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Business Consultancy Project
5,000 words
Business Consultancy Project – Summative Coursework
This assessment requires students to complete a 5,000-word Business Consultancy Project designed to replicate the work of a professional consultant. The project accounts for 100% of the module assessment and requires students to investigate a clearly defined business issue, challenge or problem identified in the earlier Consultancy Project Proposal. Students must use the same client, topic and issue proposed in the previous submission unless a change has been strongly recommended through feedback or endorsed by the supervisor or tutor. The consultancy project requires deep and independent research using credible secondary sources, including recent peer-reviewed academic journals, industry reports and reputable business publications. The analysis should translate evidence into meaningful insights and actionable recommendations that create strategic value for the client organisation. The report must critically analyse the selected business problem rather than simply describe the organisation or relevant theories. The report should contain an Executive Summary, Introduction, Company/Client Overview, Problem Definition and Consultancy Focus, Stakeholder Analysis, Data Analysis and Framework Application, Recommendations and Conclusion, and a 500-word Employability Reflection. The Data Analysis and Framework Application section carries the largest indicative allocation at approximately 1,800 words and requires students to apply two or three relevant models or frameworks. Examples provided in the brief include SWOT, PESTLE, Porter’s Five Forces and the Balanced Scorecard. Students are also expected to present and interpret secondary data using appropriate tables, charts, Excel outputs or pivot tables. The Recommendations and Conclusion section should provide three actionable and prioritised recommendations supported by evidence, while considering implementation risks, barriers and anticipated benefits. The Employability Reflection should consider research, analytical, problem-solving, project management and communication skills, together with teamwork, leadership, ethical awareness, sustainability, personal development, career relevance and future professional growth. The assessment requires Harvard referencing throughout. The report should use professional formatting, third-person academic writing, numbered pages, correctly labelled tables and figures, and accurate in-text citations and references. The brief emphasises the use of recent and credible sources, with the majority of references expected to come from publications within the previous 6–12 months.
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German Electricity Load Forecasting Using Time Series and Machine Learning Models
This individual assessment focuses on analysing and forecasting German electricity load using time-series analysis, statistical forecasting models and machine-learning approaches. Students are required to work with publicly available electricity data from the Open Power System Data platform and investigate the characteristics of the German time series before developing and comparing a range of forecasting models. The project combines exploratory data analysis, time-series modelling, regression-based forecasting and neural-network modelling, with particular emphasis on critical evaluation of modelling choices and forecast performance. The first stage requires students to retrieve the 60-minute electricity data and isolate the German data using the country identifier “DE”. The data should be aggregated into weekly and daily values using tools such as Pandas, with observations retained from 1 January 2015 through October 2020. Students must produce initial plots and conduct exploratory data analysis to identify the main components of the time series, including possible trend and seasonal patterns. Time-series analysis should then be performed to investigate and test for non-stationarity. The modelling component begins with benchmark forecasting methods such as Mean, Naive, Seasonal Naive and Drift forecasts. Students are required to use a two-year forecast horizon and compare the performance of these benchmark approaches. An autoregressive modelling approach must then be developed using SARIMA where appropriate. Students must identify suitable model parameters using the AIC likelihood method, considering the required combinations of p, d, q, P, D and Q parameters. Model fit should be assessed through residual analysis, including inspection of residual distributions and autocorrelation plots. Forecasts should include confidence intervals and performance should be evaluated using suitable metrics such as RMSE. The assessment also requires an exogenous temperature variable to be incorporated into a SARIMAX model. Berlin is specified as the representative location for German temperature data. Students must recognise the distinction between a true operational forecast and an explanatory or conditional forecast when future observed temperature values are used. The temperature variable should also be used to produce weekly forecasts. Further modelling requires students to apply a feature-based regression model, such as Random Forest or Gradient Boosting Regression, to the weekly electricity-load and temperature data. The final modelling stage uses hourly data with a Long Short-Term Memory (LSTM) neural network. Students must conduct a literature review relating to LSTM applications for the forecasting task, build and tune the model, evaluate its performance and forecast the final two years of available data. The report must critically compare the models rather than simply present numerical results. Students must address specific questions concerning improvement over the Seasonal Naive benchmark, prevention of data leakage when constructing temperature lag features, SARIMAX parameter choices, the value and availability of temperature and holiday covariates, and the relative interpretability and complexity of SARIMAX, feature-based and neural-network models. Students must also recommend one model for operational use based on accuracy, uncertainty, interpretability and maintenance considerations. The final report should be 6–8 pages and include model forecast plots, an evaluation-metrics comparison table, critical discussion of the results, comparison with real data, future improvements and appropriate references. Students must also create a GitHub repository containing the codebase and follow appropriate coding practices. The submitted code must reproduce the figures, models and numerical values presented in the report.
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Research Methods
Assignment 3 – Large Language Models: LLM Coding and Report
This individual assessment for the Research Methods module focuses on the application of Large Language Models (LLMs) to a practical data science problem. The assignment is worth 25% of the module and is designed to develop students’ knowledge and understanding of research methods, investigative planning, data analysis, model evaluation and effective technical communication. Students are required to complete both a coding component and a concise written report demonstrating how an LLM has been selected, trained or fine-tuned, applied to a suitable task and evaluated against an appropriate baseline. The assessment begins with familiarisation with relevant literature. Students are expected to investigate the history and development of their chosen problem and examine the methods that have previously been used to address it. The brief provides key papers on common types of LLMs as a starting point for the literature review. Students then select a task that can be addressed through fine-tuning an LLM, with examples including sentiment analysis, fake news detection and topic classification. A publicly available dataset suitable for the selected text-classification problem must also be identified. Suggested sources include Kaggle and Hugging Face Datasets. The data must be appropriately preprocessed, including tokenisation using BERT's tokenizer and division into training and testing sets. Students then fine-tune a pre-trained BERT or BERT-style model using suitable tools such as the Hugging Face Transformers library and PyTorch. Possible model choices include BERT, RoBERTa and T5. The selected model should be appropriate for the specific problem, recognising that different language models may perform differently across tasks. Students are expected to implement a suitable training process using an appropriate optimiser and loss function. Model performance must be evaluated using relevant classification metrics, including accuracy, precision, recall and F1-score. The performance of the selected LLM should also be compared with a baseline model, such as Logistic Regression, Naive Bayes or a pre-trained BERT model. The analysis should explain the results and consider their relevance to the chosen problem. Two main submission components are required. The first is a code notebook, such as a Jupyter or Google Colab notebook, containing annotations explaining the purpose and operation of the relevant code so that another person can understand and reproduce the work. The second is a report of no more than three pages, including appropriate figures, tables and references. The report should cover the motivation and dataset, methodology, model training and evaluation, results and discussion, limitations, conclusion and possible future improvements. The assessment rubric places particular emphasis on coding quality and implementation, model architecture, analysis and interpretation, and report presentation. Strong work should demonstrate well-structured and reusable code, clear explanation of the model architecture and configuration, appropriate evaluation metrics and visualisations, meaningful comparison with relevant literature or baseline models, and critical evaluation of the model's success and possible improvements.
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Fundamentals of Digital Technologies
Fundamentals of Digital Technologies – Group Project and Individual Refinement
This assessment is a project-based assignment for the DG4FDTL Fundamentals of Digital Technologies module. It is designed to develop students' practical understanding of digital technologies by combining mathematical concepts, programming, algorithms, data analysis and learning methods within a real-world project. The project is structured around a common group component, known as the Group Trunk, and an individual component in which each student develops a specialised refinement of the shared project. The project topics are designed around key areas covered by the module, including linear algebra, calculus, probability, learning algorithms, algorithms and Python programming. Students work in groups to establish a common project foundation and then develop an individual refinement that extends the functionality or analytical capabilities of the shared system. The project guidance is intended for a mixed cohort that may include Data Science, Computer Science and Business Analytics students. The assessment is divided into two main components: Proposal and Implementation. The Proposal accounts for 40% of the assessment, while Implementation accounts for 60%. Both the group trunk and individual trunk contribute to the assessment. The group proposal requires students to describe what the group intends to build, explain the problem being addressed, outline the proposed approach and identify the main functions and responsibilities within the project. Students should demonstrate a clear understanding of the project objectives and provide an appropriate plan for developing the shared system. The implementation stage requires students to develop the common group functionality and then complete their individual refinement. The shared component provides the basic project framework, while the individual refinement allows each student to investigate a specific aspect of the problem and add specialised functionality. Depending on the selected project topic, individual refinements may involve data analysis, visualisation, optimisation, prediction, monitoring, reporting or other computational features. The project topics include practical applications such as productivity and task-management systems, supermarket sales analysis and other data-driven applications. Students are expected to use Python and appropriate libraries or computational techniques to implement their solutions. The project materials provide examples involving data structures, CSV files, functions, numerical calculations, visualisation and analytical dashboards. The assessment emphasises both technical implementation and the student's ability to explain the problem, approach and functionality of the developed system. Students should demonstrate appropriate use of programming concepts, mathematical foundations, algorithms and data-analysis techniques. The individual refinement should clearly extend the common project and demonstrate the student's own contribution to the overall solution. Overall, the assessment develops practical digital-technology skills through collaborative project development followed by individual technical refinement. It provides experience in project planning, programming, computational problem solving, data analysis, visualisation and the application of mathematical and algorithmic concepts to practical problems.
