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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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Fundamentals of Digital Technologies
Fundamentals of Digital Technologies – Group Project and Individual Refinement
This assessment is a project-based assignment for the DG4FDTL Fundamentals of Digital Technologies module. It is designed to develop students' practical understanding of digital technologies by combining mathematical concepts, programming, algorithms, data analysis and learning methods within a real-world project. The project is structured around a common group component, known as the Group Trunk, and an individual component in which each student develops a specialised refinement of the shared project. The project topics are designed around key areas covered by the module, including linear algebra, calculus, probability, learning algorithms, algorithms and Python programming. Students work in groups to establish a common project foundation and then develop an individual refinement that extends the functionality or analytical capabilities of the shared system. The project guidance is intended for a mixed cohort that may include Data Science, Computer Science and Business Analytics students. The assessment is divided into two main components: Proposal and Implementation. The Proposal accounts for 40% of the assessment, while Implementation accounts for 60%. Both the group trunk and individual trunk contribute to the assessment. The group proposal requires students to describe what the group intends to build, explain the problem being addressed, outline the proposed approach and identify the main functions and responsibilities within the project. Students should demonstrate a clear understanding of the project objectives and provide an appropriate plan for developing the shared system. The implementation stage requires students to develop the common group functionality and then complete their individual refinement. The shared component provides the basic project framework, while the individual refinement allows each student to investigate a specific aspect of the problem and add specialised functionality. Depending on the selected project topic, individual refinements may involve data analysis, visualisation, optimisation, prediction, monitoring, reporting or other computational features. The project topics include practical applications such as productivity and task-management systems, supermarket sales analysis and other data-driven applications. Students are expected to use Python and appropriate libraries or computational techniques to implement their solutions. The project materials provide examples involving data structures, CSV files, functions, numerical calculations, visualisation and analytical dashboards. The assessment emphasises both technical implementation and the student's ability to explain the problem, approach and functionality of the developed system. Students should demonstrate appropriate use of programming concepts, mathematical foundations, algorithms and data-analysis techniques. The individual refinement should clearly extend the common project and demonstrate the student's own contribution to the overall solution. Overall, the assessment develops practical digital-technology skills through collaborative project development followed by individual technical refinement. It provides experience in project planning, programming, computational problem solving, data analysis, visualisation and the application of mathematical and algorithmic concepts to practical problems.
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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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Computing Science
Writing a Literature Review in Computing Science
This individual assessment requires students to write a concise literature review on a selected topic within computing science. The review should be no more than six pages in length and should be written in a style appropriate for a general computing science audience. The assignment is designed to demonstrate technical knowledge, independent learning, effective written communication and professionalism in producing a concise technical document. Students must select a research topic from one of five permitted areas: algorithmic bias and fairness, a data science application, quantum computing, the Internet of Things (IoT), or the use of artificial intelligence in cybersecurity. Possible topics include how algorithms can discriminate and techniques for detecting and correcting algorithmic bias, applications of data science in areas such as agriculture or healthcare, quantum computing algorithms and hardware, technical IoT problems and potential solutions, and the use of AI for cybersecurity threat detection and prevention. The literature review must contain several required components. The first page should contain only the title, student number, abstract and statement of AI usage. The abstract must provide a concise overview of the review and must not exceed 200 words. The report should also include an introduction that provides broad background information before narrowing the discussion to the selected research topic. The introduction should explain why the topic is important and provide relevant context and examples of applications. Students are expected to review a range of relevant literature, including theories, methods, techniques, ethical concerns or tools where appropriate. The selected literature should not simply be described individually; instead, students must synthesise the sources to identify important themes, findings and areas of interest and provide a critical review of the literature. The conclusion should summarise the main findings, identify open issues and discuss possible future directions. The assessment must include a bibliography with accurate and up-to-date references formatted using Harvard style. The final document may be prepared in LaTeX or Word but must use one of the provided templates and be submitted as a PDF through Blackboard. The complete review, including figures and bibliography, must not exceed six pages. The assessment is marked according to structure, sources and their description, synthesis and critical review, and presentation.
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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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Computer Science / Research Methods / Specialist Research
2,800 words
Research Specialism Report: Critical Review of Open Research Questions in Computer Science
This Advanced Research Topics in Computer Science assessment requires students to critically examine a research paper associated with their chosen MSc specialism and demonstrate an understanding of how established research techniques are used to create and extend knowledge in computer science. Eligible specialisms include Artificial Intelligence, Networking, Cyber Security, Software Engineering and Data Science. The assessment is intended to prepare students for deeper independent research as part of their Master's project. 7COM1084+Research+specialism+re… Students begin by providing a clear introduction to their selected research specialism and explaining the broader research area in a way that is accessible to readers with general computer-science knowledge. The report then identifies the open research question presented in the relevant 7COM1084 specialist lecture paper, explains the problem in detail and evaluates why it is scientifically significant or relevant to a real-world application. 7COM1084+Research+specialism+re… A substantial literature-review section requires students to examine existing and related research beyond the specialist lecture paper. The aim is to identify what previous work has achieved, explain why existing approaches do not fully solve the research problem and identify further unresolved questions. 7COM1084+Research+specialism+re… The research-methods section focuses on the approaches used in the selected specialist paper. Students are expected to describe and critically evaluate those methods, considering both their strengths and limitations. They must then propose an alternative or extended research approach that could build on the published work and investigate related open problems, drawing on principles of experimental design and theoretical or practical research. 7COM1084+Research+specialism+re… The final reflective component asks students to explain their personal investment in the research area, including why the selected question interests them and how their own strengths and prior experience would support future research in that domain. 7COM1084+Research+specialism+re… The report must not exceed 2,800 words ±10%, must use the Harvard referencing system, and must include at least 20 references, one of which must be the relevant 7COM1084 specialist paper. 7COM1084+Research+specialism+re… 7COM1084+Research+specialism+re… 7COM1084+Research+specialism+re… Overall, the assessment integrates research specialism knowledge, literature review, open-problem identification, methodological critique, research design, future-work development and scholarly communication within a Level 7 computer-science research context.
