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Advanced Research Topics
Generative Adversarial Networks – Building and Applying GANs to Real-World Problems
This individual assessment focuses on building and understanding Generative Adversarial Networks (GANs) and applying them to real-world problems across different domains. The assignment is based on the GAN topics covered in Unit 5 and requires students to implement GAN models, analyse their behaviour, generate synthetic data and critically evaluate the quality of the generated outputs. The assessment consists of two main parts: building and understanding GANs from scratch using synthetic two-dimensional data, followed by applying GANs to three real-world application areas involving medicine, cybersecurity and creative artificial intelligence. Part 1 focuses on developing a practical understanding of GANs through implementation using PyTorch and synthetic 2D data. Students must first reproduce the sine-wave GAN demonstrated in the tutorial. They then create and model a new two-dimensional distribution, selecting from a 2D spiral, a mixture of Gaussians, or a noisy parametric curve defined as y = sin(2x) + 0.3cos(5x) + epsilon. Students must modify the GAN architecture, for example by changing the activation function or layer depth, and visually compare the original data distribution with the generated samples. This section is intended to reinforce understanding of the generator, discriminator, GAN training process and the effect of architectural choices on generated data. Part 2 addresses three real-world GAN applications. The first application concerns optical coherence tomography (OCT) retinal images using the OCTMNIST subset of MedMNIST. Students must explore the dataset, examine class distributions and sample images, build a ConvNet-based Deep Convolutional GAN (DCGAN) using PyTorch or TensorFlow/Keras, train the model and track generator and discriminator losses. Generated retinal images must be compared with real images both visually and quantitatively, using a performance measure such as the Fréchet Inception Distance (FID). An optional extension involves implementing a conditional GAN and demonstrating generation of images for specific classes. The second application focuses on cybersecurity using the preprocessed CICIDS2017 dataset containing DDoS and benign network traffic. Students must combine the relevant data files, explore the dataset and understand its features and class balance. A GAN must be developed to generate synthetic feature vectors rather than images. The model should be trained using benign and DDoS attack data, training loss curves should be monitored, and PCA or t-SNE should be used to visualise and compare real and generated feature distributions. The quality of the synthetic data should then be evaluated. An optional extension involves expanding the analysis to the full CICIDS2017 dataset and examining generalisation across different attack types. The third application focuses on Creative AI using the QuickDraw birthday cake category. Students must explore the birthday cake sketch dataset and implement a ConvNet-based GAN (DCGAN) to generate realistic birthday cake sketches. Generated sketches should be evaluated visually and quantitatively, including comparison with real examples and an appropriate metric such as FID. An optional extension involves generating samples from additional QuickDraw categories and discussing how model performance changes across different classes and levels of sketch complexity. The submission consists of both code and a written report. The code accounts for 60% of the assessment and must complete the required modelling tasks, present generated samples, compare generated and real data, use appropriate functions and include clear annotations so another user can understand the implementation. The report accounts for 40% and must be 6–8 pages. It should explain the analytical steps undertaken, justify the selected approaches, provide brief descriptions of the models used, interpret the results and include appropriate figures, evaluation metrics and academic references. The report should critically discuss why particular network architectures were selected rather than simply providing textbook definitions.