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Data Science
1,000 words
Individual Project – Image Segmentation
This individual project focuses on image segmentation using a subset of the ADE20K dataset. The assessment requires students to investigate viable image segmentation models, analyse and visualise the provided data, develop appropriate data preprocessing procedures, train a segmentation model and critically evaluate the resulting performance. The assignment is worth 25% of the module assessment and is designed to assess students’ ability to apply research methods to a data science problem, communicate analytical findings effectively and select appropriate methods while understanding their advantages and limitations. Students are required to work with the provided ADE20K dataset and produce a model capable of semantic segmentation for four specified object classes: person, car, book and airplane. The model must identify which regions of an unlabelled image correspond to these four classes. The assignment specifically requires semantic segmentation rather than simply identifying whether an object is present. Treating the task as binary segmentation, where all objects are considered a single foreground class, will result in a significant penalty. The project requires students to investigate different approaches and models, perform exploratory data analysis, develop suitable preprocessing procedures and customise their selected model or models to improve performance. Students may use more than one model, although at least one model must be partially or fully trained by the student. Where multiple models are used, comparison with pretrained models is encouraged. Potential approaches include segmentation architectures such as U-Net and Mask R-CNN, with appropriate model selection justified through relevant literature and experimental evidence. The report should contain at least four core sections: an Introduction incorporating a literature review, Data Description and Exploratory Data Analysis, Methodology, and Results and Discussion. The literature review should cite at least three relevant research papers. The results section should include evaluation of three test images using the developed model and comparison with relevant published literature. Students are expected to provide a critical analysis of their work rather than simply reporting numerical results, explaining the reasons for observed outcomes and considering how modelling and implementation choices affected performance. The assessment also requires a documented Google Colab notebook containing the implemented steps used to train and evaluate the model. The notebook should be accessible to markers and should demonstrate the preprocessing, model development, training and evaluation process. The report must be between 700 and 1,000 words, excluding references and code, and should include code in text format as an appendix rather than screenshots. Figures and tables should have clear captions explaining what they present and their source. Assessment criteria include literature review and context, dataset description and exploratory data analysis, implementation quality, quality of analysis and critical discussion, and report quality and structure. Strong work is expected to demonstrate appropriate model selection, effective preprocessing and augmentation, suitable evaluation metrics such as Intersection over Union (IoU) and Dice score, meaningful visualisation and a critical interpretation of results and limitations.
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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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Business
850 words
HopeLink Community Support and Food Security Database
This assignment requires students to design, develop and analyse a relational database for HopeLink Community Support and Food Security, a fictitious non-profit organisation working across rural and urban communities to reduce poverty and hunger. HopeLink currently relies on spreadsheets, which have resulted in inefficiencies and reporting errors. The proposed database is intended to improve operational efficiency, transparency and evidence-based reporting while supporting the organisation's work towards United Nations Sustainable Development Goals (SDGs) 1 and 2: No Poverty and Zero Hunger. The database development project involves five main entities: Beneficiary, Donor, Product, Collection and Collection Detail. Students are required to create a data dictionary defining the tables, attributes and appropriate validation rules. They must then use SQL within Microsoft Access to create the required tables through SQL View, rather than using the Access front end. Primary keys and foreign keys must be identified and implemented as part of the database design. Students must also establish relationships between the database entities and enforce referential integrity to maintain accurate information for management reporting and decision-making. Appropriate validation rules, user-friendly error messages, drop-down lists and input masks should be implemented where required. The database must be populated with sufficient realistic data for testing, with minimum requirements of 10 Beneficiary records, 10 Donor records, 20 Product records, 10 Collection records and 20 Collection Detail records. A further component requires students to develop five useful SQL queries in Microsoft Access to support analysis and reporting for HopeLink. The queries must follow specific requirements, including one parameter query, at least three queries containing a WHERE clause, at least one aggregate query and at least two queries involving joins across more than two tables. Students must provide screenshots of the SQL code and resulting outputs, together with an English explanation and a short justification of how each query could support HopeLink's operations and decision-making. The final component is an 850-word maximum business insights report investigating the use of food banks in the UK and considering how initiatives could support SDG 1 and SDG 2. Students are expected to use reliable external data, present relevant graphs and analyse current trends in food-bank usage. This section requires Harvard referencing and should use reliable academic, industry and other appropriate sources. The report is based on SDG 1 and SDG 2 and is not directly linked to the database developed for the assignment. Overall, the assignment assesses students' ability to apply data-modelling techniques, database design principles, SQL and data analytics to support organisational operations and strategic decision-making. It combines practical database development in Microsoft Access with data analysis and a business-focused evaluation of food-bank trends and sustainability goals.
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Managing Customer Experience
1,000 words
Managing Customer Experience – Customer Experience Strategy Report
This assessment for the Managing Customer Experience module requires students to prepare a 1,000-word business report evaluating the customer experience (CX) strategy of one selected banking organisation. Students must choose one company from the organisations provided in the assessment brief and analyse its customer experience strategy within the specific country in which it operates. The report is designed to develop a critical understanding of the importance of customer experience for business success and the ways in which organisations can create a seamless omnichannel customer journey. The assessment provides a choice of six banking organisations: Lloyds Banking Group in the United Kingdom, HDFC Bank in India, Habib Bank Limited (HBL) in Pakistan, Nabil Bank Ltd in Nepal, Prime Bank PLC in Bangladesh, and Access Holdings/Access Bank in Nigeria. Students are required to clearly identify the selected organisation and country in their report and use relevant customer experience concepts, academic literature, research and practical examples to support their analysis. The first major requirement is to explain the concept of customer experience and critically review its importance to the success of the selected banking brand. The report should consider the role of CX, the mutual benefits created for the organisation and its customers, and the impact of customer experience on business performance, including financial performance. Students should also provide specific examples demonstrating how the selected bank has used customer experience to create or strengthen competitive advantage. The second major requirement is to evaluate how the selected bank delivers a seamless omnichannel customer experience. This requires discussion of the organisation's approach to creating a consistent customer journey across relevant channels. Students must develop a detailed customer persona based on research and data analysis, covering the persona's profile, goals, pain points and motivations. They must also create a customer journey map using the recommended class template. The journey map should identify stages, needs, activities, feelings, pain points and opportunities for improvement. The report should evaluate the effectiveness of the selected bank's customer journey and identify areas where the organisation could improve the ease and quality of the customer's experience. The customer persona and customer journey map are important components of the assessment, and the words contained within these visual templates do not count towards the 1,000-word limit. The report must be written in an academic style and in the third person. Harvard referencing is required for academic sources, figures, diagrams and independent research. The main report has a maximum word count of 1,000 words, excluding the cover sheet, title page, table of contents, references and appendices. The assessment therefore combines academic research, customer experience theory, organisational analysis, customer persona development, journey mapping and critical evaluation of omnichannel customer experience.
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Leadership in a Digital Age
5,000 words
Advanced Practice Research Project – Reflective Individual Report and Poster Presentation
This assessment requires students to produce a 5,000-word individual business consultancy report based on a chosen organisation. The selected organisation may be a private company, a public organisation that is not government owned, or a non-governmental organisation. The chosen organisation acts as the client and the student takes the role of a consultant who investigates a current or potential business challenge, examines the resulting issues and develops recommendations that can provide strategic value to the organisation and its wider industry. The project requires students to formulate a business project that has strategic importance within a chosen area of management. The report should identify a specific organisational challenge and the issues resulting from that challenge, supported by recent evidence demonstrating that the challenge is ongoing, unresolved or likely to become significant in the future. The challenge should be connected to current affairs and emerging trends within the relevant industry and business environment. The report should also provide a consultant's perspective on why the challenge matters to the selected organisation. A clear purpose for the report must be established. This should explain why the selected challenge and issues are being investigated, how the research will benefit the client organisation and how the project relates to the chosen organisational specialisation area. The report should demonstrate an understanding of the importance of strategy in business and establish a clear connection between the identified problem, the research undertaken and the proposed solutions. Stakeholder analysis is a major component of the assessment. Students must identify relevant internal and external stakeholders, explain their relationship with the identified challenge and issues, and evaluate the impact of the research on these stakeholder groups. The analysis should consider stakeholder power and interest and demonstrate how the proposed project and its findings could affect different stakeholders. The main analytical section requires the use of secondary data, including both conceptual and numerical evidence. Students should critically evaluate relevant information using appropriate management frameworks and analytical approaches. The evidence should be used to investigate the challenge, understand its causes and consequences, and support the development of appropriate recommendations. The report should demonstrate critical thinking rather than simply describing information. The final section provides recommendations designed to resolve the identified challenge and resulting issues. Recommendations should be directly supported by the secondary-data analysis and critical evaluation and should be relevant and realistic for the selected organisation. The conclusion should return to the central challenge and explain how the purpose of the report has been addressed, while demonstrating the strategic value of the recommendations for the organisation and, where appropriate, the wider industry. The recommended structure consists of a declaration page, title page, contents, executive summary, introduction, challenge and issues, purpose of the report, stakeholder analysis, evaluation and analysis of secondary data, recommendations and conclusion, Harvard references and appendices where required. The suggested allocation is approximately 500 words for the executive summary, 300 for the introduction, 500 for the challenge and issues, 200 for the purpose, 600 for stakeholder impact, 2,000 for secondary-data evaluation and 900 for recommendations and conclusion. The total report length is 5,000 words, excluding the front cover, contents, bibliography and appendices.