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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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Data Science / Data Management / Professional Practice
4,500 words
Tesla Data Science Professional Case Study: Data Management, Leadership, Entrepreneurship and Ethics
This composite case-study assessment for The Data Science Professional module requires students to critically analyse Tesla from several interconnected professional perspectives, including data management, artificial intelligence ethics, leadership, organisational development, entrepreneurship and business risk. The coursework is designed to combine technical data-science capability with strategic, ethical and managerial decision-making. a82a1aa6a2e7a3268ad021c4f5b3d44… Part A – Database Design and Distributed Frameworks focuses on data management. Students design an Entity-Relationship model and relational schema for a Tesla-related vehicle-hire business, identifying entities, relationships, cardinalities, identifiers, primary keys and foreign keys. The accompanying guidance specifies entities relating to vehicles, employees, outlets, clients, hire agreements, insurance, faults and employment records. a82a1aa6a2e7a3268ad021c4f5b3d44… 39cdc24cc46a13ddf444b8e7af838eb… The SQL component uses an Online Music database to examine Tesla customer preferences. Students create relational tables using Oracle standard SQL and write queries involving users, music, publishers, categories and download records. Evidence of implementation and query results must be provided using Oracle Live SQL. 39cdc24cc46a13ddf444b8e7af838eb… Part A also requires a critical assessment of the security, privacy and ethical implications of Tesla’s Full Self-Driving technology, connecting technical development with responsible data and AI practice. a82a1aa6a2e7a3268ad021c4f5b3d44… Part B – Leadership and Developing People requires critical evaluation of Tesla’s leadership model, organisational culture and their effects on employees and organisational performance. Students must propose a leadership and people-development strategy capable of supporting the organisation as it expands. a82a1aa6a2e7a3268ad021c4f5b3d44… Part C – Entrepreneurial Practice and Managing Risk examines a proposed Tesla spin-out venture developing innovative low-cost green hydrogen production systems. Students critically assess management support for the venture, propose an evidence-based approach to entrepreneurial risk, develop an entrepreneurial leadership role descriptor, and evaluate how GDPR and AI/data ethics may support or constrain entrepreneurial practice. a82a1aa6a2e7a3268ad021c4f5b3d44… Overall, the assessment integrates technical database design, SQL, data ethics, organisational leadership, entrepreneurship, sustainability and professional decision-making within a single Tesla-focused case study. Overview word count: approximately 360 words. Important: the assessment brief itself explicitly states that it must not be passed to third parties or posted on any website. So for a public Reference Library, use the metadata and your own finished work where permitted, but do not upload the assessment brief/guidance PDFs themselves publicly. a82a1aa6a2e7a3268ad021c4f5b3d44… The AI status is Amber: generative AI may be used for limited inspiring/planning purposes, but usage must be acknowledged with the tool, prompts and relevant evidence; the brief also specifically prohibits using LLMs to generate the Part A(3) essay. a82a1aa6a2e7a3268ad021c4f5b3d44… a82a1aa6a2e7a3268ad021c4f5b3d44…
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Data Science / Data Management / Leadership and Entrepreneurship
4,500 words
The Data Science Professional: Tesla Data Management, Leadership and Entrepreneurial Practice
This interdisciplinary Data Science Professional assessment uses Tesla as the central organisational case study to examine how data management, leadership, entrepreneurship, technology and ethics interact within a contemporary technology-driven business. Students produce an individual report of 4,500 words equivalent, combining technical data-science competencies with strategic and managerial analysis. The Data Science Professional A… The Data Science Professional A… Part A – Database Design and Distributed Frameworks focuses on data management. Students design an Entity-Relationship model for Tesla-related organisational data, translate the model into a relational schema and justify the database structure. They also work with a specified database to produce Oracle SQL queries for analytical requirements. A further component critically evaluates the security, privacy and ethical implications of Tesla's Full Self-Driving technology, supported by relevant academic evidence. The Data Science Professional A… Part B – Leadership and Developing People requires critical analysis of Tesla's leadership model and organisational culture. Students examine current leadership challenges and their implications for organisational and employee performance before proposing a leadership and people-development strategy designed to support future organisational growth, employee engagement and sustained performance. The Data Science Professional A… Part C – Entrepreneurial Practice and Managing Risk investigates the proposed creation of a Tesla spin-out venture focused on innovative, low-cost green hydrogen production systems. Students critically evaluate management support for the entrepreneurial initiative and recommend an evidence-based course of action. They must also propose a multidimensional approach to mitigating entrepreneurial risks and barriers, critically assess entrepreneurial leadership characteristics, and develop a role descriptor for the person who would lead the new venture. The Data Science Professional A… The final element examines whether GDPR and data or AI ethics constrain or support entrepreneurial practice among employees. Overall, the coursework integrates database modelling, SQL, data governance, leadership development, organisational culture, entrepreneurship, innovation, risk management and ethical decision-making within a single applied case study. The Data Science Professional A… Important for the public Reference Library: the brief explicitly states that it is for Coventry University Group students' own use and must not be passed to third parties or posted on any website. The Data Science Professional A… So publish an original high-level overview like the one above, but do not upload the original assessment brief itself to the public library.
