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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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Applied Modelling and Visualisation
2,500 words
Applied Modelling and Visualisation – Hexawing Airways Passenger Satisfaction Analysis
This assessment is a 2,500-word consultancy report for the Applied Modelling and Visualisation module within the MSc Management with Data Analytics programme at BPP University. The assignment requires students to work as Data Analytics Consultants for the fictional Hexawing Airways and analyse a passenger satisfaction dataset containing more than 103,000 records from the airline's database. The purpose of the assessment is to apply machine learning, data analysis and visualisation techniques to identify factors that influence passenger satisfaction and communicate meaningful findings to a varied corporate audience, including the Chief Executive Officer, senior flight personnel and cabin crew. The dataset contains a range of passenger and flight-related features, including gender, satisfaction status, age, age band, type of travel, travel class, flight distance, destination, continent and ratings for services such as inflight Wi-Fi, online booking, gate location, food and drink, online boarding, seat comfort, inflight entertainment, onboard service, leg room, baggage handling, check-in service, inflight service and cleanliness. It also includes departure and arrival delay information. These variables provide the basis for exploratory analysis, predictive modelling and visual communication of passenger satisfaction patterns. The assessment requires the development of a data-driven solution using Python and relevant Python libraries. Students must follow an established analytical methodology such as PPDAC or CRISP-DM and demonstrate an Extract, Transform and Load process, data preparation, exploratory data analysis and appropriate visualisations. Two analytical models must be selected, trained and tested to predict passenger satisfaction. The available modelling approaches include Logistic Regression, Naive Bayes, Decision Tree, Bagging, Random Forest, AdaBoost, XGBoost, Artificial Neural Networks or another appropriate state-of-the-art algorithm. The second task requires critical analysis of the two selected models, including their strengths and limitations, an explanation of the chosen loss function, discussion of accuracy metrics and a comparison table of model performance. The third task focuses on communicating findings through data visualisation, including outputs such as correlation matrices, heat maps and confusion matrices. The analysis should explain how exploratory data analysis guided model selection and how visualisation techniques communicate insights effectively. The final report should demonstrate the ability to formulate data-driven solutions, critically evaluate analytical models and appraise data visualisation techniques. The assessment also requires independent research, appropriate academic referencing and supporting evidence from the analytical process. A pre-run Python notebook must be embedded in the MS Word submission or provided through an appropriate shared link.
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Advanced Artificial Intelligence
CW1: Applying advanced AI methods for analysing text documents
This coursework is an individual practical assessment for the Advanced Artificial Intelligence module, focusing on the application of advanced artificial intelligence, natural language processing (NLP), natural language understanding (NLU), and neural network techniques to analyse text documents. The coursework uses a provided social-media dataset containing more than 89,000 posts associated with 947 news headlines. Each social media post is treated as an individual text document and is labelled as either real or fake according to the relationship between the linked news headline, its ground-truth status, and majority annotator agreement. The assessment consists of two main tasks. The first task focuses on the identification of fake text documents. Students are required to perform descriptive data analysis and NLP preprocessing before transforming text into numerical feature representations suitable for neural network classification. Students must design and implement both a Multi-Layer Perceptron (MLP) network and a deep learning neural network to classify documents as real or fake. The assessment requires justification of the selected architectures, including inputs, layers, neurons, activation functions and outputs. Students must also experiment with hyperparameters, evaluate model performance using appropriate metrics, compare different models, select suitable models, and save trained models for later demonstration. The second task focuses on topic discovery using natural language understanding techniques. Students are required to apply text-processing methods such as tokenisation, stop-word removal, lemmatisation or stemming and experiment with at least two different text representation strategies. Possible approaches include Bag of Words, TF-IDF, Latent Dirichlet Allocation, word vectors and word embeddings. The discovered topics must be analysed and interpreted in relation to the document content, associated news headlines, and class labels. The assessment is supported by a practical bench demonstration and a maximum of seven PowerPoint slides covering the design, model improvement process, performance evaluation and discussion of results. Students must submit their own Python code and presentation through Blackboard and demonstrate their saved models without retraining them during the demonstration. The marking scheme allocates 45% to fake text document identification, 35% to topic discovery using NLU, and 20% to the structure, organisation, professionalism and question-and-answer performance of the demonstration.