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Business Project
5,000 words
Business Consultancy Project: Strategic Analysis of a Business Challenge
This assessment requires students to produce a 5,000-word individual business consultancy report based on a chosen organisation. The selected organisation may be a private company, a public organisation that is not government owned, or a non-governmental organisation. The chosen organisation acts as the client and the student takes the role of a consultant who investigates a current or potential business challenge, examines the resulting issues and develops recommendations that can provide strategic value to the organisation and its wider industry. The project requires students to formulate a business project that has strategic importance within a chosen area of management. The report should identify a specific organisational challenge and the issues resulting from that challenge, supported by recent evidence demonstrating that the challenge is ongoing, unresolved or likely to become significant in the future. The challenge should be connected to current affairs and emerging trends within the relevant industry and business environment. The report should also provide a consultant's perspective on why the challenge matters to the selected organisation. A clear purpose for the report must be established. This should explain why the selected challenge and issues are being investigated, how the research will benefit the client organisation and how the project relates to the chosen organisational specialisation area. The report should demonstrate an understanding of the importance of strategy in business and establish a clear connection between the identified problem, the research undertaken and the proposed solutions. Stakeholder analysis is a major component of the assessment. Students must identify relevant internal and external stakeholders, explain their relationship with the identified challenge and issues, and evaluate the impact of the research on these stakeholder groups. The analysis should consider stakeholder power and interest and demonstrate how the proposed project and its findings could affect different stakeholders. The main analytical section requires the use of secondary data, including both conceptual and numerical evidence. Students should critically evaluate relevant information using appropriate management frameworks and analytical approaches. The evidence should be used to investigate the challenge, understand its causes and consequences, and support the development of appropriate recommendations. The report should demonstrate critical thinking rather than simply describing information. The final section provides recommendations designed to resolve the identified challenge and resulting issues. Recommendations should be directly supported by the secondary-data analysis and critical evaluation and should be relevant and realistic for the selected organisation. The conclusion should return to the central challenge and explain how the purpose of the report has been addressed, while demonstrating the strategic value of the recommendations for the organisation and, where appropriate, the wider industry. The recommended structure consists of a declaration page, title page, contents, executive summary, introduction, challenge and issues, purpose of the report, stakeholder analysis, evaluation and analysis of secondary data, recommendations and conclusion, Harvard references and appendices where required. The suggested allocation is approximately 500 words for the executive summary, 300 for the introduction, 500 for the challenge and issues, 200 for the purpose, 600 for stakeholder impact, 2,000 for secondary-data evaluation and 900 for recommendations and conclusion. The total report length is 5,000 words, excluding the front cover, contents, bibliography and appendices.
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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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Managing Customer Experience
2,500 words
Customer Experience Strategy: Critical Evaluation of a Global Mobile Phone Brand
This assessment requires a 2,500-word business report critically evaluating the customer experience (CX) strategy of one company operating within the global mobile phone industry. Students must select one company from Samsung, Apple, Huawei or Google and analyse its customer experience strategy within one specific country in which the company operates. The report is written from the perspective of a CX consultant and is intended for the Board of Directors of the selected company. Relevant customer experience concepts, academic literature and practical business applications should be incorporated throughout the report. The first part of the report examines the concept of customer experience and its importance to business success. The analysis should consider the role of CX, the mutual benefits created for the company and its customers, the effect of CX on business and financial performance, and specific examples of how the selected brand has used customer experience to achieve competitive advantage. The second part evaluates how the selected company delivers a seamless omnichannel customer experience. This requires consideration of customer personas and customer journey mapping and their contribution to developing an effective customer strategy. Students must create a detailed customer persona based on research and data analysis, covering the profile, goals, pain points and motivations of the selected customer. A customer journey map should then identify stages, needs, activities, feelings, pain points and opportunities for improvement. The report should evaluate the effectiveness of the company's customer journey and suggest practical improvements. The words contained within the recommended customer persona and customer journey map templates are excluded from the 2,500-word limit. The third part evaluates the effectiveness of the company's use of customer experience metrics to measure the quality of its relationship with customers. Students must discuss three CX metrics covered in the module and critically assess their benefits, limitations and relevance to the selected company. The analysis should explain why these metrics would be useful for measuring the mutually beneficial relationship between the company and its customers. The final part critically reflects on the company's ability to create a customer-centric culture in an increasingly digital environment. This includes evaluating CX leadership and CX governance, as well as one additional critical success factor such as people, CX structure, strategy and process, or innovation. The report should conclude by evaluating the overall quality of the company's relationship with its customers and proposing practical ways to improve customer experience in the era of increasing digitalisation. The assessment addresses four learning outcomes relating to the importance of customer experience, organisational CX performance metrics, seamless omnichannel customer journeys and customer experience strategies in the context of rapid digitalisation. The submission must use Harvard referencing and an academic business-report style.
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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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Time Series Modelling
Time Series Modelling Case Study – Oil Price Forecasting
This individual Time Series Modelling Case Study focuses on analysing and forecasting oil price data using established time-series techniques and an alternative modelling approach. The assessment requires students to work with daily oil price information covering the period from 2024 to 2026 and investigate the underlying patterns, stationarity and forecasting behaviour of the data. The first part of the assignment involves exploratory data analysis and time-series modelling using an ARMA-based approach. Students are required to create appropriate visualisations of the data, perform exploratory analysis and conduct tests for non-stationarity, including relevant stationarity diagnostics such as ADF, ACF and PACF analysis and differencing where required. An ARMA model must then be defined, with suitable model parameters identified using the AIC likelihood approach. The assessment requires the student to examine possible combinations of model parameters, assess model residuals, evaluate model performance using appropriate metrics such as RMSE, and produce forecasts extending 24 months into the future. Confidence intervals must also be included with the forecasts. The second part requires students to investigate an alternative modelling solution for the same oil-price time-series data. Possible approaches discussed in the assignment include models such as LSTM and Prophet. Students are expected to conduct a literature review relating to the selected alternative model, build and apply the model, tune relevant hyperparameters where appropriate, generate 24-month forecasts, create suitable visualisations and calculate appropriate evaluation metrics. The final part of the assessment requires a 6–8 page report describing the modelling process, forecasts, analysis and inferences. The report should explain the reasoning behind the analytical and modelling choices rather than simply presenting numerical results. Students are expected to critically discuss why particular approaches were selected, how the modelling decisions may have influenced the results, how forecasts compare with subsequently collected real data where available, and what improvements could be made in future work. The assessment evaluates both the technical implementation and the quality of the written analysis. The code component assesses completion of the modelling and forecasting tasks, stationarity testing, the alternative solution, code quality and annotation. The report component assesses discussion of the analysis and inferences, comparison of the modelling approaches, clarity of interpretation, report structure, appropriate use of figures and suitable academic references. The submission consists of a report in PDF or Word format, with the code submitted separately or through an appropriate repository.
