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Artificial Intelligence
2,000 words
End-to-End Applied AI Development — Comparative Machine Learning and Neural Network Modelling on a Public Dataset
This assessment runs a complete applied AI development cycle end to end: problem definition, dataset selection, preprocessing, model building, optimisation, evaluation and critical reflection. Students identify a real-world problem themselves, formulate a research question from it, and source a suitable dataset from a recognised public repository such as UCI, Kaggle, Data.gov or OpenML. Dataset choice carries more weight than students expect. It must be genuinely suitable for supervised learning, complex enough to make preprocessing and feature engineering meaningful, and — critically — structured so that a traditional machine learning approach and a deep learning approach can be sensibly compared on it. A dataset too small or too clean makes the neural network component pointless; one too large or too noisy makes the whole pipeline unfinishable within the page limit. The source must be referenced and the choice explicitly justified against the research problem. The modelling requirement is fixed: at least two supervised machine learning models, plus one artificial neural network built in a mainstream deep learning framework, all trained and tested. The comparison between them is the analytical core of the work. Reporting that the neural network scored higher is not an answer; explaining why, in terms of the data's structure and each model's inductive assumptions, is. Marks are distributed across problem framing, the traditional models, the deep learning model, evaluation and critical analysis including responsible AI considerations, and academic communication. That responsible AI component is easy to overlook and is not decorative — it asks what the model's limitations mean for anyone who might rely on it. Presentation requirements are specific. The report is page-limited rather than purely word-limited, and every plot must be described in the text while also being legible enough to communicate on its own — a common failure is dense default library output pasted in without axis labels or scale. The implementation is documented in a notebook combining markdown and code cells so the development process is visible, not just the final result, and submissions typically include the cleaned dataset alongside the code. The strongest submissions treat the notebook and the report as one argument. Weaker ones produce a working notebook and then write a report that describes it, rather than a report that uses it as evidence. Our support on assessments of this type is guidance-based. Typical areas of help include: advising on whether a candidate dataset can actually support the required model comparison, explaining how to justify preprocessing decisions, clarifying which evaluation metrics suit which problem type and why accuracy alone is often misleading, showing how to structure a critical limitations and responsible AI discussion, checking Harvard referencing, and reviewing a student's own draft against the published marking criteria.
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Data Science
1,000 words
Data Investigation Pipeline — Exploratory Analysis and Statistical Evaluation of a Chosen Dataset
This assessment simulates the opening stages of a real data investigation. Students choose their own research question and dataset, then build the full pipeline from raw data through preparation, exploration and statistical testing to visualisation — and, where the question supports it, simple modelling or forecasting. Either Python or R is acceptable; the statistical route through R typically expects an explicit hypothesis rather than a purely exploratory question. The work is structured around an established process methodology such as CRISP-DM, and the development journey is documented alongside the code rather than reported after the fact. The usual submission format is a single notebook combining markdown and code cells, so the written report and the analysis sit in one artefact, though a word-processed document containing the code is normally also accepted. The written element is short — around a thousand words — which makes selection the hardest part of the task. It must cover the scenario, the data collection, the exploratory analysis, the reasoning behind the choice of statistical tests, their results, and the visualisations. Students routinely spend that budget describing what they did and leave nothing for why. The mark distribution makes the priority explicit. Framing the problem and the data source carries the smallest share. Preparation and exploratory analysis, and evaluation of the results in context, carry the bulk in roughly equal measure. That final component is where most marks are lost: it asks for an honest assessment of accuracy, limitations and usefulness. A notebook that produces clean output and then claims more than the data supports scores below one that reports a modest result and explains precisely why it is modest. Established metrics should be used for the statistical tests, and published research cited where it informs the background or interprets the findings. Note that assessments of this type increasingly include a live demonstration in which the student explains their own project to verify authorship, so every line of the submitted work needs to be something the student can talk through unprompted. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining how to scope a research question so the analysis fits the word limit, clarifying which statistical test suits which data type and why, reviewing whether a chosen visualisation communicates what it claims, showing how to write an honest limitations section, checking Harvard referencing, and reviewing a student's own draft notebook against the published marking criteria.