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Calculus and Optimisation: Python Implementation of Gradient Descent, Derivatives and Polynomial Regression
This Calculus and Optimisation technical exercise demonstrates the implementation of mathematical and machine-learning concepts using Python. The document is organised as a set of code fragments that must logically work together to perform symbolic calculus, numerical optimisation, physical modelling and polynomial regression. The material uses libraries including NumPy, SymPy, Matplotlib and Scikit-learn, linking mathematical theory with practical computational implementation. Jigsaw_Puzzle_Calculus_Original… A central component is the implementation of gradient descent for linear regression. The supplied function calculates predictions, evaluates prediction errors, computes gradients and updates model parameters iteratively using a configurable learning rate. Mean squared error is recorded during optimisation, and a tolerance-based stopping condition is used to terminate the algorithm when successive cost values change by only a very small amount. Jigsaw_Puzzle_Calculus_Original… Synthetic linear-regression data is generated using a fixed NumPy random seed to support reproducible experimentation. A bias column is added to the feature matrix, initial parameter values are randomly generated, and the custom gradient-descent function is then executed to estimate the intercept and slope of the relationship. Jigsaw_Puzzle_Calculus_Original… The document also demonstrates symbolic differentiation and optimisation using SymPy. A cubic polynomial is defined and differentiated to obtain both its first and second derivatives. Critical points are identified by solving where the first derivative equals zero, while the second derivative is evaluated at each critical point to determine whether the point represents a local minimum, local maximum or saddle point. Jigsaw_Puzzle_Calculus_Original… Jigsaw_Puzzle_Calculus_Original… A further section applies mathematical formulas to projectile motion. Using a specified initial velocity, launch angle and gravitational acceleration, the code calculates both maximum projectile height and horizontal range. This component illustrates how calculus-related mathematical relationships can be translated directly into executable computational models. Jigsaw_Puzzle_Calculus_Original… The final major element explores polynomial regression. Synthetic nonlinear data is generated from a cosine-based function with added random noise. Scikit-learn pipelines are then used to compare polynomial models of degrees 1, 4 and 15. The models are fitted to the synthetic dataset and visualised against the underlying true function, allowing comparison of model complexity and illustrating concepts such as underfitting and overfitting. Jigsaw_Puzzle_Calculus_Original… Jigsaw_Puzzle_Calculus_Original… Overall, the exercise integrates calculus, optimisation, numerical methods and machine-learning modelling through Python. It provides practical experience with differentiation, critical-point analysis, iterative optimisation, mathematical simulation, regression modelling and visualisation. Important: because this file does not identify a university, assessment weighting, academic level, reference style or required word count, those fields should remain Not specified / Not applicable rather than being invented.
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Artificial Intelligence / Natural Language Processing / Deep Learning
Applying Advanced AI Methods for Analysing Text Documents
This Advanced Artificial Intelligence coursework requires students to implement and evaluate natural language processing, natural language understanding and neural-network techniques for analysing text documents. The assessment uses a supplied social-media dataset containing more than 89,000 posts linked to 947 news headlines, with each post labelled as real or fake through a combination of headline ground truth and majority-vote annotation. Each social-media post is treated as an individual text document for classification and topic-analysis purposes. CMP_6059B_7059B_2025_26_CW1-pre… The first major task focuses on identifying fake text documents. Students must preprocess the text using appropriate NLP techniques, transform documents into numerical feature representations and experiment with alternative preprocessing approaches to determine which performs best. A separate unseen test set must be reserved to evaluate generalisation, while the remaining data is used for training both shallow and deep neural-network classifiers. Students are expected to explain and justify how the data is split. CMP_6059B_7059B_2025_26_CW1-pre… Students first design a multi-layer perceptron (MLP) capable of predicting whether documents are real or fake. The architecture must be justified in terms of input dimensions, number of layers, neuron counts, activation functions and outputs. A second deep-learning neural network must then be developed for the same classification problem, with justification of the chosen network structure, layer types, activations and other configuration decisions. CMP_6059B_7059B_2025_26_CW1-pre… For both networks, students train baseline models and select three hyperparameters considered most important for improving performance. These hyperparameters must be tuned systematically, with visuals prepared to show the experimentation process and resulting performance changes. Appropriate evaluation metrics are then used to compare the trained models. The strongest MLP and deep-learning models must be saved so that they can be loaded and tested on unseen data during the final demonstration without retraining. CMP_6059B_7059B_2025_26_CW1-pre… The second task focuses on topic discovery using NLP and NLU techniques. Students perform syntactic preprocessing such as tokenisation, stop-word removal and lemmatisation or stemming, and experiment with at least two different text-representation approaches. Suggested methods include Bag of Words, TF-IDF, LDA, word vectors and word embeddings. Students must interpret the discovered topics and explain how those topics relate to document content, linked news headlines and class labels. The best