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Digital Marketing / Marketing Analytics / Data-Driven Marketin
Data-Driven Marketing Analytics: Google Analytics 4 and Google Merchandise Store Campaign Analysis
This Data Driven Marketing assessment requires students to develop practical digital analytics skills and apply them to the evaluation of a real-world marketing campaign. The assessment combines professional training through LinkedIn Learning with a recorded executive-level presentation based on data from the Google Analytics 4 Demo Account for the Google Merchandise Store. The aim is to analyse campaign performance, interpret relevant marketing KPIs and present actionable recommendations that senior executives and managers can use for decision-making. CW 1_ LinkedIn Learning and Rec… The first task requires completion of two LinkedIn Learning courses: Google Analytics 4 (GA4) Essential Training and Advanced Google Analytics. Students must submit the certificates of completion as evidence of developing the foundational and advanced skills required for digital marketing analysis. CW 1_ LinkedIn Learning and Rec… The second task places the student in the role of a digital marketing executive for the Google Merchandise Store. Students select and analyse a digital marketing campaign using GA4 demo-store data and then create a 10-minute recorded presentation, with a tolerance of ±2 minutes, providing evidence-based recommendations for improving campaign performance. CW 1_ LinkedIn Learning and Rec… The main presentation is limited to 12 slides and should include an executive summary, key findings and insights, recommendations, actionable steps, and references or appendix material. An additional 4–6 appendix/reference slides may be used for supporting charts, graphs and data. Recommendations must be linked directly to analytical evidence and aligned with the campaign's overall marketing objectives. CW 1_ LinkedIn Learning and Rec… The analysis should examine relevant marketing performance indicators, including click-through rate, conversion rate, cost per acquisition, return on ad spend and user-engagement measures such as bounce rate, session duration, pages per session and retention patterns. CW 1_ LinkedIn Learning and Rec… Students are expected to move beyond descriptive reporting and develop strategic recommendations concerning budget reallocation, creative optimisation, audience targeting and funnel performance. The presentation should identify underperforming and high-performing channels, evaluate audience segments, examine points of user drop-off and propose practical interventions to improve conversion and campaign efficiency. CW 1_ LinkedIn Learning and Rec… The marking scheme gives 20 points for LinkedIn Learning certification, 35 points for executive communication and structure, 35 points for data analysis and insight generation, and 10 points for references and appendices. CW 1_ LinkedIn Learning and Rec… CW 1_ LinkedIn Learning and Rec… Overall, the assessment integrates GA4 skills, campaign analytics, KPI interpretation, data visualisation, strategic recommendation development and executive presentation skills within a practical digital-marketing context. The brief also requires Cite Them Right Harvard referencing. CW 1_ LinkedIn Learning and Rec…
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Strategic Management / Sustainability / Responsible Leadership
2,500 words
Organisational Strategy and Sustainability: Strategic Evaluation of Engineers & Planners Company Ltd
This Consultancy Project Proposal assessment requires students to develop a professional and feasible research proposal addressing a current organisational issue or business challenge affecting an existing organisation. The work combines critical business analysis with research design, secondary-data methodology, ethics and visual communication through an accompanying one-page digital poster. MSc Management Summative Assess… The first task requires students to introduce the selected organisation and critically examine between one and three current challenges. The discussion must be evidence-based and supported by recent sources. Stronger work should connect the identified issues to current affairs, recent news, Sustainable Development Goals and relevant academic or company evidence. MSc Management Summative Assess… The second task requires development of a clear research aim, three objectives and, for stronger submissions, one or two research questions. Students must demonstrate awareness and application of research methodology using secondary research only, while incorporating both qualitative and quantitative approaches. The research design should be directly connected to the selected organisational challenges. MSc Management Summative Assess… The third task focuses on the research plan and ethical considerations. Students must explain ethical issues associated with secondary-data collection and support the discussion using credible literature. The accompanying poster should present a coherent research plan and demonstrate effective data-analysis and data-presentation skills through relevant graphs, charts, tables or descriptive statistics. MSc Management Summative Assess… The fourth task assesses the student's ability to critically organise and synthesise information into a coherent proposal. The written report should use recent, credible sources, while the poster should include a short reflection on challenges encountered when synthesising evidence for the proposed study. MSc Management Summative Assess… The report must be written in the third person and use Harvard referencing. The proposed structure allocates approximately 150 words to the introduction, 400 words to the challenge discussion, 150 words to the research aim and objectives, 500 words to methodology, 200 words to ethical considerations and 100 words to the conclusion. The digital poster has no formal word count but must fit on a single A4 page and use a readable 10–12 point font. MSc Management Summative Assess… MSc Management Summative Assess… Overall, the assessment develops skills in consultancy problem definition, research design, secondary-data analysis, mixed-method thinking, ethical research practice, critical synthesis and professional visual communication.
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Business Consultancy Project: Strategic Analysis, Stakeholder Evaluation and Evidence-Based Recommendations
Business Consultancy, Strategic Management, Business Analysis, Consultancy Project, Stakeholder Analysis, Mendelow Matrix, Business Strategy, Data Analysis, Secondary Research, SWOT Analysis, PESTLE Analysis, Porter’s Five Forces, Balanced Scorecard, Ethics, Sustainability, UN Sustainable Development Goals, Recommendations, Risk Analysis, Implementation Barriers, Organisational Strategy, Business Problem Solving, Employability Skills, Professional Development, Reflective Practice, Project Management, Evidence-Based Decision-Making
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Business Management / Consultancy / Strategic Management
5,000 words
Business Consultancy Project: Strategic Analysis, Stakeholder Evaluation and Evidence-Based Recommendations
This Business Consultancy Project assessment requires students to produce a professional 5,000-word consultancy report addressing a strategically important issue, challenge or problem facing a client organisation. The project must normally continue from the topic and client proposed in the earlier Consultancy Project Proposal, ensuring continuity between the proposal and final consultancy work. The assessment is designed to replicate professional consultancy practice through independent research, critical analysis and the development of actionable recommendations. Copy of Apr 25 Onwards brief_EE… Copy of Apr 25 Onwards brief_EE… The report begins with an Executive Summary, followed by an introduction and company/client overview. Students must define one clear business problem, establish the overall project aim and purpose, and specify the consultancy focus, which may relate to areas such as strategy, human resources, marketing or sustainability. The selected issue should be clearly connected to the organisation’s wider strategic and industry context. Copy of Apr 25 Onwards brief_EE… A dedicated Stakeholder Analysis section requires identification and evaluation of internal and external stakeholders. Students must assess stakeholder interests, influence, expectations, conflicts and power relationships, and are encouraged to apply a framework such as Mendelow’s Matrix. The purpose is to show how stakeholder dynamics influence the consultancy problem and the feasibility of proposed solutions. Copy of Apr 25 Onwards brief_EE… The largest section is Data Analysis and Framework Application, where students critically evaluate secondary evidence and apply two or three relevant management frameworks. Suitable approaches may include SWOT, PESTLE, Porter’s frameworks and the Balanced Scorecard. The analysis should incorporate credible company and industry evidence, Excel outputs, tables or charts where relevant, and connect patterns in the data to strategic, operational or HR implications. Ethical and sustainability considerations should be embedded where appropriate. Copy of Apr 25 Onwards brief_EE… The project concludes with three prioritised and evidence-based recommendations. Recommendations should be practical, justified, risk-aware and linked directly to the findings. Students must explain expected benefits and demonstrate how the proposed actions create strategic value for the client organisation. Copy of Apr 25 Onwards brief_EE… The final assessed section is a 500-word Employability Reflection covering skills developed through the project, application of theory to practice, professional behaviours, personal strengths and weaknesses, career relevance and specific future-development actions. Copy of Apr 25 Onwards brief_EE… Overall, the assessment integrates consultancy problem definition, stakeholder analysis, strategic frameworks, secondary-data interpretation, ethics, sustainability, recommendations and professional reflection within a Masters-level business project. The marking criteria reward criticality, current evidence, intellectual originality, professional consultancy thinking and accurate Harvard referencing. Copy of Apr 25 Onwards brief_EE… One important point: the brief states that the majority of references should come from sources published within the last 6–12 months, so the final project is expected to use very current company, industry and academic evidence. Copy of Apr 25 Onwards brief_EE…
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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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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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Big Data Analytics / Machine Learning
3,000 words
Machine Learning on Big Data Using PySpark: Large-Scale Data Analysis and Predictive Modelling
This group-based Machine Learning on Big Data project requires students to apply machine learning techniques to a large real-world dataset using PySpark DataFrames and Spark machine-learning libraries. Students select a substantial dataset, ideally between approximately 300 MB and 1 GB, from sources such as Kaggle, workplace data or other valid repositories, and develop an end-to-end big-data analytics workflow. CN7030 CRWK 26T1 The project begins with data loading and preprocessing using PySpark. Students are expected to handle missing values, perform data normalisation and feature engineering, identify class imbalance and propose appropriate mitigation strategies. Where text datasets are selected, additional preprocessing may include stemming, lemmatization and TF-IDF representation. CN7030 CRWK 26T1 The modelling stage requires implementation of an appropriate machine-learning approach using PySpark MLlib or Spark ML. The brief expects a multiclass rather than binary classification problem and allows techniques including multiclass classification, ensemble learning, clustering and text mining. Students must justify their model choice and consider model robustness, bias and variance when attempting to improve predictive performance. CN7030 CRWK 26T1 Students then perform hyperparameter tuning using techniques such as grid search or random search and evaluate the resulting model with appropriate measures. Relevant evaluation outputs may include accuracy, F1-score, precision, recall and a confusion matrix. Results should also be visualised or clearly presented and interpreted to identify meaningful patterns and performance characteristics. CN7030 CRWK 26T1 The project additionally requires consideration of Legal, Social, Ethical and Professional (LSEP) issues. Students discuss potential ethical concerns associated with their dataset, including bias and privacy risks, and propose suitable mitigation strategies. The final work is consolidated into a single user-friendly HTML analytics report that clearly presents the group's preprocessing, modelling, optimisation, evaluation and interpretation. CN7030 CRWK 26T1 CN7030 CRWK 26T1 Overview word count: approximately 335 words. If you are also uploading the presentation separately to the Reference Library, that should be a second entry under “Presentations and Academic Posters”, because the presentation forms a distinct 40% component and assesses understanding of Spark, preprocessing, modelling, optimisation, evaluation and responses to examiner questions. CN7030 CRWK 26T1