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Engineering and Environment Advanced Practice London Campus Consultancy Project
2,000 words
Individual Reflective Report – Group Consultancy Project
This assessment is an individual reflective report based on the student’s learning and experience during the Group Consultancy Project for the Engineering and Environment Advanced Practice London Campus Consultancy Project module at Northumbria University. The assessment requires students to critically reflect on their activities, contributions, skills development and professional learning throughout the group-based consultancy experience. The report has a total word limit of 2,000 words, excluding the table of contents, page numbers and captions for figures and tables. It is structured around two main components: a Progress Report of approximately 1,000 words and a Reflection of Learning and Development of approximately 1,000 words. The Progress Report brings together the main themes of the module from the perspective of additional skills development, engagement in self-development, group-based learning, cultural awareness and ethical awareness. Students are expected to discuss key personal activities undertaken during the group project, skills gained through participation, personal contributions to the project, and the development of interpersonal and intrapersonal skills. Academic literature can be used to support the discussion. The Reflection of Learning and Development section requires students to critically evaluate their personal strengths and weaknesses and demonstrate their ability to engage in continuous self-development within the context of group project work. Students should reflect on a range of project activities and provide examples of their involvement in decision-making, problem-solving, communication and influencing, technical skills, collaboration, innovation and proactivity. The assessment therefore focuses not only on describing what students did, but also on evaluating what they learned and how the experience contributed to their professional and personal development. The assessment is worth 50% of the total marks available for the module, which is assessed on a pass/fail basis. The assessment rubric evaluates the Progress Report at 50%, Reflection of Learning and Development at 40%, and Writing Style at 10%. Strong submissions are expected to provide clear and relevant examples, integrate activities with skills and contributions, demonstrate critical reflection on strengths and weaknesses, and present a well-structured and professional written report. Students are also expected to follow the university’s requirements regarding academic integrity and the responsible use of generative AI. The assessment guidance states that AI may assist with activities such as improving grammar, formatting structure, organising ideas and generating suggestions, but the main content, analysis and conclusions must remain the student’s own work. Students are required to declare their use of AI tools when submitting the assessment.
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Engineering and Environment Advanced Practice London Campus Consultancy Project
5,000 words
Group Consultancy Report – Engineering and Environment Advanced Practice
This assessment is a group consultancy project for the LD7119 Engineering and Environment Advanced Practice London Campus Consultancy Project module at Northumbria University. Students work in consultancy groups of four to five members and are assigned a live project in collaboration with an organisation and the university. The purpose of the assessment is to investigate an organisational issue or requirement and develop practical, evidence-based solutions that can support the organisation in implementing improvements. The main assessment consists of a 5,000-word Group Consultancy Report and a compulsory 10-minute client presentation. The report is expected to demonstrate a professional and commercially appropriate approach to consultancy work. It should provide a clear introduction to the project, establish the organisational context, and analyse the business requirements and needs of the client organisation. The report should then explain the research methodology used, including appropriate ethical considerations and the design of the practical work undertaken. A substantial part of the assessment focuses on research, discussion and findings. Students are expected to collect and analyse relevant evidence, present their findings clearly, and provide evidence of practical implementation and testing where applicable. The assessment therefore requires students to connect academic research and relevant theories with practical consultancy activities and organisational requirements. Evidence from diagnostic tools, feedback and other appropriate sources can be incorporated into the report to support the analysis and conclusions. The final part of the report focuses on recommendations and improvements. Students should develop practical and actionable recommendations that are relevant to the organisation and can contribute to the successful implementation of the proposed ideas. Recommendations should be supported by the research findings and should demonstrate an understanding of the organisation's requirements and potential implementation considerations. The assessment is worth 50% of the total marks available for the module, which is assessed on a pass/fail basis. The report has a 5,000-word limit, excluding the table of contents, page numbers and captions for figures and tables. The client presentation is compulsory, although it does not have marks directly allocated to it; its purpose is to help the client and supervisor understand the consultancy project. The assessment is evaluated across professional and commercial presentation and introduction, business and requirement analysis, research methodology including ethics and practical work design, research and findings including implementation and testing, and recommendations including improvements. Students are also required to acknowledge sources appropriately and complete the assessment declaration regarding their work and any use of generative AI. The assessment brief states that AI may assist with activities such as improving grammar, structure, organising ideas and providing suggestions, but the main content, analysis and conclusions must remain the student's own work.