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Data Science / Data Analytics
Airline Passenger, Ticket Price and Revenue Analysis Using Python
This applied Data Science project requires students to use Python to analyse passenger demand, ticket prices and airline revenues across the period 2021–2023. Two datasets are provided: one containing daily passenger counts and revenue or average-price information for 2021 and 2022, and another containing a representative sample of individual journeys made during 2023. The datasets may include domestic, European and intercontinental routes as well as standard/economy and business/club travel classes. 202526_SmallProject_semB Students must develop Python code that reads the assigned datasets and produces a series of analytical visualisations. A core requirement is to plot daily passenger numbers for economy and business classes across all three years, combining scatter plots with smoothed trend lines. The smoothing must be calculated using the first eight terms of a Fourier series through the supplied scrfft.py module. 202526_SmallProject_semB Further analysis examines changes in average ticket prices, including monthly comparisons across 2021, 2022 and 2023, together with annual average prices that incorporate revenue generated from additional services such as seat reservations and extra baggage. 202526_SmallProject_semB A fourth analysis is personalised according to the final digit of the student's ID. Depending on the allocated task, students may investigate weekly journey distributions, seasonal travel patterns, price-versus-distance relationships, business and economy class behaviour, additional-service revenues, weekend journeys or linear relationships between selected variables. Three additional numerical values, labelled X, Y and Z, must also be calculated according to the assigned scenario. 202526_SmallProject_semB The final report must include four figures, relevant mathematical formulae and a critical discussion of the findings. Students are expected to interpret trends in passenger numbers, prices, revenues, seasonal patterns and airline profitability rather than simply presenting calculations. The report is limited to five A4 pages, while the Python source code and report are submitted separately. 202526_SmallProject_semB Overview word count: approximately 330 words. Note: the university name is not printed directly in the visible assignment heading, but the submission instructions use a herts.ac.uk module-leader address and StudyNet/Canvas, which identifies the institution as the University of Hertfordshire. 202526_SmallProject_semB
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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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Statistical Programming / Data Science / Business Analytics
Statistical Programming with R: Data Analysis, Probability, Regression and Business Decision-Making
This Statistical Programming assessment evaluates students' ability to apply statistical techniques and R programming to practical data-science and business decision-making problems. The individual assessment combines descriptive statistics, data preparation, visualisation, probability, regression, correlation and sampling, requiring students to demonstrate both conceptual statistical understanding and practical implementation in RStudio. The module learning outcomes emphasise the application of statistical methods to large and real-world datasets, critical evaluation of analytical techniques and awareness of legal, cultural and ethical issues associated with data analysis and dissemination. KL7012 - Statistical Programmin… The early tasks examine fundamental statistical reasoning. Students interpret weight-loss data comparing exercise classes with gym-only workouts using sample size, mean, mode and standard deviation, and explain an appropriate method for dealing with missing data, including its advantages and disadvantages. KL7012 - Statistical Programmin… A substantial practical component uses a cystic fibrosis dataset containing variables such as age, sex, height, weight, body-mass-related measurements, forced expiratory volume, residual volume, functional residual capacity, total lung capacity and maximum expiratory pressure. Students import the data into an R data frame, generate descriptive summaries and interpret the results. They then use scatterplots to investigate relationships between variables and sex-stratified boxplots to identify possible outliers. KL7012 - Statistical Programmin… The assessment also covers major probability models. Students apply probability concepts to healthcare survival, helpdesk email arrivals and fuel-demand scenarios, while also discussing how changing assumptions or real-world conditions can affect interpretation. These exercises assess understanding of statistical distributions and their application to operational and managerial decision-making. KL7012 - Statistical Programmin… Further analytical tasks examine linear regression and correlation. Students analyse the relationship between temperature and converted sugar in a chemical process, use a regression model to estimate the expected response at a specified temperature, and interpret relevant summary statistics. They also calculate and evaluate the suitability of a correlation coefficient for examining the relationship between advertising activity and product purchases. KL7012 - Statistical Programmin… The final and most substantial task involves a real-world M1 traffic-speed investigation for a manufacturing organisation. Students must design an appropriate sampling strategy, collect data from the specified Traffic England source, conduct statistical analysis in RStudio and develop evidence-based conclusions. The statistical report for this task is limited to 1,500 words and should include sampling methodology, collected data, statistical analysis, results, conclusions and relevant background research, supported by appropriate graphs, tables and charts. Raw data and RStudio calculations must be included in an appendix. KL7012 - Statistical Programmin… Overall, the assessment integrates statistical theory with R-based practical analysis, covering descriptive statistics, probability, visualisation, missing-data treatment, regression, correlation, sampling and critical interpretation of results in healthcare, operational and business contexts.