topic-discovery model or models must also be saved for live analysis during the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… The assessment is completed through a bench demonstration, supported by a maximum of seven PowerPoint slides. The slides should document the system design, model-improvement process, performance evaluation and discussion of results for both fake-document classification and topic discovery. Students also submit a ZIP file containing only their Python source files. The demonstration lasts up to 15 minutes, consisting of approximately 10 minutes for presentation and technical demonstration followed by 5 minutes for questions and transitions. CMP_6059B_7059B_2025_26_CW1-pre… The marking scheme allocates 45% to fake-document identification, including descriptive analysis, preprocessing, MLP design and deep-learning design; 35% to topic discovery, including preprocessing, model development and interpretation; and 20% to the structure, organisation, professionalism and Q&A quality of the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… Important for the public Reference Library: the brief explicitly states that the use of Large Language Models or generative AI to produce any part of the submission is strictly prohibited, including code, data processing, testing, writing or PowerPoint content. Therefore, this entry should remain only a high-level public description of the assessment and should not be presented as material intended for direct student submission. CMP_6059B_7059B_2025_26_CW1-pre…
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Probability and Statistics / Data Analysis / Python
Probability and Statistics Jigsaw Puzzle: Probability Calculations and Chi-Square Analysis in Python
This technical Probability and Statistics exercise presents a set of Python code fragments designed to demonstrate basic probability calculations and statistical hypothesis testing. The material is organised as a jigsaw-style collection of code segments, allowing the underlying logical sequence to be reconstructed from variable definitions, probability calculations, observed data, statistical testing and final interpretation. Jigsaw_Puzzle_3_Original_Struct… The first part models a two-bag probability problem. Bag 1 contains four white and two black items, while Bag 2 contains three white and five black items. The code calculates the probabilities of selecting a white or black item from each bag and then uses multiplication rules to determine the probability of drawing two white items, two black items, or one white and one black item across the two bags. Jigsaw_Puzzle_3_Original_Struct… Jigsaw_Puzzle_3_Original_Struct… Jigsaw_Puzzle_3_Original_Struct… A second section demonstrates a chi-square test of independence using an observed frequency table containing grade outcomes for girls and boys across grades A, B, C and D. The data are stored in a NumPy array and analysed using SciPy's chi2_contingency function. The resulting chi-square statistic, p-value, degrees of freedom and expected frequencies are calculated and displayed. Jigsaw_Puzzle_3_Original_Struct… Jigsaw_Puzzle_3_Original_Struct… The final fragment applies a conventional significance threshold of α = 0.05. If the p-value is greater than 0.05, the code reports that the null hypothesis should not be rejected; otherwise, it concludes that a statistically significant difference exists. Jigsaw_Puzzle_3_Original_Struct… Overall, the document demonstrates core statistical-programming concepts including probability rules, contingency tables, chi-square analysis, expected frequencies, p-value interpretation and hypothesis testing using Python, NumPy and SciPy. Important: because this file does not identify a university, module, academic level, academic year, assignment brief, formal word count or referencing system, I would leave those fields as Not specified rather than guessing.
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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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Cybersecurity / Security Operations
Catnip Games International: SOC Automation and Incident Response Platform
This cybersecurity project presents the design and implementation of a prototype Security Operations Centre (SOC) automation and incident-response platform for Catnip Games International. The scenario addresses security challenges affecting a gaming organisation operating more than 300 Linux servers across two data centres, including credential-stuffing bot attacks, compromised player accounts, phishing campaigns and delayed coordination during security incidents. Catnip_Games_SOC_Complete Catnip_Games_SOC_Complete The proposed solution integrates TheHive 5, Cortex 3, Elasticsearch, Cassandra and Python-based automation, with MISP explored for threat-intelligence integration. TheHive functions as the central incident and case-management platform, Cortex provides automated observable analysis, Elasticsearch supports search and log storage, Cassandra provides persistent case and alert storage, and Python scripts automate alert ingestion and workflow activities through REST APIs. Catnip_Games_SOC_Complete Catnip_Games_SOC_Complete Implementation includes environment configuration, Docker deployment, API integration, automated alert generation, incident-response playbooks, KPI monitoring and backup procedures. Three attack scenarios are modelled: bot attacks, account takeover and phishing. Alerts are automatically ingested into TheHive, converted into cases and processed through analyst triage, investigation and resolution workflows. Catnip_Games_SOC_Complete Catnip_Games_SOC_Complete The project also develops structured response playbooks covering triage, containment, investigation, recovery and post-incident actions for each security scenario. Operational metrics are visualised through a KPI dashboard measuring alert volumes, response times, Mean Time to Detect (MTTD), Mean Time to Respond/Resolve (MTTR) and platform availability. Catnip_Games_SOC_Complete Catnip_Games_SOC_Complete Overall, the work demonstrates practical application of SOC architecture, security automation, incident management, threat analysis, containerised infrastructure, API-based integration, operational metrics and cyber-response procedures within a realistic gaming-industry security scenario. Catnip_Games_SOC_Complete Overview word count: approximately 330 words. Important before putting the presentation on a public Reference Library: redact any API keys/authentication tokens and other live credentials shown in the technical slides. The presentation includes API-key material in the Cortex and Python automation sections, so those credentials should also be revoked/rotated if they were ever active. Catnip_Games_SOC_Complete Catnip_Games_SOC_Complete