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Leadership in a Digital Age
4,000 words
Leadership in a Digital Age: Leadership Strengths and Development Needs
This individual assessment for the Leadership in a Digital Age module requires students to produce a 4,000-word report that critically analyses their leadership strengths and development needs within the context of the digital age. The assessment places particular emphasis on critical reflection, self-awareness, contemporary leadership theory and continuous professional development. Students are expected to apply relevant leadership models and frameworks, use evidence from diagnostic assessments and connect their personal development to the skills and behaviours required of effective digital leaders. The report is structured into five main sections. The first section critically reviews contemporary leadership models and theories in relation to digital leadership. Students are expected to select two or three relevant leadership theories, explain their key principles and critically evaluate their relevance to digital leadership traits and characteristics. The assessment encourages the selection of contemporary approaches rather than outdated leadership theories and requires students to demonstrate clear links between theory and the demands of leading in digitally changing environments. The second section focuses on self-analysis. Students identify relevant digital leadership traits and conduct a thorough evaluation of their own leadership characteristics using a range of diagnostic tools. The assessment identifies tools covering temperament, workplace culture, motivation at work, emotional control and management skills, together with other available university diagnostic assessments. Students should report and interpret their results and use the findings to develop a personal SWOT analysis focused specifically on digital leadership strengths and areas requiring development. The third section examines leadership capabilities and behaviours when leading a hybrid, multi-generation team. Students should consider themselves working as a digital leader responsible for such a team, identify potential challenges and propose appropriate solutions. The discussion should evaluate how leadership capabilities and behaviours can be applied in practice or within a future role. Particular attention must be given to the ethical, social and legal responsibilities of digital leaders towards team members. The fourth section critically evaluates how artificial intelligence can support digital leadership and accelerate organisational digital transformation. Students should use a workplace example to demonstrate how emerging technologies such as machine learning, predictive analytics, intelligent automation or generative AI could improve operations, decision-making or innovation. The analysis should consider organisational benefits as well as the resources required to implement and sustain AI-driven transformation, including data infrastructure, skills and partnerships. The final section requires a Personal Development Plan based on the findings from the earlier analysis. Students should identify personal development objectives that demonstrate their ability to develop the competencies required of an effective digital leader. The plan should establish future leadership goals, learning activities, measurable success criteria and realistic timescales. Overall, the assessment requires students to integrate leadership theory, self-awareness, digital transformation, AI, team management and continuous professional development into a coherent critical analysis of their leadership development.
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Computational Algorithms and Paradigms
1,000 words
Computational Algorithm Analysis – Research Paper Algorithm
This individual coursework for the Computational Algorithms and Paradigms module requires students to thoroughly analyse a computational algorithm proposed in a research paper selected from the list of research papers provided on Canvas. The purpose of the assessment is to develop students' ability to understand, explain and critically evaluate computational algorithms presented in academic research. Students must first identify and describe the computational problem addressed by the selected research paper and clearly state the research questions investigated by the authors. They must then extract the main computational algorithm proposed in the paper and present it in pseudocode. The assessment also requires students to clearly identify the inputs required by the algorithm and the outputs produced by it. The coursework consists of six main analytical sections. The first section focuses on the computational problem and research questions addressed in the selected paper. Students are expected to provide an accurate description of the problem and explain the research questions that the proposed algorithm attempts to address. The second section requires the proposed computational algorithm to be represented using suitable pseudocode. The third section identifies and explains the algorithm's inputs and outputs. The fourth section requires students to explain the proposed algorithm using simple and understandable language. The explanation should demonstrate a clear understanding of how the algorithm operates rather than simply reproducing the description provided in the research paper. The fifth section focuses on analysing the time complexity of the proposed algorithm. Students should evaluate the computational cost of the algorithm and explain its time complexity appropriately. The final section requires a critical evaluation of the algorithm's strengths and weaknesses. Students should identify the advantages and limitations of the proposed approach and discuss potential improvements where appropriate. This section should demonstrate critical thinking about the effectiveness, efficiency and practical applicability of the algorithm. The coursework has a total word count requirement of 800–1,000 words. The inputs and outputs, pseudocode and time-complexity analysis sections are excluded from this word-count limit. The template requires approximately 250 words for the computational problem and research questions, approximately 250 words for the simple explanation of the algorithm, and approximately 300 words for the strengths and weaknesses evaluation. Students must report the word count for sections 1, 4 and 6 after completing the assignment. The submitted work must be original and is subject to plagiarism and collusion checks through Turnitin. The assessment brief also states that generative AI tools may be used for proofreading but are not permitted for creating the coursework content. No figures or images are permitted, and the coursework must be submitted using the provided Word template in DOC or DOCX format.