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Information Visualisation / Data Analytics / Data Science
1,500 words
Information Visualisation Project Using Power BI and Python
Assignment overview — ready to paste This Information Visualisation project requires students to design, implement and critically evaluate effective data visualisations using two different technological approaches: Microsoft Power BI and Python. The assessment focuses on the practical application of information-visualisation principles, including data preparation, visual design, interaction, audience requirements and the extraction of meaningful patterns and insights from complex datasets. Assignment 002 Coursework 2025-… Task 1 focuses on interactive visualisation using Power BI. Students work with the UK Department for Transport's Road Safety Open Data (STATS19), which contains information relating to road traffic accidents, casualties, vehicles, locations, times and contributing factors. Students may analyse one year or multiple years of data depending on their visualisation objectives. Assignment 002 Coursework 2025-… The Power BI work requires students to identify an appropriate target audience and report type, select relevant variables, clean and transform the data, develop an appropriate data model and create analytical measures using Data Analysis Expressions (DAX). The resulting dashboard should communicate the context of the data and reveal meaningful trends, patterns and insights. Assignment 002 Coursework 2025-… Task 2 requires students to develop visualisations programmatically using Python, with Jupyter Notebook recommended as the implementation environment. Students independently select a real-world, publicly available dataset containing at least 10,000 observations and more than five variables. Unlike Task 1, visualisation tools that automatically construct visualisations, such as Tableau or Power BI, cannot be used for this component because programming is an explicit requirement. Assignment 002 Coursework 2025-… Assignment 002 Coursework 2025-… The submitted Jupyter Notebook should operate as an educational technical report explaining the selected dataset, preprocessing procedures, visualisation choices and resulting insights. Students are expected to justify their visualisation techniques, critically evaluate findings and discuss challenges encountered during development. The textual content of the notebook is limited to 1,500 words, excluding code and visualisations. Assignment 002 Coursework 2025-… The complete assessment contains several deliverables, including a maximum 6-minute Power BI demonstration video, a maximum 2-page Power BI report, the .pbix file, an 8-minute Python/Jupyter visualisation demonstration, the Jupyter Notebook, dataset and README file. All materials must ultimately be packaged into a single ZIP submission. Assignment 002 Coursework 2025-… Assignment 002 Coursework 2025-… Important: the brief does not specify a named referencing style or academic level, so I would select Not specified for those two portal fields rather than guessing. It also explicitly states that generative AI must not be used to create any part of the assessed submission, including code, debugging, writing, paraphrasing or bibliographies. Assignment 002 Coursework 2025-…
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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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Computer Science
2,800 words
Specialism Research Report
The Specialism Research Report is an individual assessment for the Advanced Research Topics in Computer Science module. The assignment provides students with an opportunity to develop a deeper understanding of a selected computer science research specialism and to investigate an open research question within that area. The available research specialisms are Artificial Intelligence, Networking, Cyber Security, Software Engineering and Data Science. Students are expected to examine the research specialism associated with their degree route and develop their understanding of current research challenges, established techniques and potential future research directions. The report requires students to provide an overview of the research paper presented in the relevant 7COM1084 Research Specialism Lecture. The report must identify and explain the research question addressed by the specialist paper, consider why the research problem is scientifically interesting or relevant to a real-world application, and investigate possible approaches for addressing the question. Students must also identify further research that could build upon the existing work and consider their own personal strengths, interests and experience in relation to the proposed research. The report is structured into five main sections. The first section, Introduction of Research Specialism, provides a general overview of the selected research area and is intended to be accessible to readers with broad computer science knowledge. The second section, Open Research Question, identifies and explains the research problem discussed in the specialist lecture paper and considers its scientific or practical significance. The third section, Existing and Related Work, provides a literature review of research beyond the specialist paper, identifies limitations in existing approaches and discusses related open research problems. The fourth section, Research Approach, examines the methods used in the specialist paper, evaluates their strengths and weaknesses, and proposes an approach for extending the research. The fifth section, Personal Investment, explains the student's interest in the research question and evaluates their personal strengths and experience relevant to conducting the proposed research. Students are expected to use module reading materials and secondary research to support their discussion. The report must use Harvard referencing and include at least 20 references, including the 7COM1084 specialist paper. The references are excluded from the word count. The submission must not exceed 2,800 words, with a permitted range of ±10%. The assessment develops students' ability to understand established research techniques, identify research problems from relevant literature, propose alternative solutions, critically evaluate research literature, develop approaches for future research and communicate research knowledge effectively in a scholarly manner.
