assessment additionally examines behaviours associated with effective teamwork, including contributing views, clarifying problems and working collaboratively with others. Learners then explain how they keep their professional knowledge current and identify methods used to follow developments within the people profession. The final component requires evidence of active continuing professional development (CPD). Learners provide a CPD record containing at least two recent development activities and reflect on how these activities affected their knowledge, capability or professional practice. Written responses to Questions 1–4 should total approximately 2,000 words ±10%. The CPD record is submitted as additional evidence and is excluded from the word count.
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International Entrepreneurship / Business Management
International Entrepreneurship: Start-up Business Idea and International Market Expansion Pitch
This group presentation assessment requires students to develop and pitch an internationally oriented entrepreneurial business idea. Students may create their own start-up or select an existing small or medium-sized enterprise or family firm. Large multinational organisations are not permitted. The selected business must have an international dimension, either from its inception or through credible plans to undertake international activities such as overseas sourcing, manufacturing, selling or market expansion within approximately three to five years. The presentation requires students to clearly explain the proposed business idea, its products or services and the customer value proposition. Students must demonstrate how a viable market opportunity or gap has been identified and explain how the proposed venture intends to capture that opportunity. Consideration may also be given to social entrepreneurship and the potential contribution of the business to local communities. A significant part of the assessment examines how an entrepreneurial venture can overcome the practical challenges associated with start-up development and internationalisation. Students should evaluate resource scarcity and the role of entrepreneurial networks or open innovation, propose appropriate funding mechanisms such as angel investment, venture capital or crowdfunding, and explain how the venture's intellectual property could be protected. Students must also present a credible international growth strategy. This includes identifying suitable foreign markets, analysing competitors, selecting appropriate market-entry modes, defining target customer segments and developing suitable marketing and promotional strategies. All arguments should be supported with reliable evidence, with sources cited directly within the presentation and a reference list included. The assessment places emphasis on knowledge and application of entrepreneurship theories, clarity and focus of the business idea, quality and reliability of supporting materials, professional presentation delivery and the group's ability to respond confidently during questioning. All group members are expected to contribute to the work and presentation. The accompanying Group Engagement Form specifically records whether members contributed equally and allows agreed contribution percentages to be documented. Reference style: enter Not specified rather than automatically selecting Harvard. The brief requires reliable sources, slide citations and a reference list, but the uploaded assessment documents do not prescribe a named referencing system. If the actual library file you are uploading is the FourGaz presentation sample, rather than a new 2025/26 submission, use the title “FourGaz: International Entrepreneurship Start-up and Market Expansion Strategy” and do not label it 2025/26, because that sample is dated 25 November 2019.
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Quality Engineering / Manufacturing Engineering / Statistical Quality Contro
2,500 words
Advanced Quality Engineering: Gauge R&R, Process Capability and Statistical Process Control Analysis
This Advanced Quality Engineering coursework requires students to apply quantitative quality-management techniques to a realistic manufacturing scenario involving the production of precision components. The assessment combines measurement system analysis, process capability evaluation and Statistical Process Control (SPC) to determine whether measurement and manufacturing processes are sufficiently reliable and capable of meeting specified engineering requirements. The first stage focuses on Gauge Repeatability and Reproducibility (Gauge R&R). Students are provided with measurements from ten components, with each component measured twice by two different operators. Using the supplied data and the module's Gauge R&R calculator, students must assess whether the measurement system is capable of evaluating a critical component dimension specified between 115 mm and 116 mm. This section tests the ability to evaluate measurement variation and determine the suitability of a measuring system for quality-control purposes. The second task requires comparison of two CNC machining processes. Historical batch measurements from a Honzaki CNC vertical milling machine are compared with measurements from a replacement Kawda CNC vertical milling machine. Students must calculate the Cp and Cpk process capability indices for both processes, interpret the results and determine which process is better suited to producing components within specification. Recommendations for process improvement and responsibility for corrective action must also be considered. The final task applies Statistical Process Control. Students must construct appropriate X-bar and R control charts, calculate the relevant control limits, plot process performance and investigate potential out-of-control conditions to identify differences between components produced by the two machines. Supporting module material emphasises the distinction between common and special causes of variation, interpretation of abnormal control-chart patterns, process capability and the use of SPC as a feedback mechanism for preventing defective output and improving manufacturing quality. Reference style: keep this as Not specified in the portal. The handbook requires external material to be properly acknowledged, but the uploaded guideline does not prescribe Harvard, APA, IEEE or another named referencing system.