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Computer Networks / Network Security / Cloud and Software Defined Networking
3,500 words
Network Systems and Security: Ad Hoc, Cloud and Software Defined Networking Emulation
This Network Systems and Security coursework requires students to design, implement and critically evaluate a series of practical network-emulation environments covering wireless Ad Hoc networking, cloud services and Software Defined Networking (SDN). The project combines Python-based network configuration with practical connectivity testing, cloud deployment, controller-based networking and theoretical evaluation of contemporary network-security technologies. The first task involves designing an Ad Hoc wireless network representing an emergency-response scenario. Students configure at least three wireless stations using Mininet-WiFi, assign appropriate network parameters and demonstrate connectivity through ICMP communication. The task requires discussion of the design and implementation together with the Python script used to configure the emulated environment. 7COM1076+ref+def+CW+2025+26+ The second task focuses on cloud-service emulation. Students develop a simple static website, deploy it through Render.com and GitHub, and access the hosted service from a Mininet-emulated network. Required evidence includes the Python implementation, cloud configuration screenshots, commands used to provide internet connectivity, webpage access through an xterm environment and the associated HTML code. 7COM1076+ref+def+CW+2025+26+ The third practical component examines Software Defined Networking using the ONOS controller. Students construct an emulated topology containing hosts, servers and programmable switches, demonstrate complete ICMP connectivity and perform a TCP transmission lasting 600 seconds. Evidence must include the network-emulation script, ONOS graphical interface and connectivity results. 7COM1076+ref+def+CW+2025+26+ The final analytical section critically evaluates whether Software Defined Networking and Network Functions Virtualisation (NFV) complement one another and examines security algorithms used within cloud computing. Students compare two selected cryptographic approaches, evaluating their respective advantages and disadvantages. 7COM1076+ref+def+CW+2025+26+ Overall, the coursework integrates network modelling, wireless networking, cloud deployment, SDN control, Python scripting, connectivity testing and security analysis. The marking scheme gives substantial weight to system modelling, cloud and SDN implementation, ICMP/TCP functionality, technical analysis and overall report quality. 7COM1076+ref+def+CW+2025+26+
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Artificial Intelligence / Machine Vision / Computer Vision
Artificial Intelligence and Machine Vision: Neural Network-Based Image Processing Application
This postgraduate Artificial Intelligence and Machine Vision coursework requires students to design, implement and critically evaluate a neural network-based image-processing application addressing a real-world problem. Students may select an application such as medical imaging, plant or fruit classification, skin cancer detection, object detection or image segmentation and must demonstrate an appropriate end-to-end machine vision workflow. CN7023 Coursework T2 25-26 (1) The project begins with a clear definition of the selected real-world problem, the objectives of the proposed solution and its potential practical impact. Students are expected to demonstrate creativity in selecting and designing their approach, explain the neural-network or image-processing methods adopted and justify why the selected techniques are suitable for the chosen dataset and application. CN7023 Coursework T2 25-26 (1) A substantial component focuses on simulation and implementation. Students describe the dataset, including its source, size, classes and representative images, before explaining how image data were encoded and preprocessed for use within the chosen neural network. The report must then document the selected network architecture, learning algorithm and procedures used for training, validation and testing. CN7023 Coursework T2 25-26 (1) Model performance must be communicated using quantitative and visual evidence. Required outputs include test-set accuracy, accuracy curves across training, validation and testing, and a confusion matrix supported by appropriate explanation. Students must also critically analyse the results, identify factors affecting model performance and discuss alternative methods or simulation changes that could improve the solution. CN7023 Coursework T2 25-26 (1) The coursework permits several technical routes, including combining image processing with artificial neural networks, deep learning or computer vision, or focusing on one of these approaches independently. Development may be completed using MATLAB or Python. The wider module covers artificial neural networks, CNNs, digital image processing, image restoration, compression, segmentation, classification and ethical, legal, privacy and social issues associated with AI systems. CN7023 Coursework T2 25-26 (1) Module handbook 2526-B (1) Overview word count: approximately 335 words. Important note: the coursework cover page labels the assignment as “Individual Assignment 100%,” but the module handbook clarifies that the coursework report itself contributes 50% of the module, with the remaining marks allocated to MATLAB course completion (20%), lab tasks (15%) and presentation (15%). For the Reference Library, I would use the handbook’s 50% report weighting if you need to record the assessment contribution.