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Principles of Data Science
3,000 words
Principles of Data Science – Predictive Modelling and Data Analysis
This individual assessment for the Principles of Data Science module requires students to select, apply and critically evaluate data science methods, tools and techniques using one of three provided datasets and its associated scenario. The main assessment takes the form of a 3,000-word report in which students explore their chosen dataset, identify an appropriate predictive modelling approach, build and evaluate models, interpret the findings and critically reflect on the overall process and outcomes. The assessment addresses the principles and foundations of data science, statistical methods, data preparation, visualisation, predictive modelling, decision making and the critical evaluation of data science techniques and tools. Students begin by exploring the selected dataset to understand its structure, characteristics and limitations. Although the supplied datasets have already been cleaned, students may undertake additional data preparation or transformation where necessary. Any preprocessing decisions must be justified in relation to the requirements of the selected analytical methods. Feature selection should also be considered as part of preparing the data for model development. The assessment requires students to identify suitable forms of analysis for the selected scenario and justify their choice of methods. At least two different techniques must be used to develop models with predictive capacity for the response variable in the chosen dataset. The models must be trained and tested consistently, using the same training and test datasets so that their performance can be compared fairly. Where appropriate, students should also provide insight into feature importance and explain the contribution of relevant variables to predictive performance. Model performance must be evaluated using suitable metrics, followed by a clear description of the findings and recommendations appropriate for the intended audience. The report should document the complete analytical workflow, including data exploration, preprocessing, feature selection, model development, testing and evaluation. Students are expected to explain and justify the decisions made throughout the process rather than simply presenting code or model results. The assessment also requires students to demonstrate practical proficiency in data science tools and techniques. The brief expects the use of R for completing the assignment and requires evidence of important elements of the code, although the complete code does not need to be submitted. Data visualisation must be used to support the written discussion and communicate relevant findings effectively. The assessment is evaluated across theoretical knowledge and method selection, data exploration and processing, technical application and model evaluation, communication of findings, and overall presentation and referencing. The assessment therefore combines technical implementation with critical analysis, requiring students to explain why particular methods were selected, evaluate their effectiveness and consider the limitations and implications of the resulting findings. A separate second assessment component accompanies the written report. This component requires a presentation of the key findings from the written work using a maximum of five slides and a presentation duration of no more than seven minutes. It should summarise the dataset, methods, key findings and project outcomes while providing critical reflective commentary on lessons learned, factors affecting success and potential real-world applications.
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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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Programming for Data Science
4,000 words
Programming for Data Science – Individual Portfolio
This individual portfolio assessment for the Programming for Data Science module at Coventry University consists of four tasks designed to assess programming, debugging, data science, data visualisation, data protection and data ethics skills. The assessment carries 20 credits and has a total value of 4,000 words equivalent, excluding the reference list and output. Students are required to submit one clearly organised report containing all four tasks, with each task beginning on a new page. Python code, outputs and relevant plots must be included directly within the report. Task 1 focuses on analysing, critiquing and debugging Python code. Students are required to identify syntax errors, logical errors, style and readability issues in a supplied program, determine what the program is intended to calculate, and make appropriate corrections. Students must test the program using varying values, explain the changes made, and improve its overall readability and annotation so that an unfamiliar user can understand it. The task also requires students to investigate computational efficiency by measuring execution time for different input limits and identifying more efficient coding or logical approaches. Task 2 requires students to design, build and test a simple Python implementation of the Blackjack card game. The program should simulate a single-player game against a computer-controlled dealer, allow the player to choose between hitting and standing, automatically simulate the dealer's turn, and offer the option to play additional rounds. Complex rules such as splitting and betting are excluded, and students must not implement a Python class or graphical visualisation. The submission must include the Python code and the output from five games, with sufficient storytelling in the output to allow the code to be tested from the results shown. Task 3 assesses the student's ability to critically assess, select and apply data science tools. Students work with animal lifespan information from the AnAge database and use Python, including pandas and appropriate graphical libraries, to explore and communicate insights. The task involves summarising the number of animal species represented within each animal Class and producing plots of maximum longevity against adult weight for the four Classes with the most represented species. Students must discuss whether smaller or larger animals live longer, identify extreme outliers, compare trends between animal groups and consider implications for ageing research. Task 4 examines data protection and data ethics using the Cancer Genome Atlas (TCGA) as a case study. Students must explain how a potential data breach could occur, identify the personal and sensitive information that could be compromised, and discuss consequences for patient confidentiality, institutional reputation, participation in future research and possible legal or public relations responses. A second part considers a hypothetical UK database and requires discussion of GDPR and the UK Government Data Ethics Framework, including informed consent, anonymisation, transparency, ethical governance, privacy and public trust in biomedical research. The assessment assesses two module learning outcomes. MLO2 focuses on designing, building, testing, adapting and critiquing small programs in a high-level programming language and is assessed through Tasks 1 and 2. MLO3 focuses on critically assessing, selecting and applying data science tools, libraries or algorithms throughout the data science project lifecycle and is assessed through Tasks 3 and 4. The assignment requires APA referencing and asks students to provide in-text citations and reference lists where relevant. The brief also classifies the assessment as “Amber” for Generative AI: AI tools may be used for inspiration but not to generate answers or analyse datasets. Any permitted use must be clearly acknowledged, documented and cited using APA style.