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Engineering and Professional Practice
3,000 words
Internship Reflective Report and Poster Presentation — Professional Practice in an Industry Placement
An internship module assessment asks students to convert a period of workplace experience into evidenced academic reflection. It is not a report on what the host organisation does; it is an account of what changed in the student's own understanding of their discipline as a result of working outside the taught programme. The written component typically runs to around three thousand words and is built in weighted sections. An opening section covers how the placement was secured — the reasoning behind the choice, the search and application process, and what that process itself taught the student. The bulk of the marks then sit on the reflection proper: what the student actually did, how existing disciplinary knowledge held up when applied in an unfamiliar setting, and where gaps appeared. A final section addresses personal and professional development, supported by specific examples rather than general claims of having "grown in confidence." The second component is a short poster presentation summarising the placement and its learning, delivered live. Posters are assessed on professional presentation, clarity, correct referencing and balance — students commonly over-fill them with text that a viewer cannot read at presentation distance. Formatting requirements on this type of assessment are unusually prescriptive and carry marks: a cover page with name, student ID, tutor name and declared word count; a table of contents; page numbers; captions on every figure and table; specified font, size and line spacing; numbered headings; and a strict file-naming convention. Marks are lost here for no reason other than inattention. Note also that in-text citations and quotations usually count toward the word limit even when tables and references do not, and markers may simply stop reading once the limit is exceeded by more than the allowed margin. The defining challenge of reflective assessment at this level is criticality. Rubrics consistently distinguish description from evaluation: recounting tasks performed scores at the lower bands, while analysing why something was difficult, what it revealed about a gap in preparation, and what will be done differently scores at the top. Reflective frameworks give this structure, but they must organise genuine experience rather than substitute for it. This assessment is inherently personal — the content derives entirely from the student's own placement. Our support is therefore confined to guidance: explaining what distinguishes descriptive from critical reflection with worked examples, showing how a reflective framework structures a section, advising on poster layout and information density, checking formatting and referencing against the specification, and reviewing a student's own completed draft against the published rubric.
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Operations and Supply Chain Management
1,800 words
Strategic Sourcing and Supply Chain Resilience Under Global Uncertainty — A Firm-Level Critical Evaluation
This type of postgraduate coursework asks students to take a single real firm — a British company, or a multinational with substantial UK operations — and critically evaluate how its sourcing and supply chain strategy has adapted to a decade of sustained disruption: the pandemic, geopolitical conflict, tariff volatility, and departure from the European single market. The analytical scope is broad but the depth expectation is narrow. Students choose one or two themes rather than surveying all of them: insourcing versus outsourcing and location decisions; single versus multi-sourcing across supplier segments or regions; how supplier selection and monitoring criteria have shifted toward reliability and geographic proximity; alignment between sourcing decisions and wider corporate objectives; supplier and customer relationship management under cross-border friction; inventory tactics such as stockpiling and safety stock repositioning; digital adoption for visibility and compliance; sustainable sourcing under cost pressure; or risk management around currency, customs and compliance. The evidence base is deliberately mixed. Alongside academic literature, students are expected to draw on annual reports, shareholder briefings, company statements, practitioner journals and recent news coverage — with a minimum spread of credible sources across both categories, and some of the non-academic material drawn from the most recent year so the analysis reflects current firm behaviour rather than historical commentary. At least one theoretical framework from the module or the wider literature must be applied to the firm's decisions, and at least one recent documented practice must be examined with specific sourcing. Two requirements distinguish strong submissions. The first is criticality: the brief explicitly separates evaluation from description, and marks weight critical analysis most heavily. Reporting what a firm did is not the task; assessing whether it was the right response, and what opportunities and risks it created, is. The second is the recommendation — at least one specific, actionable proposal with steps, anticipated obstacles, expected outcomes, and a clear line back to the firm's own stated goals. A generic suggestion to "diversify suppliers" fails this test; a costed, sequenced proposal grounded in the firm's actual constraints does not. Word-count conventions are stricter than students often expect: in-text references, tables, illustrations and front matter typically count, while appendices and the reference list do not. Our support on assessments of this type is guidance-based. Typical areas of help include: narrowing a firm and theme so the analysis fits the word limit, explaining the difference between description and critical evaluation with worked examples, clarifying how a theoretical framework should structure an argument rather than sit decoratively in a paragraph, checking source mix and Harvard referencing consistency, and reviewing a completed draft against the published assessment weightings.