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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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7COM1076
4,000 words
7COM1076 Network Design, Modelling and Evaluation Coursework
This 7COM1076 coursework requires students to design, model, emulate, test and evaluate networking environments for a new department building at the University of Hertfordshire. The assessment combines practical network modelling with theoretical analysis and evaluation, covering wireless and mobile networking, cloud networking, Software Defined Networking (SDN), network applications and contemporary networking technologies. The assignment is designed to develop practical knowledge through Python-based network emulation and critical understanding through academic research. Task 1 focuses on wireless and mobile networking. Students must use Python to emulate a building containing three WiFi access points and two user stations, identified as UE1 and UE2. The access points are connected using a physical linear topology, while the stations use Class C private IP addresses. The task requires configuration of SSIDs, passwords, channels, ranges and coordinates, with WPA2 encryption and standalone fail mode. The stations must also be configured with mobility, following specified movement sequences and speed ranges. Students must discuss the design and implementation, complete the configuration and mobility tables, provide the Python script used with the Mininet API, and include screenshots demonstrating mobility, access-point association and successful ping connectivity. Task 2 examines cloud services using Mininet and Render.com. Students must emulate a network containing three switches and two hosts and deploy a simple static website using Render.com. Host H1 must access the deployed webpage through the xterm environment. Deliverables include a discussion of the design and implementation, the Python script, evidence of configuring Render.com with the GitHub repository, commands used to connect H1 to the internet and access the webpage, screenshots of the webpage through xterm, and the HTML code of the website. Task 3 focuses on Software Defined Networking and connectivity. Students must emulate an environment containing ten hosts and three servers using a linear topology, with an ONOS controller enabled for control-plane programmability. The task requires configuration of assigned IP and MAC addresses, demonstration of connectivity between hosts and servers, and a UDP transmission using a duration of 600 seconds and bandwidth of 100 Mbps. Students must provide a design discussion, Python emulation script, ONOS GUI screenshot, full ping connectivity evidence and the required UDP transmission results. Task 4 is the analysis and evaluation component. Students must critically discuss three contemporary networking issues using academic sources. These include the challenges faced by a small-to-medium organisation providing services to the UK National Health Service when migrating from on-premises infrastructure to cloud computing; the opportunities and challenges of incorporating Software Defined Networking into future optical networks; and whether WiFi and 5G should coexist to provide ubiquitous services to users. The final report must use 12-point font, normal margins and Harvard referencing in accordance with University of Hertfordshire guidelines. The required report length is 4,000 words with a permitted variation of plus or minus 10%, excluding the title page, contents page, references and appendix, with a maximum page limit of 25 pages. The recommended structure includes an introduction, discussions and results for Tasks 1–3, the three Task 4 analysis sections, conclusion, references and an appendix containing the Task 1, Task 2 and Task 3 code. The assessment covers wireless and mobile networking, cloud and SDN networking, network applications, analysis and evaluation, MCQ tests and overall report quality. The marking allocation includes WiFi networking, mobility and ICMP, cloud configuration, SDN networking, cloud web page, UDP, three analysis sections, two MCQ tests and quality of the report.
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Programming for Data Science
2,500 words
Individual Coursework – Programming for Data Science
This Individual Coursework assignment for the Programming for Data Science module requires students to demonstrate practical programming skills and the ability to critically select and apply Python data science tools and libraries. The assessment is worth 20 credits and has a word count of 2,500 words plus 10%, excluding the reference list and output. Students are required to submit one clearly organised report covering two tasks and their individual subtasks. Task 1 focuses on designing, building, testing, explaining, adapting and critiquing a Python program. Students are required to implement a dice-based football match simulation for two players using at least two Python functions. The implementation should follow the logic of the physical dice game, demonstrate good Python coding style, and use functions designed with high cohesion and low coupling. The report must include the full Python code and carefully selected output from a sample match that demonstrates the progression of the game and changes in the score. Students must not implement a Python class or graphical visualisation. The second part of Task 1 requires students to explain how they designed their implementation, including assumptions, incremental development and testing. They must then modify their program to estimate at least two performance measures relevant to a football manager. The coursework asks students to investigate how these measures change when a restrictive shot clock is introduced and to model a realistic “Hail Mary” shot when the shot clock is close to expiring. Students must provide modified Python code, clearly identify the changes made, include selected output for checking the logic, and provide a robust conclusion based on comparison of the results. Task 2 focuses on critically assessing, selecting and applying Python data science libraries and algorithms. Students must describe an applied data science problem involving unstructured data such as image, audio, video or text. They must provide a specific example based on the context of a Coventry University fresher and explain how the problem could be solved manually. Students then select two Python libraries, justify their selection, compare their capabilities, apply both libraries to the chosen problem, and provide the relevant Python code and output. The final part of Task 2 requires a critical assessment of the selected Python libraries using the student's experience and additional sources. Factors may include coding difficulty, adaptability, level of control and quality of the resulting solution. Students must make a reasoned assessment of the suitability of the libraries for their chosen application and support their discussion with appropriate references. The assignment assesses three module learning outcomes: understanding essential programming concepts relevant to data science; designing, building, testing, explaining, adapting and critiquing small programs in a high-level programming language; and critically assessing, selecting and applying data science tools, libraries or algorithms for different applications and tasks. The submission must be provided as a single Microsoft Word or PDF report, organised by subtask, with each task starting on a new page. Python code, relevant output and plots must be included directly in the report. The brief requires APA referencing and states that sources should be cited in-text with a reference list for each task where relevant.