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Principles of Data Science
2,000 words
Principles of Data Science – Data Analysis Portfolio
This portfolio assignment for the Principles of Data Science module at Coventry University requires students to analyse the Global Life-Work Balance Index 2025 dataset using statistical and data science techniques in R. The dataset ranks 60 countries according to life-work balance using factors including statutory annual leave, paid maternity leave, sick leave, healthcare, public safety, public happiness, LGBTQ inclusivity and average working hours per employee. The assignment has a 2,000-word equivalent limit, excluding the reference list and output. The portfolio consists of two main tasks. Task 1 is a group task involving multivariate data analysis. Students must use R to perform Principal Component Analysis (PCA) and Cluster Analysis on the dataset. For PCA, students analyse quantitative variables, produce and interpret relevant visualisations such as screeplots, biplots and loadings plots, and investigate the effects of Region and Healthcare System. The PCA analysis also requires comparison of the overall dataset with countries from Europe. The cluster analysis component requires students to cluster both countries and variables using different distance metrics and hierarchical clustering methods. Students compare methods such as Manhattan and Euclidean distances and single linkage and Ward’s method, present comparisons in compact tables, and interpret relevant dendrograms. They must then compare the conclusions obtained from PCA and Cluster Analysis, identifying common insights and apparent conflicts and discussing the extent to which the results are explainable rather than simply interpretable. Task 2 is an individual task focusing on Exploratory Data Analysis and Linear Models. Students create a scatter matrix using ggpairs(), investigate strongly correlated variables, and identify quantitative variables that may help predict Region for European and Asian countries. They then develop and critically assess linear regression models for predicting Score, including models based on employment variables and broader quantitative predictors. Model comparison and selection use concepts including AIC, while diagnostic plots are used to identify countries requiring further investigation. The individual task also requires students to use European Life-Work Balance Index 2023 data to make predictions for European countries not included in the 2025 dataset and to construct a Residuals versus Fitted Values plot. Finally, students must combine the conclusions from their individual linear modelling work with the PCA and Cluster Analysis findings to identify specific discoveries about the variables and countries in the dataset. R code, output and relevant plots must be included directly within the reports. The assignment encourages use of the R tidyverse and requires appropriate referencing of sources. The brief specifies APA-style referencing for the individual and group work. It also states that generative AI may be used for inspiration but not for generating answers or analysing the datasets, and any permitted AI use must be acknowledged and documented.
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Data Mining / Data Science
Customer Service Escalation Risk Analytics Using Data Mining and Machine Learning
This advanced Data Mining assessment applies the Knowledge Discovery in Databases (KDD) process to a real-world customer-service analytics scenario. Acting as a Data Scientist, students analyse a historical Customer Service Escalation Risk dataset to help an organisation reduce serious and legal customer escalations by identifying early signs of dissatisfaction, service bottlenecks and operational risk. The findings are intended to support business decisions relating to staffing, employee training, customer-journey improvement and escalation prevention. The dataset contains information covering customer demographics, account characteristics, communication channels, issue categories, operational measures such as wait times, transfers and SLA breaches, behavioural indicators including sentiment and response delays, and commercial variables such as monthly fees and contract value. The target variable contains four escalation outcomes: No escalation, Minor escalation, Serious escalation and Legal escalation. The first stage requires data exploration, visualisation and summary, including examination of variable distributions, dataset structure, descriptive characteristics and potential data-quality issues. Students then perform appropriate data cleaning, transformation, feature engineering and preprocessing. Particular attention must be given to variables that could introduce prediction leakage because of their meaning, timing or reliability. The supervised-learning component requires development and tuning of predictive models using suitable techniques such as k-nearest neighbours, Decision Trees, Support Vector Machines, ensembles or neural networks. Models must be evaluated using appropriate multiclass metrics and compared systematically, with interpretation of influential features and model behaviour. The assessment also requires unsupervised learning. After removing the escalation target, students apply and compare clustering approaches such as K-Means and hierarchical clustering. Appropriate preprocessing, encoding, normalisation or dimensionality reduction may be used, with visualisations such as PCA, t-SNE or scatterplots used to explore cluster structure and its relationship with escalation behaviour. Overall, the project assesses the student's ability to independently design a coherent KDD workflow, justify analytical decisions, compare alternative modelling approaches and communicate actionable findings to both technical and executive audiences. Overview word count: approximately 350 words. AI-use note: the brief permits AI tools only to assist with small, specific code snippets. Any AI-generated code must be clearly acknowledged and cited, while the submitted coding, analysis, interpretation and decision-making must remain the student's own work.
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Machine Learning / Artificial Intelligence and Data Science
1,000 words
End-to-End Machine Learning Model Development, Tuning and Evaluation
This Level 7 Machine Learning and Intelligent Agents assessment requires students to develop and document an end-to-end machine-learning solution, covering the complete workflow from data preparation through model training, tuning, testing and evaluation. Students select an appropriate dataset or scenario, formulate a research question and determine whether the problem is most appropriately addressed through supervised learning, unsupervised learning or reinforcement learning. Suitable machine-learning techniques must then be implemented to create a model that can be systematically trained and tested. The assignment requires students to follow a structured machine-learning development process and document the complete development journey. The report should explain the selected scenario, data collection or dataset, Exploratory Data Analysis (EDA), rationale for selecting particular machine-learning methods, model training, fine-tuning and evaluation. Model performance must be assessed using appropriate established metrics, with relevant published research used to justify methodological decisions and support the interpretation of results. The technical implementation should demonstrate the ability to identify the performance of machine-learning algorithms, implement machine-learning approaches using one or more object-oriented programming languages, and determine which algorithms are most appropriate for a particular analytical brief. These requirements directly correspond to the module learning outcomes relating to machine-learning performance, implementation and algorithm selection. Students are advised to document their work within a Jupyter Notebook, combining Markdown explanations with executable code. The notebook may be submitted directly or converted to PDF. Alternatively, students may prepare the 1,000-word report in Microsoft Word, provided that the Python code is included within the submitted document. Assessment is divided into three principal areas: Introduction (20 marks), Machine Learning Process (40 marks), and Evaluation of Model Performance (40 marks). Higher-level work is expected to demonstrate strong understanding of machine-learning concepts, a functioning and thoroughly tested implementation, appropriate selection of algorithms and critical evaluation of the developed solution. Overall, the assessment integrates research-question formulation, data exploration, algorithm selection, programming, model optimisation and evidence-based evaluation within a reproducible machine-learning workflow. All academic sources must be presented using Harvard referencing.