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Computer Science
1,700 words
Systematic Literature Review — Final Report with Data Extraction and Synthesis
A Research Methods final report at postgraduate computer science level is a systematic literature review written to a defined protocol. Unlike an essay, the method itself is assessed: markers re-run the student's search string and check that the number of papers returned matches what the report claims, so the process must be reproducible rather than merely described. The report is typically built in five chapters. Chapter 1 introduces the research area and states that a literature review is the chosen method. Chapter 2 gives the background, with citations behind every claim or assertion. Chapter 3 sets out the research protocol — the research question decomposed through PICO (population, intervention, comparison, outcome), the search string, and explicit inclusion and exclusion criteria. Chapter 4 presents the results: a data extraction table holding direct quotations and references from each included paper, followed by a data synthesis table that groups those extractions into cross-cutting themes. Chapter 5 concludes by answering the research question from the Chapter 4 evidence alone. Several requirements catch students out. The word limit applies to the main body only — tables, figures, references and appendices sit outside it, which changes how the argument should be distributed. Screenshots and supplementary search strings belong in appendices, not the body. Where an interim report has already been submitted and marked, the final report must visibly incorporate that formative feedback rather than reproduce the earlier chapters unchanged. File naming and file completeness are often mark-bearing in their own right, with missing files scored at zero. Presentation carries weight too: consistent heading and font usage, labelled tables and figures, and error-free spelling and punctuation. The most common conceptual error is treating Chapter 4 as a narrative summary of each paper in turn. A synthesis groups evidence by theme across papers and answers the question; a summary walks through the reading list. A related error is a research question that the background has not motivated — the introduction and background should make the question feel necessary before the protocol formalises it. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining how to decompose a research question using PICO, reviewing whether a search string is syntactically valid and whether inclusion criteria genuinely follow from the question, showing how extraction tables feed into synthesis tables, clarifying Harvard referencing conventions, checking report structure and formatting against the specification, and reviewing a completed draft against the published marking criteria before submission.
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Research Methods (Computer Science / Systematic Literature Review)
500 words
Interim Report – Research Methods (7COM1085)
This assignment is an Interim Report for the Research Methods module (7COM1085). The purpose of the report is to demonstrate that the research team has identified a suitable Computer Science research topic and developed a systematic literature review protocol that can be used for the final dissertation or research project. Students are required to formulate a clear and well-justified research question, define the associated PICO (Population, Intervention, Comparison, Outcome) elements, create an IEEE Xplore Boolean search strategy, and establish appropriate inclusion and exclusion criteria for selecting academic literature. The report must also provide background information on the chosen research area and justify the importance of the research question using relevant scholarly sources. The interim report should follow a structured academic format consisting of an Introduction, Background, Literature Review Protocol, and Bibliography. Students must use Harvard referencing throughout the report and ensure that all citations are properly integrated within the text. The report should demonstrate consistency between the research question, search strategy, and selection criteria while maintaining a professional academic presentation. The main body of the report must not exceed 500 words, although supporting materials such as search results, screenshots, and other evidence may be included in appendices. The assignment is designed to assess students' understanding of research methodologies, systematic literature reviews, critical analysis of academic sources, and their ability to design a rigorous research protocol for a Computer Science investigation.
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