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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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Data Management / Business Analytics
2,500 words
Data and Decision Making (BS776) — Business Report: Two-Source Data Analysis in Python for Evidence-Based Decision-Making
This Level 7 report applies data management theory to a self-selected industry problem and carries it through to a working Python analysis and a defensible business recommendation. The brief is deliberately open on sector — finance, healthcare, transport, cyber security, business intelligence and others are all permitted — but firm on one point: the chosen topic must carry a genuine business implication rather than being a purely technical or clinical analysis. The work therefore begins by framing a specific data-driven decision the organisation needs to make, and returns to that decision at every stage. Two distinct data sources are then identified from approved open repositories and critically evaluated side by side. The evaluation covers the data types each holds, how the data was collected and what bias that introduces, how each is stored and managed, and where the weaknesses lie — proposing concrete data management solutions for the problems identified rather than simply cataloguing them. The analytical core examines, transforms and explores both datasets using univariate and multivariate techniques. All work is carried out in Python within Google Colab, with full screenshots of the code and outputs placed in the appendices and the live Colab link shared for verification. Charts and tables sit in the main body where they support interpretation, each labelled and referenced back to its data source, and each appendix is cited from the narrative so the reader can move between argument and evidence. Data cleaning and transformation steps are shown and justified, not glossed. Findings are reported at length and converted into a clear recommendation covering both the immediate decision and the current and future direction of data management for the business. The limitations section is written honestly — sample coverage, data recency, the assumptions the transformation forced, and what the proposed solution cannot address. Running alongside this, the module's weekly consolidation discussions are evidenced. Five or more critical responses across units two to nine are screenshotted, dated, individually labelled as appendices, and each supported by academic and practice references. Crucially, these are not left sitting in the appendix: they are cited and used within the main body to support the critical discussion, which is where the marks for that component sit. The report follows the prescribed structure — title page, executive summary, contents, introduction, main section with subsections per task, findings, recommendations, limitations, conclusion, Harvard reference list and full appendices — submitted as a single file.
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Machine Learning and Deep Learning
2,000 words
Development and Evaluation of Deep Learning Models for Healthcare Classification
This individual technical assessment focuses on the design, development, analysis and evaluation of a deep learning solution for a healthcare-related classification problem. Students select one of two provided scenarios: Polycystic Ovary Syndrome (PCOS) detection using ultrasound images or heartbeat classification using electrocardiogram (ECG) signals. The objective is to develop an appropriate deep learning approach and demonstrate critical understanding of the complete machine learning workflow, from initial data exploration through to model evaluation and reflection. Students may either design and train a deep learning model from scratch or customise and fine-tune an existing pre-trained architecture. The complete work is presented through a single Jupyter Notebook integrating Python code, technical discussion, results and visualisations. The notebook must clearly define the selected healthcare problem, explain its significance, justify methodological and architectural choices, and critically evaluate the resulting solution. The first stage involves exploratory data analysis and preprocessing, including investigation of class distributions, data imbalance and relevant patterns. Students prepare the data through techniques such as normalisation, augmentation, train-validation-test splitting and appropriate handling of class imbalance. This is followed by model design, training, validation and hyperparameter tuning, with the architecture selected according to the characteristics of the data and classification task. Model performance must then be evaluated using appropriate classification measures, including precision, recall, F1-score, ROC curves and area under the curve (AUC). The developed model should also be compared against suitable benchmark approaches, which may include traditional machine learning algorithms or alternative deep learning architectures. This comparison should identify the relative strengths and limitations of the proposed solution. The final component requires clear visual presentation and critical reflection on the complete modelling process, including limitations, challenges and opportunities for improvement. Importantly, grading prioritises methodological rigour, analytical depth and critical evaluation rather than simply achieving the highest predictive accuracy. Overview word count: approximately 330 words. AI restriction: this brief only permits automated AI tools for spelling and grammar checking. It explicitly prohibits tools such as ChatGPT, Gemini or Copilot from authoring assessment text or code; any permitted AI use must also be acknowledged.