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Machine Learning / Cloud Computing / Artificial Intelligence
4,004 words
Cloud-Based Machine Learning for Financial Fraud Detection
This Machine Learning on Cloud assessment requires students to design, implement and critically evaluate a cloud-oriented machine-learning solution for financial fraud detection. Working as a group, students address a scenario in which a financial-services organisation requires an automated model capable of detecting fraudulent transactions, reducing financial losses and improving customer security. The project combines machine-learning development with critical evaluation of cloud infrastructure, data preparation, model performance and responsible AI considerations. NUL - LD7187 -Assessment Brief … The project begins with a cloud feasibility study comparing at least two major machine-learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Students evaluate factors including performance, scalability, cost, compliance, system integration and vendor lock-in before providing a justified recommendation. The project then moves into Exploratory Data Analysis, where patterns, anomalies and correlations within the fraud dataset are investigated using visualisations such as heatmaps, histograms and boxplots. NUL - LD7187 -Assessment Brief … A substantial part of the assessment focuses on data preprocessing and class imbalance. Students are expected to clean and transform the dataset, apply scaling and encoding, perform feature engineering and investigate techniques such as SMOTE, undersampling and cost-sensitive learning. These decisions must be justified in terms of their potential effect on predictive performance. NUL - LD7187 -Assessment Brief … Students must then select and train at least two machine-learning models. Suggested algorithms include Logistic Regression, Random Forest, XGBoost and Neural Networks. Appropriate cross-validation and hyperparameter-tuning procedures should be applied, followed by systematic evaluation using precision, recall, F1-score, AUC and precision-recall curves. Supporting visualisations should include confusion matrices, ROC curves and feature-importance analysis. NUL - LD7187 -Assessment Brief … The final component addresses professionalism and ethics in cloud-based AI, including bias, fairness, transparency, data privacy and environmental sustainability. Overall, the project integrates cloud-platform selection, exploratory analytics, preprocessing, imbalanced-data handling, predictive modelling, model evaluation and ethical AI into an applied financial fraud-detection solution. NUL - LD7187 -Assessment Brief … Note: the uploaded brief does not explicitly name a referencing system. If your portal requires a selection, I would use Not specified rather than assume Harvard.
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Statistical Programming / Data Science / Business Analytics
Statistical Programming with R: Data Analysis, Probability, Regression and Business Decision-Making
This Statistical Programming assessment evaluates students' ability to apply statistical techniques and R programming to practical data-science and business decision-making problems. The individual assessment combines descriptive statistics, data preparation, visualisation, probability, regression, correlation and sampling, requiring students to demonstrate both conceptual statistical understanding and practical implementation in RStudio. The module learning outcomes emphasise the application of statistical methods to large and real-world datasets, critical evaluation of analytical techniques and awareness of legal, cultural and ethical issues associated with data analysis and dissemination. KL7012 - Statistical Programmin… The early tasks examine fundamental statistical reasoning. Students interpret weight-loss data comparing exercise classes with gym-only workouts using sample size, mean, mode and standard deviation, and explain an appropriate method for dealing with missing data, including its advantages and disadvantages. KL7012 - Statistical Programmin… A substantial practical component uses a cystic fibrosis dataset containing variables such as age, sex, height, weight, body-mass-related measurements, forced expiratory volume, residual volume, functional residual capacity, total lung capacity and maximum expiratory pressure. Students import the data into an R data frame, generate descriptive summaries and interpret the results. They then use scatterplots to investigate relationships between variables and sex-stratified boxplots to identify possible outliers. KL7012 - Statistical Programmin… The assessment also covers major probability models. Students apply probability concepts to healthcare survival, helpdesk email arrivals and fuel-demand scenarios, while also discussing how changing assumptions or real-world conditions can affect interpretation. These exercises assess understanding of statistical distributions and their application to operational and managerial decision-making. KL7012 - Statistical Programmin… Further analytical tasks examine linear regression and correlation. Students analyse the relationship between temperature and converted sugar in a chemical process, use a regression model to estimate the expected response at a specified temperature, and interpret relevant summary statistics. They also calculate and evaluate the suitability of a correlation coefficient for examining the relationship between advertising activity and product purchases. KL7012 - Statistical Programmin… The final and most substantial task involves a real-world M1 traffic-speed investigation for a manufacturing organisation. Students must design an appropriate sampling strategy, collect data from the specified Traffic England source, conduct statistical analysis in RStudio and develop evidence-based conclusions. The statistical report for this task is limited to 1,500 words and should include sampling methodology, collected data, statistical analysis, results, conclusions and relevant background research, supported by appropriate graphs, tables and charts. Raw data and RStudio calculations must be included in an appendix. KL7012 - Statistical Programmin… Overall, the assessment integrates statistical theory with R-based practical analysis, covering descriptive statistics, probability, visualisation, missing-data treatment, regression, correlation, sampling and critical interpretation of results in healthcare, operational and business contexts.
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Information Visualisation / Data Analytics / Data Science
1,500 words
Information Visualisation Project Using Power BI and Python
Assignment overview — ready to paste This Information Visualisation project requires students to design, implement and critically evaluate effective data visualisations using two different technological approaches: Microsoft Power BI and Python. The assessment focuses on the practical application of information-visualisation principles, including data preparation, visual design, interaction, audience requirements and the extraction of meaningful patterns and insights from complex datasets. Assignment 002 Coursework 2025-… Task 1 focuses on interactive visualisation using Power BI. Students work with the UK Department for Transport's Road Safety Open Data (STATS19), which contains information relating to road traffic accidents, casualties, vehicles, locations, times and contributing factors. Students may analyse one year or multiple years of data depending on their visualisation objectives. Assignment 002 Coursework 2025-… The Power BI work requires students to identify an appropriate target audience and report type, select relevant variables, clean and transform the data, develop an appropriate data model and create analytical measures using Data Analysis Expressions (DAX). The resulting dashboard should communicate the context of the data and reveal meaningful trends, patterns and insights. Assignment 002 Coursework 2025-… Task 2 requires students to develop visualisations programmatically using Python, with Jupyter Notebook recommended as the implementation environment. Students independently select a real-world, publicly available dataset containing at least 10,000 observations and more than five variables. Unlike Task 1, visualisation tools that automatically construct visualisations, such as Tableau or Power BI, cannot be used for this component because programming is an explicit requirement. Assignment 002 Coursework 2025-… Assignment 002 Coursework 2025-… The submitted Jupyter Notebook should operate as an educational technical report explaining the selected dataset, preprocessing procedures, visualisation choices and resulting insights. Students are expected to justify their visualisation techniques, critically evaluate findings and discuss challenges encountered during development. The textual content of the notebook is limited to 1,500 words, excluding code and visualisations. Assignment 002 Coursework 2025-… The complete assessment contains several deliverables, including a maximum 6-minute Power BI demonstration video, a maximum 2-page Power BI report, the .pbix file, an 8-minute Python/Jupyter visualisation demonstration, the Jupyter Notebook, dataset and README file. All materials must ultimately be packaged into a single ZIP submission. Assignment 002 Coursework 2025-… Assignment 002 Coursework 2025-… Important: the brief does not specify a named referencing style or academic level, so I would select Not specified for those two portal fields rather than guessing. It also explicitly states that generative AI must not be used to create any part of the assessed submission, including code, debugging, writing, paraphrasing or bibliographies. Assignment 002 Coursework 2025-…
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Applied Data Science
500 words
Critique of a Data Science Book and Selected Chapter
This assignment requires students to critically evaluate a selected data science book and a specific chapter from that book. Students must choose one book from the list provided in the assignment, read the preface or introduction to understand the intended audience, and then select a chapter that is relevant to their interests, existing knowledge and learning objectives. The available books cover a range of data science and analytical subjects, including practical time series analysis, machine learning with Python, Python-based data science, technical analysis, Bayesian statistics and artificial intelligence applications. The main purpose of the assignment is to develop the student's ability to engage critically with technical literature rather than simply summarising its content. The critique should identify the selected book and its intended audience, clearly state the chosen chapter and explain the reasons for selecting it. Students are expected to consider what they hoped to learn from the selected material and then critically assess whether the chapter achieved these objectives. The assessment should consider the clarity, usefulness and accessibility of the material, as well as the extent to which it contributes to the student's understanding of data science concepts. Students should also discuss additional knowledge they would like to gain from the book and identify particular aspects that were either helpful or less useful. This may include the quality of explanations, examples, technical depth, practical applications, organisation of material and relevance to the student's existing knowledge. The critique should demonstrate engagement with the selected chapter and provide reasoned observations rather than simply describing what the author has written. The final submission is a 500-word critique with a permitted variation of plus or minus 10 percent, meaning the expected range is approximately 450–550 words. The text must be written as a continuous narrative and should not use subheadings for the individual assessment points. The headline should follow the format “Critique of <book title> by <book author>”, with the student's name and student ID as the subtitle. The assignment assesses both technical presentation and content, including grammar, writing style, word count, completeness, breadth and depth of the book assessment, critical analysis and evidence of engagement with the selected material. Students must submit text that can be processed by Turnitin.