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Global Supply Chain Management / Operations Management
2,494 words
Global Supply Chain and Operations Performance: Company Case Analysis
This Level 7 individual assignment requires students to produce a 2,500-word company case analysis examining an important operations performance issue within a real organisation. Students may select an organisation of any size, type, industry or country and must concentrate their analysis on one operations performance objective: cost, dependability, flexibility, speed or quality. The report requires students to identify the current challenges faced by the chosen organisation in relation to the selected performance objective and critically examine those challenges using relevant operations and supply chain management theories, concepts and analytical tools. Rather than providing only a descriptive account of organisational activities, students are expected to apply appropriate theoretical frameworks to the case and evaluate how these concepts could contribute to improved organisational and supply chain performance. A significant component of the assignment involves critically discussing appropriate best practices for performance improvement. These may include digital technologies, sustainability practices or other relevant operational and supply chain approaches. Students should connect these practices directly to findings from their chosen company case and assess their practical relevance, opportunities and limitations. Academic literature and appropriate real-world evidence should be used throughout to substantiate the analysis and recommendations. The assignment evaluates students' ability to analyse supply chain and operations problems, apply academic theory to organisational practice, and develop evidence-based conclusions and practical recommendations. The marking rubric places substantial emphasis on analysis and discussion (30%), application of theory (30%), and conclusions and recommendations (30%), with the remaining 10% allocated to presentation, logical structure, English expression and correct referencing. The report must use Harvard referencing, include a contents page, and follow the specified academic formatting requirements, including Arial size 12, 1.5 line spacing and A4 pages with 2.54 cm margins.
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Computer Science / Research Methods
500 words
Interim Report: Systematic Literature Review Research Protocol
This postgraduate computer science assignment requires students to prepare an Interim Report establishing the research protocol for a systematic literature review. The assessment focuses on whether the proposed research question is suitable for computer science research, clearly formulated, appropriately motivated by existing literature, and capable of being investigated through a systematic review. The main body of the report must not exceed 500 words, while supporting evidence may be provided separately where appropriate. The report is structured around three main chapters. Chapter 1 introduces the selected research area and summarises the purpose and structure of the report. Chapter 2 provides relevant background and a brief history of the chosen research domain, identifies the problem being addressed, and supports the discussion with at least two relevant academic citations. Chapter 3 presents the formal Literature Review Protocol, including the research question, its context, and the associated PICO elements: Population, Intervention, Comparison and Outcome. Students must also develop and test a Boolean search string using IEEE Xplore and report the number of papers returned by that search. The search strategy is accompanied by explicit inclusion and exclusion criteria governing which studies will be considered for the review. The supplied protocol template requires students to document these elements through two structured tables: one covering the research question and PICO framework, and another recording the search string, paper count, and study-selection criteria. The assessment places significant emphasis on methodological consistency. The research question, PICO elements, search strategy, paper count and inclusion/exclusion criteria must align logically with one another. Students are also assessed on the justification and motivation of the research question, document structure, presentation quality, spelling, grammar and academic referencing. The report must include a title page, table of contents, bibliography and the required research-protocol tables. Harvard referencing is required for both in-text citations and the final reference list. Overall, the assignment develops the foundational skills required for conducting a rigorous systematic literature review, including research-question formulation, structured evidence searching, transparent study-selection procedures, academic justification and professional research reporting.