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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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Computing and Digital Technologies
3,000 words
Contemporary Computing and Digital Technologies: AI Agents Hackathon Reflective Report
This postgraduate reflective assessment forms part of the Contemporary Computing and Digital Technologies module and is based on experiential learning undertaken through an AI-focused hackathon. The hackathon theme, “AI Agents Unleashed – Building the Future of Automation,” requires MSc students from computing-related disciplines to collaborate on intelligent agent-based solutions capable of automating complex tasks, solving real-world problems and supporting human decision-making. The hackathon encourages students to investigate agent-based system design, intelligent automation and responsible AI development. Potential applications include autonomous cybersecurity monitoring, multi-agent systems for information gathering and decision support, automated data pipelines, intelligent software-development assistants, and conversational systems such as virtual tutors or career coaches. Students may use code-based approaches or platforms such as Flowise, Microsoft Power Automate and Make.com, while more advanced implementations can use technologies including LangChain, AutoGen, Python-based agent SDKs, APIs and large language models. The 3,000-word Individual Reflective Report, worth 70% of the module assessment, evaluates the student's learning and professional development arising from these experiential activities. The first component is a 2,000-word Portfolio of Evidence, requiring evidence-based reflection on participation in the hackathon. Students should evaluate their leadership and teamwork competencies using concrete evidence such as screenshots, code commits and feedback while identifying key lessons for personal and professional development. They must also consider how the experience applies to future research, career development or professional practice. The remaining 1,000 words comprise a Critical Self-Reflection examining the student's personal contribution and achievement of learning-contract goals. Students are expected to critically consider challenges encountered, how those challenges were addressed, lessons learned and their development as effective collaborative team members. Overall, the assessment integrates technical experimentation, reflective practice, teamwork, leadership, professional development and responsible use of emerging AI technologies, supported by a structured portfolio of evidence
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Machine Learning / Cloud Computing
3,984 words
Machine Learning on Cloud: Fraud Detection Model Design, Training and Evaluation
This postgraduate group project focuses on the design, development and critical evaluation of a cloud-based machine learning solution for financial fraud detection. The scenario involves a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and improve customer security. Students are required to analyse an appropriate dataset, develop a machine learning solution, evaluate its effectiveness and critically consider the suitability of cloud technologies for deployment. The project begins with a cloud feasibility study, requiring critical comparison of at least two major machine learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Evaluation criteria include performance, scalability, cost, compliance, integration and vendor lock-in, followed by a justified recommendation. Students then conduct exploratory data analysis to identify patterns, anomalies and correlations using appropriate visualisations such as heatmaps, histograms and boxplots. A substantial component addresses data preprocessing and class imbalance. Students are expected to clean and transform the data, apply scaling and encoding, perform feature engineering and investigate approaches such as SMOTE, undersampling and cost-sensitive learning. Each preprocessing choice must be justified in terms of its potential impact on model performance. Students must select and train at least two machine learning models, with suggested approaches including Logistic Regression, Random Forest, XGBoost and Neural Networks. Model development incorporates cross-validation and hyperparameter tuning. Evaluation uses fraud-relevant measures including Precision, Recall, F1 score, AUC and precision-recall curves, supported by confusion matrices, ROC curves and feature-importance visualisations. The project concludes with critical consideration of professional and ethical issues in cloud-based AI, including bias, fairness, transparency, data privacy and sustainability. The overall assessment therefore integrates cloud-platform evaluation, machine learning development, imbalanced classification, model evaluation and responsible AI practice. Overview word count: approximately 340 words.