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Team Research and Development
Team Research and Development – Research Question, Hypothesis and Statistical Data Analysis
This group assessment for 7COM1079 – Team Research and Development requires students to collaboratively select a dataset and conduct a structured research investigation using data analysis. The assessment is worth 40% of the module mark and requires group members to work together to develop and submit a final research report. All group members are expected to contribute equally to the work, with one designated group member submitting the final assessment on behalf of the group. The main objective of the assignment is to develop a meaningful research question and corresponding hypothesis based on a selected dataset. The research question defines a specific claim or issue that the group intends to investigate and provides the starting point for constructing and testing an evidence-based argument. The hypothesis provides a proposed explanation or prediction that can be examined through analysis of the available data. Together, the research question and hypothesis establish what evidence is required, how that evidence will be evaluated and how effectively the findings can support or challenge a particular position. The assignment permits three types of research questions. The first involves establishing a difference in means between two groups. The second involves establishing a difference in proportion between two groups. The third involves establishing a correlation between two measures. The selected research question should therefore be appropriate for the characteristics of the chosen dataset and should allow the group to perform meaningful statistical analysis. Following the selection of the dataset and formulation of the research question and hypothesis, students are expected to produce appropriate data visualisations and statistical analysis. The analysis should provide evidence relevant to the research question and allow the proposed hypothesis to be examined systematically. The resulting findings should be interpreted in relation to the original research question and hypothesis rather than simply presenting numerical results. The assignment provides a Microsoft Word final report template containing the required table of contents, chapter and subchapter names and explanations of the expected content. Students are instructed to download and use this template when preparing their final report. Work produced by individual students during their first assignment may be incorporated where the student examined the same dataset allocated to the group, provided the material is appropriately incorporated into the group submission. All italicised instructional text in the template must be removed before submission. The completed amended template and the group's dataset file must be submitted through Canvas. Acceptable submission formats include PDF, DOC, DOCX, CSV and XLS. The assignment has a late-submission penalty, and the submission deadline is stated as being available in the assignment specification on Canvas. Assessment is based on the criteria provided in the module rubric. The rubric is used to assess the group's work, with group members initially receiving the same mark, although peer review may be taken into account. The assignment therefore combines collaborative research, statistical reasoning, data visualisation, analytical interpretation and academic report writing. The assignment instructions explicitly state that students should not use AI for this assessment. The module team also reserves the right to arrange a viva if academic misconduct is suspected. The final report should therefore represent the group's own research, analysis, interpretation and contribution.
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Principles of Data Science
3,000 words
Principles of Data Science – Predictive Modelling and Data Analysis
This individual assessment for the Principles of Data Science module requires students to select, apply and critically evaluate data science methods, tools and techniques using one of three provided datasets and its associated scenario. The main assessment takes the form of a 3,000-word report in which students explore their chosen dataset, identify an appropriate predictive modelling approach, build and evaluate models, interpret the findings and critically reflect on the overall process and outcomes. The assessment addresses the principles and foundations of data science, statistical methods, data preparation, visualisation, predictive modelling, decision making and the critical evaluation of data science techniques and tools. Students begin by exploring the selected dataset to understand its structure, characteristics and limitations. Although the supplied datasets have already been cleaned, students may undertake additional data preparation or transformation where necessary. Any preprocessing decisions must be justified in relation to the requirements of the selected analytical methods. Feature selection should also be considered as part of preparing the data for model development. The assessment requires students to identify suitable forms of analysis for the selected scenario and justify their choice of methods. At least two different techniques must be used to develop models with predictive capacity for the response variable in the chosen dataset. The models must be trained and tested consistently, using the same training and test datasets so that their performance can be compared fairly. Where appropriate, students should also provide insight into feature importance and explain the contribution of relevant variables to predictive performance. Model performance must be evaluated using suitable metrics, followed by a clear description of the findings and recommendations appropriate for the intended audience. The report should document the complete analytical workflow, including data exploration, preprocessing, feature selection, model development, testing and evaluation. Students are expected to explain and justify the decisions made throughout the process rather than simply presenting code or model results. The assessment also requires students to demonstrate practical proficiency in data science tools and techniques. The brief expects the use of R for completing the assignment and requires evidence of important elements of the code, although the complete code does not need to be submitted. Data visualisation must be used to support the written discussion and communicate relevant findings effectively. The assessment is evaluated across theoretical knowledge and method selection, data exploration and processing, technical application and model evaluation, communication of findings, and overall presentation and referencing. The assessment therefore combines technical implementation with critical analysis, requiring students to explain why particular methods were selected, evaluate their effectiveness and consider the limitations and implications of the resulting findings. A separate second assessment component accompanies the written report. This component requires a presentation of the key findings from the written work using a maximum of five slides and a presentation duration of no more than seven minutes. It should summarise the dataset, methods, key findings and project outcomes while providing critical reflective commentary on lessons learned, factors affecting success and potential real-world applications.
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Machine Learning and Big Data
2,000 words
Machine Learning for Big Data
This assignment for the Machine Learning and Big Data module requires students to produce a 2,000-word individual report demonstrating their understanding and practical application of machine learning techniques to big data. The assessment is worth 15 credits and is structured around five interconnected areas: data in big data, machine learning architecture, model deployment, model evaluation, and the machine learning lifecycle. The first part focuses on identifying and evaluating suitable datasets for a selected big data topic and determining whether the datasets are appropriate for the intended machine learning application. Students are expected to examine the characteristics of their data and apply appropriate pre-processing approaches, including consideration of attribute selection and data preparation. The second part addresses machine learning modelling architecture. Students must develop an appropriate architecture for their big data application and may compare alternative architectural approaches. The report should explain the selected machine learning techniques and demonstrate how they operate as part of the proposed system. Practical considerations such as performance, scalability, fault tolerance, technology usage and reliability should also be considered. The third part requires students to implement and deploy the proposed data and machine learning model. This includes testing, visualising and evaluating the resulting outcomes. The fourth part requires critical evaluation of the dataset selection, modelling design, implementation and application, including assessment of whether the selected machine learning techniques are appropriate for the intended purpose. The final part focuses on the complete project lifecycle. Students are expected to critically reflect on the work undertaken, identify what they have learned, evaluate the development process and explain how the machine learning application could be improved in a future implementation. The assessment develops five learning outcomes covering big data sources and applications, machine learning techniques, practical application of machine learning tools, critical evaluation of techniques and tools, and the ability to follow a complete big data analysis lifecycle. The marking criteria allocate 20% to each of these five areas. The assignment is submitted as an individual written report. The brief states that Microsoft Word should be used rather than PDF and requires students to acknowledge sources and any AI tools used in accordance with the stated AI policy.
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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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Consultancy Project
1,500 words
Consultancy Project Proposal
This assessment requires students to develop a professional Consultancy Project Proposal addressing a current organisational issue or business challenge. The assessment consists of a 1,500-word individual project proposal worth 80% of the module grade and an A4 Consultancy Project Proposal Digital Poster worth 20%. The proposal should provide an in-depth analysis of a selected business or management issue and establish a comprehensive research methodology for investigating the identified challenge. The assignment requires students to select an existing organisation and critically examine between one and three current and ongoing organisational challenges. The discussion should be evidence-based and supported by relevant, recent and credible sources, such as academic journals, company reports and other appropriate research. Students are encouraged to establish links between the selected challenges, current affairs, news events and the United Nations Sustainable Development Goals where relevant. The proposal must establish a clear research aim and three research objectives connected to the selected organisational challenges. Students must demonstrate awareness and application of appropriate research methodologies, including consideration of qualitative and quantitative approaches. The assessment also encourages the development of one or two concise research questions and requires students to demonstrate how the selected research methods can be applied to investigate the identified challenges. Students must also develop a feasible research plan and discuss ethical considerations, particularly those relating to secondary data collection. The digital poster should communicate the research plan clearly and demonstrate data analysis and data presentation skills through appropriate graphs, charts and tables. The final proposal should critically organise and synthesise information, provide a clear conclusion, and maintain professional academic standards. The recommended structure consists of a cover page, table of contents, list of abbreviations where appropriate, introduction, critical discussion of challenges, research aim and objectives, application of research methodologies, ethical considerations, research plan, critical organisation and synthesis of information, conclusion, references, the Consultancy Project Proposal Digital Poster and appendices where required. The main proposal has a 1,500-word limit, while the digital poster should not exceed one A4 page. The assignment requires Harvard referencing and the report should be written in the third person.