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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assessment requires students to develop a comprehensive analytical solution to a real-world healthcare prediction problem using the WiDS Datathon 2025 Health Outcomes Prediction Dataset. The dataset contains socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents, with the principal objective of developing predictive models for ADHD diagnosis. The assessment is designed to demonstrate the complete data-science lifecycle, from data preparation and exploratory analysis through predictive modelling, interpretation and evidence-based recommendations. Students begin by exploring the dataset's features, data types and distributions before addressing missing values, outliers and other inconsistencies. Appropriate feature engineering should be undertaken where necessary, followed by Exploratory Data Analysis (EDA) using relevant visualisations to identify relationships, patterns and correlations within the data. Students with limited computational resources may use a representative subset, provided that the sampling method preserves the integrity and distribution of the original dataset and is clearly justified. A major component of the assignment involves developing and comparing at least three classification models. Appropriate techniques may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Model performance should be evaluated using measures including accuracy, precision, recall, F1-score and ROC-AUC, allowing students to identify the strongest-performing model through systematic comparison. The assessment also requires model interpretation and explainability. Students should explain the results of the selected model and may apply techniques such as SHAP or LIME to investigate feature importance and individual predictions. A feature-importance visualisation must be produced, and the most influential variables should inform practical recommendations for healthcare professionals regarding the potential use of predictive modelling in supporting earlier ADHD diagnosis and intervention. Overall, the assignment integrates data cleaning, exploratory analytics, predictive modelling, model comparison, explainable AI and research-informed healthcare recommendations. Students must submit a comprehensive report of no more than 2,500 words, alongside a Jupyter Notebook containing the implementation and outputs. The report must use Harvard referencing, with appropriate academic research integrated into the analysis, recommendations and conclusion.
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Machine Learning / Data Science
Linear Regression and Stability of the Moore–Penrose Pseudoinverse Using Python
This machine learning practical and assessment activity develops an understanding of linear regression, Ordinary Least Squares and the Moore–Penrose pseudoinverse using Python. The work progresses from generating synthetic regression datasets to implementing regression algorithms manually, applying established machine learning libraries, analysing real datasets and evaluating the stability of estimated regression coefficients. The laboratory component begins with the generation of synthetic linear regression data using NumPy, including explanatory variables, random noise and an outcome variable. Students then implement simple linear regression without relying on machine learning libraries, using the least-squares solution to estimate the intercept and slope. The resulting observations and fitted regression line are visualised using Matplotlib. The work is subsequently extended to multiple linear regression, where several independent variables are used and coefficients are first calculated manually before the same problem is solved using Scikit-learn. The laboratory also introduces application of regression to the Scikit-learn Diabetes dataset, including feature and target standardisation, model fitting, prediction, correlation analysis and interpretation of regression coefficients. It also highlights the importance of residual analysis when assessing whether a linear model is appropriate. The associated weekly challenge focuses on the stability of linear regression solutions estimated using the Moore–Penrose pseudoinverse. Using a house-price dataset containing variables such as property size, number of bedrooms, distance from the city centre and property age, students construct the design matrix, standardise features and the response variable, and calculate regression coefficients using the pseudoinverse. Students then investigate model robustness by repeatedly fitting the regression model to random subsamples of different sizes and analysing the mean and standard deviation of each coefficient. Tables, boxplots or error-bar visualisations can be used to compare coefficient variability. The final discussion considers which variables are most influential, which coefficients are most stable, how sample size affects stability and whether coefficient interpretation remains reliable across different samples. The final work is submitted as a single PDF exported from Jupyter Notebook or Google Colab, combining documented Python code, experimental results, plots and written interpretation in a professionally organised notebook. Overview word count: approximately 360 wor
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Machine Learning / Artificial Intelligence and Data Science
1,011 words
End-to-End Machine Learning Model Development, Testing and Evaluation
This Level 7 Machine Learning and Intelligent Agents assignment requires students to develop and document an end-to-end machine-learning solution, covering the complete process from data preparation through model training, tuning, testing and evaluation. Students independently select a suitable dataset or scenario, formulate an appropriate research question and determine whether the problem should be addressed using supervised learning, unsupervised learning or reinforcement learning. Appropriate machine-learning algorithms must then be implemented to create a model capable of being trained and objectively tested. The assessment encourages the use of a structured machine-learning development methodology. Students are expected to explain the selected scenario and data source, perform suitable data preparation and Exploratory Data Analysis (EDA), and provide a reasoned justification for the machine-learning methods selected. The development process should demonstrate how the chosen algorithms are trained and fine-tuned before their performance is evaluated using established and relevant metrics. Published academic research should be incorporated to justify methodological choices and support the interpretation of results. The technical work is normally documented within a Jupyter Notebook, combining Markdown explanations with executable code cells. Alternatively, the report may be produced in Microsoft Word provided that the Python implementation is included. The assignment therefore assesses both conceptual understanding and practical programming competence. Students must demonstrate an ability to identify the performance of machine-learning algorithms, implement machine-learning techniques using an object-oriented programming language, and evaluate which algorithms are appropriate for a particular analytical brief. Assessment places particular emphasis on three areas: the Introduction, the Machine Learning Process, and the Evaluation of Model Performance. The marking criteria reward strong understanding of machine-learning concepts, a functioning and thoroughly tested implementation, appropriate selection of algorithms, and critical evaluation of the final solution. At the highest achievement level, implementations are expected to work without exception, satisfy the required functionality, demonstrate comprehensive testing and extend beyond the basic requirements. Overall, the assignment combines research-question formulation, data analysis, algorithm selection, machine-learning implementation, model optimisation and evidence-based evaluation within a reproducible technical workflow. All academic sources and supporting material must be presented using Harvard referencing.