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Critical Analysis of Computational Algorithms: Research Paper Evaluation and Complexity Analysis
This postgraduate Computer Science coursework requires students to undertake a critical technical analysis of a computational algorithm presented in a prescribed academic research paper. Students select one paper from the available options and demonstrate that they understand both the research problem addressed by the authors and the algorithmic solution proposed. The assessment contributes 30% of the overall module grade and is completed individually. assignment The available research papers cover several algorithmic topics, including an improved Dijkstra shortest-path algorithm for sparse networks, a modified merge-sort approach for large-scale datasets, parallel merge sort with load balancing, and a Prim-based algorithm for hierarchical clustering. Students must extract the principal algorithm from their selected paper and explain its purpose, inputs, outputs and operating procedure. A major component involves identifying the research question and computational problem addressed by the selected study. Students then reproduce or extract the proposed algorithm in pseudocode form and clearly identify the information supplied to the algorithm and the outputs it generates. The algorithm must also be explained step by step using straightforward language so that its operation can be understood without relying exclusively on formal notation. The coursework further requires a detailed time-complexity analysis, demonstrating understanding of how computational requirements grow with input size and how the proposed technique compares with alternative or conventional approaches. Students must critically evaluate the algorithm’s strengths, weaknesses, performance characteristics and limitations, and suggest potential improvements where appropriate. The marking rubric gives substantial emphasis to five areas: identifying the computational problem and research questions, extracting the proposed algorithm, identifying inputs and outputs, explaining the algorithm clearly, analysing its time complexity, and critically evaluating its strengths and weaknesses. assignment The written submission must be 800–1,000 words, although the inputs/outputs, pseudocode and time-complexity sections are excluded from that limit. Figures and images are not permitted, and the work must be submitted using the prescribed coursework template in DOC/DOCX format. Overview word count: approximately 340 words. AI-use note: the guideline permits generative AI only for proofreading. AI tools are explicitly not permitted to create the assessed work itself. assignment
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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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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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Artificial Intelligence / International Business
3,000 words
AI Innovation Consultancy: Evaluating Artificial Intelligence Solutions for Business Problems
This individual consultancy assessment requires students to act as an AI Innovation Consultant and evaluate how artificial intelligence could address a significant real-world business problem. Students select one industry—such as healthcare, retail, FinTech, manufacturing or agriculture—and concentrate on a single clearly defined organisational challenge rather than comparing multiple sectors. Potential issues include long waiting times, high operating costs, fraud and risk, poor customer experience or inefficient supply chains. The report develops a practical AI solution by identifying suitable technologies such as machine learning, natural language processing or computer vision and explaining how they would operate within the chosen organisational context. Students are not required to build an AI system; instead, the emphasis is on demonstrating business-level technical understanding, critical thinking and the ability to assess whether the proposed technology can realistically integrate with existing organisational processes. The analysis considers the capabilities and limitations of AI, technical feasibility, integration requirements and the skills or organisational capabilities required for implementation. Students must also critically examine ethical, legal and social implications, including issues such as algorithmic bias, transparency, accountability, privacy and regulatory obligations such as UK GDPR. Appropriate risk-mitigation measures should be proposed. A substantial element of the report develops the business case for AI adoption. Students evaluate implementation costs and expected benefits, estimate return on investment, identify assumptions and commercial risks, and assess the overall strategic value of the solution to the organisation. The report concludes with clear recommendations, implementation priorities and a final judgement on whether the proposed AI initiative is feasible and worthwhile. The assessment places strong emphasis on critical analysis, technical understanding, business acumen and professional communication. Students are expected to support arguments with credible academic, industry and government evidence and include at least two professional visualisations such as frameworks, diagrams or tables. Harvard referencing is required throughout. Overview word count: approximately 330 words. The brief also allows authorised use of generative AI for idea generation, drafting/structuring and proofreading, provided the student verifies accuracy, references appropriately and submits the required GenAI declaration.