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Managing Customer Experience
1,000 words
Customer Experience Strategy Report
This formative assessment for the Managing Customer Experience module requires students to prepare a 1,000-word business report analysing the customer experience (CX) strategy of one selected company in one specific country where the company operates. The report is written from the perspective of a customer experience consultant and is intended for the Board of Directors of the chosen organisation. Students must apply relevant customer experience concepts, academic literature and practical examples to evaluate how the organisation creates value for its customers and supports business success. Students must select one company from the organisations provided in the assessment brief: H&M, Chen One, or M&S. The selected company and the specific country being analysed must be clearly identified in the report. The first part of the report requires an explanation of the concept of customer experience and an evaluation of its importance to the success of the chosen brand. This includes considering the role of CX, mutual benefits for the organisation and customers, the impact of CX on business and financial performance, and examples of how the brand has used customer experience to achieve competitive advantage. The second part focuses on how the selected brand delivers a seamless omnichannel customer experience. Students must discuss the organisation's strategy for creating a seamless customer journey, including the role of customer personas and customer journey maps. A detailed customer persona should be developed based on research and data analysis, covering the profile, goals, pain-points and motivations of the selected customer. Students must also create a customer journey map covering stages, needs, activities, feelings, pain-points and opportunities for improvement. The report should critically evaluate how effectively the chosen organisation creates a seamless customer journey and identify potential areas for improvement. The assessment is aligned with learning outcomes concerning the importance of customer experience to business success and the evaluation of seamless omnichannel customer journeys. The report should use an academic style, avoid first-person writing, and appropriately reference figures, diagrams and independent research using the Harvard Referencing System.
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Understanding Patient Data
Data Presentation: NJ OSME Drug-Related Deaths in NJ Counties
This assignment focuses on data presentation and management using drug-related death data from New Jersey counties. The assignment is part of the Understanding Patient Data course and develops practical skills in inspecting, cleaning, organizing, analyzing, and presenting patient-related datasets using Microsoft Excel. Students are required to use the data provided in the file “5.3a Chart on Drug Deaths by NJ County (2015)” and input the county-level information into an Excel spreadsheet. The assignment requires students to inspect and clean the data where necessary, including removing, imputing, and explaining incomplete data entries. Students must also organize and sort the data and obtain descriptive statistics for heroin drug-related deaths and a second variable of their choice. The analysis includes creating descriptive statistics for four variables and comparing the descriptive statistics of heroin with a selected variable. Students must create a copy of the original dataset on a separate worksheet, sort total deaths from largest to smallest, and create a 2-D bar chart and scatterplot. The charts are then used to develop interpretive statements about heroin-related deaths based on the combined analysis of the visualizations. The assignment also requires students to use descriptive statistics to compare the central tendency of three specified variables: Cocaine, Fentanyl, and Oxycodone. A separate worksheet must contain a key or log explaining variable names, abbreviations, and terms used in the dataset. The assignment develops practical skills in Excel-based healthcare data analysis, descriptive statistics, data visualization, interpretation of patient data, and data management. The grading criteria include data input and cleaning, descriptive statistics, sorted data, bar chart creation, scatterplot presentation, and interpretive analysis. The completed assignment must be submitted electronically in Microsoft Excel (.xls or .xlsx) format.
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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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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assessment requires students to develop a comprehensive analytical solution to a real-world healthcare prediction problem using the WiDS Datathon 2025 Health Outcomes Prediction Dataset. The dataset contains socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents, with the principal objective of developing predictive models for ADHD diagnosis. The assessment is designed to demonstrate the complete data-science lifecycle, from data preparation and exploratory analysis through predictive modelling, interpretation and evidence-based recommendations. Students begin by exploring the dataset's features, data types and distributions before addressing missing values, outliers and other inconsistencies. Appropriate feature engineering should be undertaken where necessary, followed by Exploratory Data Analysis (EDA) using relevant visualisations to identify relationships, patterns and correlations within the data. Students with limited computational resources may use a representative subset, provided that the sampling method preserves the integrity and distribution of the original dataset and is clearly justified. A major component of the assignment involves developing and comparing at least three classification models. Appropriate techniques may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Model performance should be evaluated using measures including accuracy, precision, recall, F1-score and ROC-AUC, allowing students to identify the strongest-performing model through systematic comparison. The assessment also requires model interpretation and explainability. Students should explain the results of the selected model and may apply techniques such as SHAP or LIME to investigate feature importance and individual predictions. A feature-importance visualisation must be produced, and the most influential variables should inform practical recommendations for healthcare professionals regarding the potential use of predictive modelling in supporting earlier ADHD diagnosis and intervention. Overall, the assignment integrates data cleaning, exploratory analytics, predictive modelling, model comparison, explainable AI and research-informed healthcare recommendations. Students must submit a comprehensive report of no more than 2,500 words, alongside a Jupyter Notebook containing the implementation and outputs. The report must use Harvard referencing, with appropriate academic research integrated into the analysis, recommendations and conclusion.
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Machine Learning / 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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Operations and Supply Chain Management
1,987 words
Supply Chain Analytics and Quantitative Data Analysis for Organisational Decision-Making
This postgraduate individual report focuses on the application of quantitative data analysis to Operations, Logistics and Supply Chain Management decision-making. Students are required to select an organisation from the private, public or third sector and investigate a relevant operational or supply-chain issue using quantitative data. The purpose is to demonstrate how data can be collected, prepared, analysed and interpreted to generate evidence-based insights that may support managerial decision-making. The selected dataset must relate to the organisation's operations or supply chain and may include variables such as revenues, product orders, sales, transportation costs, procurement expenditure, inventory levels or other appropriate quantitative measures. The dataset must contain at least 60 observations, and the analysis must involve at least two variables. Data may be obtained directly from organisations or from recognised secondary-data platforms and databases. The assessment consists of two equally weighted components. Part A – Motivation and Justification for the Analysis requires students to formulate relevant analytical questions and explain their practical importance by linking them to Operations and Supply Chain Management theory and business practice. Appropriate academic, industry and practitioner evidence should be used to justify the selected issue. Research questions may also be translated into testable hypotheses where appropriate. Part B – Execution of the Analysis requires students to answer the identified questions through appropriate statistical techniques. Potential methods include tables, charts, summary statistics, t-tests and regression analysis. Data may first need to be cleaned, transformed and structured before analysis. The results must then be interpreted clearly for a managerial audience such as the organisation's board, owner or CEO. The statistical analysis is expected to be conducted using Stata, with all data-cleaning, manipulation and analytical commands recorded in a reproducible do-file. The report must also demonstrate explicit links between theory and practice and contain a suitable mixture of academic and professional evidence, including at least five academic journal articles. Harvard referencing is required throughout. The resulting work demonstrates practical competence in business analytics, statistical interpretation, supply-chain decision support, reproducible analysis and evidence-based managerial communication. Overview word count: approximately 370 words.
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Machine Learning / Artificial Intelligence and Data Science
1,011 words
End-to-End Machine Learning Model Development, Testing and Evaluation
This Level 7 Machine Learning and Intelligent Agents assignment requires students to develop and document an end-to-end machine-learning solution, covering the complete process from data preparation through model training, tuning, testing and evaluation. Students independently select a suitable dataset or scenario, formulate an appropriate research question and determine whether the problem should be addressed using supervised learning, unsupervised learning or reinforcement learning. Appropriate machine-learning algorithms must then be implemented to create a model capable of being trained and objectively tested. The assessment encourages the use of a structured machine-learning development methodology. Students are expected to explain the selected scenario and data source, perform suitable data preparation and Exploratory Data Analysis (EDA), and provide a reasoned justification for the machine-learning methods selected. The development process should demonstrate how the chosen algorithms are trained and fine-tuned before their performance is evaluated using established and relevant metrics. Published academic research should be incorporated to justify methodological choices and support the interpretation of results. The technical work is normally documented within a Jupyter Notebook, combining Markdown explanations with executable code cells. Alternatively, the report may be produced in Microsoft Word provided that the Python implementation is included. The assignment therefore assesses both conceptual understanding and practical programming competence. Students must demonstrate an ability to identify the performance of machine-learning algorithms, implement machine-learning techniques using an object-oriented programming language, and evaluate which algorithms are appropriate for a particular analytical brief. Assessment places particular emphasis on three areas: the Introduction, the Machine Learning Process, and the Evaluation of Model Performance. The marking criteria reward strong understanding of machine-learning concepts, a functioning and thoroughly tested implementation, appropriate selection of algorithms, and critical evaluation of the final solution. At the highest achievement level, implementations are expected to work without exception, satisfy the required functionality, demonstrate comprehensive testing and extend beyond the basic requirements. Overall, the assignment combines research-question formulation, data analysis, algorithm selection, machine-learning implementation, model optimisation and evidence-based evaluation within a reproducible technical workflow. All academic sources and supporting material must be presented using Harvard referencing.