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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assignment focuses on developing a comprehensive analytical solution to a real-world healthcare prediction problem. Using the WiDS Datathon 2025 Health Outcomes Prediction Dataset, students are required to analyse complex and high-dimensional healthcare data containing socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents. The principal predictive objective is to determine ADHD diagnosis from the available features. Students may use a representative subset of the dataset where computational resources are limited, provided that the sampling approach maintains the integrity and distribution of the original data and is appropriately justified. The assessment requires a complete data-science workflow beginning with data understanding and preprocessing. Students investigate the dataset's features, data types and distributions before addressing missing values, outliers and inconsistencies. Appropriate feature engineering should then be undertaken where it can improve the predictive capability of the models. Exploratory Data Analysis is used to identify important patterns, relationships and correlations, supported by relevant visualisations that communicate meaningful insights. A major component of the work involves the development and comparison of at least three classification models for predicting ADHD diagnosis. Suitable approaches may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Models are evaluated using performance measures including accuracy, precision, recall, F1-score and ROC-AUC, after which the most effective model is selected based on the evidence obtained. The assessment also places substantial emphasis on model interpretation and explainability. Students must interpret the selected model and may use approaches such as SHAP or LIME to explain feature importance and individual predictions. A feature-importance visualisation is required, and the most influential variables should inform practical recommendations. The final section translates analytical findings into recommendations for healthcare professionals, considering how predictive modelling could assist early ADHD diagnosis and intervention. Research literature must be integrated into the recommendations and conclusion. The assessment therefore combines preprocessing, exploratory analysis, predictive modelling, explainable AI and evidence-based healthcare decision-making within a single applied data-science project. The required report is a maximum of 2,500 words, with code, supplementary charts and tables permitted in appendices. A Jupyter Notebook containing the implementation and outputs is also required. Harvard referencing must be used throughout.
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Data Science / Deep Learning
3,000 words
Advanced Research Topics (7PAM2016) — Building GANs from Scratch and Applying Them to Medical Imaging, Network Traffic and Sketch Generation
This Masters-level assessment asks for a complete generative adversarial network study, delivered as an annotated code submission carrying sixty per cent of the marks and a six-to-eight page technical report carrying the remaining forty. The work spans four separate GAN implementations, moving from a controlled synthetic setting into three contrasting real-world application domains. Part one builds a GAN from scratch in PyTorch on synthetic two-dimensional data. The tutorial sine-wave generator is reproduced first as a baseline, then a new distribution is modelled — a noisy parametric curve of the form y = sin(2x) + 0.3cos(5x) with an additive noise term — before the architecture itself is varied. Activation functions and layer depth are altered systematically and the resulting sample distributions plotted against the originals, so the effect of each architectural choice on convergence and sample fidelity can be seen rather than asserted. Part two applies the same principles at scale across three domains. The medical strand trains a DCGAN on the OCTMNIST subset of MedMNIST, generating synthetic optical coherence tomography retinal images, tracking generator and discriminator losses across training, and evaluating output both visually and quantitatively using Fréchet Inception Distance. A conditional GAN extension conditions the generator on class label so that images for a chosen retinal pathology can be produced on demand. The cybersecurity strand shifts from images to feature vectors, using preprocessed CICIDS 2017 network intrusion data. Benign and DoS traffic is combined and explored for class balance, a GAN is built to synthesise tabular feature vectors rather than pixels, and real against generated distributions are compared through PCA and t-SNE projections, with a discussion of how well the model generalises across attack types. The creative strand trains a DCGAN on the QuickDraw 'birthday cake' sketch category, tracking visual outputs epoch by epoch and benchmarking generated sketches against real ones, with an extension covering additional categories of differing sketch complexity. The accompanying report explains the analysis steps and the reasoning behind each architectural decision rather than restating textbook definitions of the method. It gives brief descriptions of the models used, presents generated samples and loss curves as figures, interprets the evaluation metrics, and reflects honestly on failure modes — training instability, mode collapse, and the visible flaws in synthetic output that determine whether such data is fit for downstream use. The code is written as reusable functions, commented for a reader other than its author, and reproduces every figure and numerical value quoted in the report.
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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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