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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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Leadership / Digital Leadership / Business Management
4,000 words
Leadership in a Digital Age: Critical Self-Analysis, AI Transformation and Personal Development
This Leadership in a Digital Age assessment requires students to critically analyse their leadership strengths, behaviours and development needs within contemporary digital environments. The individual 4,000-word report combines academic leadership theory, diagnostic self-assessment, professional reflection, digital transformation and forward-looking personal development. Its central purpose is to demonstrate self-awareness and evaluate how leadership capabilities must evolve in response to technological, organisational and workforce change. NUL Assessment Brief LD7090 202… The first section requires critical evaluation of two or three contemporary leadership theories or models in relation to digital leadership. Students should avoid outdated approaches and instead examine how relevant theories align with the behaviours and characteristics required of leaders operating in technology-enabled organisations. NUL Assessment Brief LD7090 202… The second section focuses on self-analysis. Students use diagnostic tools relating to areas such as temperament, workplace culture, motivation, emotional control, management skills and Belbin team roles. Results should be reported and interpreted before being used to construct a personal SWOT analysis concentrating specifically on leadership characteristics relevant to the digital age. NUL Assessment Brief LD7090 202… A further component examines the leadership of hybrid, multi-generational teams. Students identify challenges that may arise in such environments and evaluate leadership capabilities and behaviours that could address them. Ethical, social and legal responsibilities associated with digital leadership must also be considered. NUL Assessment Brief LD7090 202… The report additionally evaluates how digital leaders can use Artificial Intelligence to support digital transformation and organisational performance. Appropriate workplace examples may involve machine learning, predictive analytics, intelligent automation or generative AI, with consideration of required resources such as data infrastructure, organisational skills and external partnerships. NUL Assessment Brief LD7090 202… The final section requires a Personal Development Plan containing justified leadership-development objectives, learning activities, measurable success criteria and timescales. These objectives should emerge directly from the earlier self-analysis and demonstrate how the student intends to become a more effective leader in a current or future digital role. NUL Assessment Brief LD7090 202… Overall, the assessment integrates leadership theory, reflective self-evaluation, hybrid-team management, AI-driven transformation and structured professional development within the context of leadership in the digital age.