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Big Data Analytics / Data Analytics
4,000 words
Big Data Analytics Using Python and Business Intelligence with Tableau
This individual Big Data Analytics assessment requires students to critically analyse data using programming languages, statistical techniques, data visualisation methods and business intelligence software. The assessment combines practical data analytics using Python with interactive business intelligence and dashboard development using Tableau, requiring evidence of technical implementation, research, critical appraisal and justification of the selected analytical approaches. The first section, worth 70%, is based on a dataset containing accidental drug-related deaths recorded in Connecticut between 2012 and 2024. The dataset contains 12,964 observations and 49 variables. Students are required to conduct exploratory data analysis using Python, develop three to four research questions, formulate null and alternative hypotheses, and apply appropriate statistical methods. The analytical process also requires an evaluation of alternative technologies and methodological approaches, supported by relevant research. Students must justify their selected methodology and present a clear workflow diagram. The solution-development stage involves data preprocessing, descriptive statistical analysis, visualisation, answering the research questions and conducting statistical significance testing to determine whether the null hypothesis should be accepted or rejected. Evidence of coding and a link to working code are also required. The final Python component requires evaluation of the findings, consideration of limitations and recommendations for future development using emerging technologies. The second section, worth 30%, focuses on Business Intelligence using Tableau and uses a historical Olympic Games dataset containing 271,116 rows and 15 columns. Students analyse relationships between medals and host cities, athlete age and medal type, season and medal counts, and sex and medal type. The section culminates in an interactive Tableau dashboard containing at least four interconnected sheets, where changes to relevant parameters are reflected across the dashboard. Overall, the assessment develops practical competence in Python-based analytics, statistical reasoning, research-question development, hypothesis testing, data visualisation and interactive business intelligence dashboard design. Overview word count: approximately 350 words. AI note: the brief permits AI for limited support such as grammar, structure, organisation of ideas and suggestions, but states that the main content, analysis and conclusions must remain the student's own work. AI use must also be declared.
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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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Computer Networks and Network Engineering
3,500 words
Wireless, Cloud and Software Defined Networking: Network Modelling, Emulation and Evaluation
This technical networking assignment focuses on the design, implementation, emulation and evaluation of wireless, cloud and Software Defined Networking environments. It combines practical network modelling with analytical discussion and requires students to demonstrate their understanding of modern networking architectures through Mininet-WiFi, Mininet, cloud deployment technologies and an ONOS Software Defined Networking controller. The first task involves creating an ad-hoc wireless network for an emergency-response scenario. A minimum of three wireless stations must be configured using specified parameters such as transmission range, antenna height, antenna gain, SSID and wireless capabilities. Students are required to explain the network design and implementation, provide the Python configuration script used with Mininet-WiFi, and demonstrate connectivity using ICMP communication between appropriate stations. The second task examines cloud-service emulation. Students must construct a Mininet topology containing a switch and two hosts and deploy a simple static website using Render.com. The task requires evidence of cloud configuration, GitHub repository integration, commands used to provide internet access to the emulated host, webpage access through Xterm, screenshots of the resulting webpage and the associated HTML code. The third task addresses Software Defined Networking (SDN). Students create an emulated environment involving three hosts and three servers, implement the required network topology and use the ONOS controller to provide control-plane programmability. Evidence must include the Python emulation script, ONOS GUI output, host-to-server connectivity testing and TCP transmission testing. The final analytical component requires students to critically evaluate the relationship between Software Defined Networking and Network Functions Virtualisation (NFV) and assess security algorithms used in cloud computing, including a comparison of the advantages and disadvantages of two selected algorithms. The coursework therefore integrates practical network configuration, connectivity testing, cloud deployment, programmable networking and academically referenced technical evaluation. Overview word count: approximately 335 words. Important: the brief explicitly states that AI-generated report or code content is prohibited, so this Reference Library description should be treated only as catalogue/metadata content, not as coursework material for submission.
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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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Computer Science / Machine Learning
Machine Learning Portfolio Challenge: Support Vector Machines and Kernel Methods
This machine learning portfolio challenge requires students to demonstrate both theoretical understanding and practical application of machine learning methodologies introduced during the second block of the module. During Weeks 7–12, students select one of the machine learning methods covered in class and apply it to a dataset of their choice. The completed work contributes to an assessment portfolio and must demonstrate a clear, systematic and professionally documented experimental process. The accompanying learning material places particular emphasis on Support Vector Machines (SVMs) and kernel-based machine learning. SVMs are presented as maximum-margin classifiers that construct a decision hyperplane between classes, with support vectors playing the key role in defining the classification boundary. The material also introduces soft-margin optimisation, slack variables, the regularisation parameter C, primal and dual formulations, and the use of KKT multipliers. SVMs are additionally discussed in relation to multiclass classification through approaches including one-versus-all and Error Correcting Output Codes (ECOC). Kernel methods extend these principles by replacing explicit feature transformations with similarity functions. Students encounter concepts including Gram matrices, feature mappings, Mercer conditions and the kernel trick, alongside common kernel choices such as linear, radial basis function and polynomial kernels. The material demonstrates how kernel methods can represent nonlinear decision boundaries in the original input space while retaining a linear representation in an embedded feature space. The final submission must be produced as a single PDF lab notebook. It should contain clear and well-commented MATLAB, Python or equivalent code explaining each methodological step, experimental results presented through appropriate tables and/or plots, and narrative discussion explaining the selected approach, observations and conclusions. The notebook should integrate code, outputs and written explanation into a coherent and professional submission. Students may also optionally present their solution in class, where the quality of explanation and discussion can contribute additional marks.
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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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