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Computer Science / Machine Learning
Machine Learning Portfolio Challenge: Support Vector Machines and Kernel Methods
This machine learning portfolio challenge requires students to demonstrate both theoretical understanding and practical application of machine learning methodologies introduced during the second block of the module. During Weeks 7–12, students select one of the machine learning methods covered in class and apply it to a dataset of their choice. The completed work contributes to an assessment portfolio and must demonstrate a clear, systematic and professionally documented experimental process. The accompanying learning material places particular emphasis on Support Vector Machines (SVMs) and kernel-based machine learning. SVMs are presented as maximum-margin classifiers that construct a decision hyperplane between classes, with support vectors playing the key role in defining the classification boundary. The material also introduces soft-margin optimisation, slack variables, the regularisation parameter C, primal and dual formulations, and the use of KKT multipliers. SVMs are additionally discussed in relation to multiclass classification through approaches including one-versus-all and Error Correcting Output Codes (ECOC). Kernel methods extend these principles by replacing explicit feature transformations with similarity functions. Students encounter concepts including Gram matrices, feature mappings, Mercer conditions and the kernel trick, alongside common kernel choices such as linear, radial basis function and polynomial kernels. The material demonstrates how kernel methods can represent nonlinear decision boundaries in the original input space while retaining a linear representation in an embedded feature space. The final submission must be produced as a single PDF lab notebook. It should contain clear and well-commented MATLAB, Python or equivalent code explaining each methodological step, experimental results presented through appropriate tables and/or plots, and narrative discussion explaining the selected approach, observations and conclusions. The notebook should integrate code, outputs and written explanation into a coherent and professional submission. Students may also optionally present their solution in class, where the quality of explanation and discussion can contribute additional marks.
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Entrepreneurship and Business Start-up
5,000 words
Enterprise Start-up Portfolio — Business Plan, Fundraising Strategy and Entrepreneurial Self-Assessment
An enterprise start-up portfolio assessment asks students to originate a business idea and build the full supporting case for it, then present that case both in writing and as a recorded investor pitch. It is a pass/fail module structure in which every component must be passed individually — a strong business plan cannot compensate for a weak pitch, or vice versa. The written portfolio divides into two unequal halves. The larger part is the business plan itself, built to a fixed section structure with prescribed word allocations: an executive summary; the business idea set against an identified market gap, with market research and competitor analysis; customer profiles and segmentation; product development; marketing and communications; financials and business models covering costing, pricing, sales and revenue; and the founding team with its core competencies. The word allocations are not decorative — financials carry the single largest weighting in both the word budget and the marking rubric, which tells students where analytical depth is expected and where concision is. The smaller part is reflective. It covers the fundraising strategy — which funding sources are realistic for this venture, and how each would be targeted, approached and secured — and the student's own entrepreneurial tendency, informed by a standardised self-assessment instrument taken online. The rubric here rewards critical self-reflection over description: reporting a test result scores poorly; interpreting what the result means for how this founder should build a team and where they need support scores well. The pitch component is assessed on visual and audio quality, content and message, comprehension, delivery, and evident preparation. Technical execution carries real marks, which students routinely underestimate. Two things separate strong submissions. The first is internal consistency: the revenue model must follow from the pricing, the pricing from the customer segment, the segment from the identified gap. Plans that read as seven separate essays under seven headings lose marks even when each section is individually competent. The second is specificity in the financials — costing assumptions stated and justified, rather than round numbers presented without derivation. Note that assessments of this type commonly require the student to retain all drafts and earlier versions of their work, and to sign a detailed declaration itemising exactly how any AI tools were used. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining what a market gap argument needs to be credible, showing how competitor analysis should be structured rather than listed, clarifying how costing and pricing assumptions should be built and presented, reviewing whether a fundraising strategy matches the venture's actual stage, explaining how reflective writing is assessed at postgraduate level, and checking a completed draft against the published rubric.
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