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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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Data Mining
Data Mining – Classification, Model Optimisation and Evaluation
This individual Data Mining assignment requires students to apply the techniques covered in the module using the WEKA data mining platform. The assessment is worth 40% and focuses on practical application of machine learning and data mining methods, requiring students to configure algorithms, analyse datasets, optimise model parameters, evaluate classification performance and explain their technical choices and results. The assignment also assesses the ability to critically evaluate different algorithms and models of data mining. The assignment includes several tasks covering different stages of the data mining process. Students are required to work with supplied datasets and use appropriate preprocessing and classification techniques. The datasets include a balanced screenshot dataset containing processed screenshots classified into categories such as "Okay" and "Bad", where the objective is to train a model capable of identifying inappropriate content. The data has been processed using PCA to provide a smaller four-dimensional representation while protecting privacy and reducing the size of the data. Another supplied dataset concerns furniture reviews, containing positive ("pos") and negative ("neg") written feedback, with the objective of building a model that can determine whether a furniture review belongs to either class. The assessment evaluates students' ability to understand and describe the datasets, including the number of instances, number of columns, data types and relevant statistical information. For text-based data, students must apply appropriate vectorisation and describe the resulting dataset characteristics. Students must also consider class imbalance and apply an appropriate method where necessary, explaining how the chosen approach affects the distribution of instances between the classes. A significant component of the assignment involves classification algorithms and parameter optimisation. The assessment requires students to work with algorithms including Naive Bayes, LibSVM and J48. Students must investigate appropriate parameters and perform parameter searches or fine grid searches to identify suitable configurations. They must explain the selected parameters, their impact on the model and the reasoning behind the chosen values. Model performance must be evaluated using appropriate validation techniques, including cross-validation. Students are required to compare the algorithms using results such as overall accuracy and confusion matrices. The assignment expects students to identify an appropriate or best-performing algorithm in the context of the dataset and to provide a clear explanation of the comparison rather than simply reporting numerical results. The rubric places emphasis on accurate configuration, clear explanation of parameter choices, dataset analysis, class-balance treatment, parameter optimisation, cross-validation and critical comparison of algorithm strengths and weaknesses. High-quality work should explain both the technical process and the implications of the results, with results presented clearly through appropriate tables, confusion matrices and graphical outputs where required. The submission must be a single PDF document containing the report and must not exceed 10 pages. Students are instructed to include their student ID at the beginning of the report but not their name or other identifying details so that marking remains anonymous. Screenshots are specifically required to demonstrate use of the student's ID number as the random seed; other WEKA results should be presented in the student's own tables or result formats. The brief also states that no research beyond the material covered in the module is required and therefore no citations or reference list are required. The assignment explicitly prohibits the use of Generative AI tools for creating content and prohibits using GenAI tools or proofreading services for proofreading. Students are expected to complete the practical work themselves and explain their own technical choices and results.
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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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Cryptography
2,500 words
Cryptography – Secure Contract Exchange Protocol
This 2,500-word Cryptography assignment for Coventry University examines the design of a secure communication protocol for the digital exchange and signing of property contracts. The scenario is based on Lauren Order & Cashgrab LLP (LO&C), a UK and overseas property law firm seeking to establish a comprehensive document handling, exchange and signing system that supports remote property transactions while remaining consistent with legal requirements and enforceable under UK law. The assignment requires students to consider the exchange of contracts using the extended CIA model and to devise a secure communication protocol involving three parties: LO&C, the buyer's solicitor Hackit & Run (H&R), and the seller. The scenario specifies that LO&C communicates with the buyer through H&R, that LO&C and the seller collaborate on initial contract drafts, and that LO&C prepares and sends the final contract to the seller for approval and digital signing before forwarding the signed contract to H&R for the buyer's signature. LO&C and H&R have an existing secure communication relationship. A central requirement is the identification and justification of suitable encryption algorithms for the different stages of the contract exchange protocol. Students may select algorithms covered in lectures or undertake additional research to identify alternative algorithms. The report must explain why particular algorithms are appropriate at different stages of the protocol and demonstrate how the selected cryptographic techniques address the practical security requirements of the scenario. The protocol must be clearly illustrated using suitable graphics and pseudocode, with functioning code being an optional higher-level approach. Students are required to identify the strengths and limitations of their proposed protocol and discuss their findings. The assignment therefore combines theoretical knowledge of modern cryptography with practical protocol design and evaluation. Generative AI may be used to create suitable code where permitted, but students must demonstrate their understanding of the code. The assessed learning outcomes cover modern cryptographic concepts and techniques, including symmetric-key cryptography, key exchange, asymmetric cryptography, digital signatures, digital certificates and authentication. Students are also expected to model, test and assess the suitability of cryptographic protocols and algorithms for practical requirements and critically evaluate current research and technological developments in cryptography and its applications.
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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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