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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assignment focuses on developing a comprehensive analytical solution to a real-world healthcare prediction problem. Using the WiDS Datathon 2025 Health Outcomes Prediction Dataset, students are required to analyse complex and high-dimensional healthcare data containing socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents. The principal predictive objective is to determine ADHD diagnosis from the available features. Students may use a representative subset of the dataset where computational resources are limited, provided that the sampling approach maintains the integrity and distribution of the original data and is appropriately justified. The assessment requires a complete data-science workflow beginning with data understanding and preprocessing. Students investigate the dataset's features, data types and distributions before addressing missing values, outliers and inconsistencies. Appropriate feature engineering should then be undertaken where it can improve the predictive capability of the models. Exploratory Data Analysis is used to identify important patterns, relationships and correlations, supported by relevant visualisations that communicate meaningful insights. A major component of the work involves the development and comparison of at least three classification models for predicting ADHD diagnosis. Suitable approaches may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Models are evaluated using performance measures including accuracy, precision, recall, F1-score and ROC-AUC, after which the most effective model is selected based on the evidence obtained. The assessment also places substantial emphasis on model interpretation and explainability. Students must interpret the selected model and may use approaches such as SHAP or LIME to explain feature importance and individual predictions. A feature-importance visualisation is required, and the most influential variables should inform practical recommendations. The final section translates analytical findings into recommendations for healthcare professionals, considering how predictive modelling could assist early ADHD diagnosis and intervention. Research literature must be integrated into the recommendations and conclusion. The assessment therefore combines preprocessing, exploratory analysis, predictive modelling, explainable AI and evidence-based healthcare decision-making within a single applied data-science project. The required report is a maximum of 2,500 words, with code, supplementary charts and tables permitted in appendices. A Jupyter Notebook containing the implementation and outputs is also required. Harvard referencing must be used throughout.
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People Analytics / Human Resource Management
2,003 words
Principles of People Analytics and Evidence-Based Decision Making
This CIPD Level 3 assessment introduces the principles of people analytics and evidence-based decision making within the people profession. It focuses on how data, professional expertise, research evidence and stakeholder information can be used to diagnose organisational issues, support rational decision making and improve people practices. The unit emphasises the practical application of analytics rather than data collection alone. The assessment uses a recruitment scenario in which the learner applies for the position of People Analytics Administrator at Company X, an organisation providing HR and people-management solutions. Learners complete an assessment pack containing eight questions designed to demonstrate understanding of how analytics can support business and people decisions. The written component requires learners to explain evidence-based practice and demonstrate how it could be applied in an organisational context. Other areas include the importance of accurate data in diagnosing problems, different forms of data measurement, the role of organisational policies and procedures in decision making, and how people professionals create value for employees, organisations and wider stakeholders. Learners must also consider how a people analytics professional can remain customer focused and standards driven. The practical analytics element uses employee overtime data from Blue Mountain Patisserie. Learners calculate average overtime for individual employees, express overtime as a percentage of normal working hours, interpret patterns within the data and identify potential organisational problems and possible solutions. Findings must then be communicated using at least two different diagrammatic formats, such as bar graphs, pie charts or line graphs. The required written evidence is approximately 1,500 words for Questions 1–6 and 500 words for Question 7, giving approximately 2,000 words in total, with the visualisations excluded from the word count.
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Business Management / Business Consultancy / Supply Chain Management
5,000 words
Digital Transformation for Sustainable Supply Chain Transparency at Unilever: Blockchain, IoT and AI
This MSc Business Consultancy Project requires students to undertake an evidence-based consultancy investigation addressing a strategically significant business problem within a selected organisation. The assessment is designed to replicate professional consultancy practice by requiring students to define a focused organisational challenge, critically analyse secondary evidence, apply relevant management frameworks, evaluate stakeholder implications and develop practical recommendations that create value for the client organisation. The final submission is a 5,000-word consultancy report, including a 500-word employability reflection. The reference project examines Unilever Plc and focuses on the challenge of improving transparency and traceability across its complex global supply chain. Particular attention is given to the potential application of Blockchain, Internet of Things (IoT) and Artificial Intelligence (AI) to support real-time traceability, predictive analytics, ethical sourcing, operational efficiency and sustainability performance. The project considers how digital transformation could support Unilever's sustainability objectives while responding to growing regulatory, environmental and stakeholder pressures. The consultancy report requires a structured analysis consisting of an executive summary, introduction, company/client overview, clearly defined business problem and consultancy focus, and detailed stakeholder analysis. Students then undertake an extensive data analysis and framework application section using two or three relevant theoretical models alongside credible secondary evidence, industry reports, company data, tables, charts or Excel outputs. Findings should be interpreted critically and linked back to appropriate strategic or management frameworks while incorporating ethical and sustainability considerations. The project concludes with three prioritised, actionable and evidence-based recommendations, including consideration of implementation risks, barriers and anticipated benefits. Students must also critically reflect on the employability skills developed through the consultancy project, including research, analysis, problem-solving, project management, communication, professional behaviour, ethical awareness and future career development. All academic and professional evidence must be cited using the Harvard Referencing System, with emphasis on credible and current sources.
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Computing / Artificial Intelligence / Digital Transformation / Management Consultancy
7,000 words
AI Readiness and Programme Adoption Strategy for the NewFutures: AI Programme at Northumbria University London
This postgraduate consultancy project focuses on developing an AI readiness, skills-development and programme-adoption strategy for students and recent alumni at Northumbria University London. The project supports the university’s participation in NewFutures: AI, a funded AI skills and career-readiness programme offering a four-week online course covering responsible AI foundations and specialist pathways in Marketing and Communications, Finance and Accounting, Business Operations and Logistics, Administration, and ICT and Technical Support. The central consultancy challenge is to understand the AI literacy, confidence, readiness and training needs of Northumbria University London students and alumni and translate that evidence into a practical implementation and outreach strategy. The client aims to reach approximately 12,000 students and recent alumni and support a target of 6,000 LMS registrations during the 2026–2027 programme period. The project requires primary and secondary research into AI readiness, demand for different AI-skilling pathways and barriers to participation such as awareness, time, perceived value and accessibility. The consultancy team is also expected to benchmark comparable initiatives and use research evidence to develop recommendations appropriate to different academic disciplines and student and alumni groups. The implementation component focuses on designing an evidence-based outreach and adoption campaign, including appropriate communication channels, messaging, timing, incentives, faculty engagement and stakeholder participation. Recommended channels may include email campaigns, newsletters, social media, student services, events, learning platforms and alumni communications. The project also requires an implementation timeline and indicative budget for the 2026–2027 programme period. The wider assessment develops professional consultancy capability through business and requirements analysis, research methodology, ethical research practice, practical implementation, testing and strategic recommendations. The project charter additionally establishes milestones for research design, data collection, analysis, report development, review and presentation, together with defined responsibilities for project management, data analysis, AI expertise and stakeholder communication. The individual component complements the consultancy work through critical reflection on personal contribution, skills development, decision-making, problem-solving, communication, collaboration, technical capability, innovation and continuous professional development.
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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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Assignment 2 - Individual project Image segmentation
Assignment tasks This assignment will focus on Image Segmentation using the ADE20K dataset. This is an individual assignment where each student will produce a report on the data analysis they will perform. You are encouraged to utilise Google Colab for the coding part of your assignment. https://herts.instructure.com/courses/129101/assignments/406384 1/86/17/26, 12:10 PM Assignment 2 - Individual project - Image segmentation - 25% You will explain and discuss the data processing, the method(s) you make use of and elaborate the outcome. You will work on the ADE20K dataset (explained below in more detail) to research viable models to train, discuss different approaches to explore and visualise the data (i.e., perform EDA), build a tool to pre-process the dataset, and customise your chosen model(s) to improve performance. You will produce a code that does semantic segmentation of the 4 classes targeted in this assignment: person, car, book, airplane. In more detail, your model(s) should identify which of these 4 classes the region of the image corresponds to, and should be applicable to any unlabelled image. To be clear: doing only binary segmentation (i.e. any class vs background) will result in a very large penalty, as you will be considered not to have done the required task. You may use more than one model, but one has to be trained partially or fully by you. Should you use more than one, you are encouraged to compare your main trained model with one or more pre-trained models.
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