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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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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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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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Data Visualisation / Business Intelligence
2,500 words
Data Visualisation (BS666) — Business Analyst Client Report: Dashboard Development, Tool Evaluation and Accessibility in Power BI and Tableau
This Level 7 resit assessment takes the form of a single client-facing report written from the position of a qualified business analyst. Rather than assembling semester activities, it asks for one sustained piece of analytical writing that carries a business case from raw open data through to a defended set of visualisations and the decisions they support. The report opens by identifying a data source drawn from an approved open repository and setting out the business problem the client faces. Provenance, structure, granularity and known limitations of the dataset are examined honestly at this stage, since every downstream claim rests on them, and all data is referenced in full — including data appearing inside charts, as the brief specifically requires. The main body works through three connected strands of critical evaluation. The first traces the development sequence of the visualisations themselves: how the data was prepared and cleaned, why particular chart types were selected over the alternatives available, how layout and interactivity were arranged for the intended audience, and how the design changed across iterations once weaknesses became visible. At least three completed visualisations are then evaluated individually and critically — what each one reveals, where each falls short, and what a reader could reasonably conclude from it. The second strand compares Power BI and Tableau as working environments for this specific dataset rather than in the abstract. Data connectivity, transformation and calculation capability, visual flexibility, publishing and sharing, licensing and governance are all weighed against what the client actually needs, with the practical friction encountered during the build reported rather than smoothed over. The third strand addresses accessibility and cognitive processing. It examines how each platform handles colour contrast and colour-vision deficiency, text alternatives, keyboard navigation and screen reader support, and then moves into the perceptual side — pre-attentive attributes, data-ink economy, chart junk, working memory limits and how visual encoding choices either reduce or inflate the effort a reader must spend to extract meaning. The argument connects this directly to decision quality in organisations with diverse analytical literacy. Findings are reported at length and translated into concrete business implications, with a conclusion that states what the client should do and on what evidence. The submission follows the prescribed structure throughout: title page, executive summary, contents, introduction, business problem, main evaluative section, findings, conclusion, Harvard reference list and appendices, presented as a single file for Turnitin.
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Data Visualisation / Business Intelligence
2,500 words
Data Visualisation (BS664) — Portfolio: Tableau and Power BI Dashboard Development for Evidence-Based Decision-Making
This Level 7 portfolio accumulates work across a full semester of data visualisation study and is submitted as one continuous document combining reflective writing, tool evaluation and an applied dashboard project. The emphasis throughout falls on interpretative rigour — showing not just that a visualisation was built, but why each design decision serves the decision-maker who has to act on it. The first activity documents sustained critical participation across the module's weekly discussion topics. Five or more substantive responses are evidenced with dated screenshots, each paired with the corresponding workshop date, and each grounded in both academic literature and practitioner sources rather than opinion alone. Live URLs to the Power BI and Tableau dashboards built during the module are included, and the activity closes with a consolidated review of how understanding developed across the units. The second activity covers formal training completion in both platforms, with certificates and badges reproduced, and then turns to accessibility. It examines the visualisations produced during that training against accessibility principles — colour contrast and colour-blind safe palettes, text alternatives, chart type legibility, cognitive load — and argues why accessibility is not a compliance afterthought but a determinant of whether a dashboard actually informs decisions inside a global organisation with a diverse user base. The third activity is a structured comparison of Tableau and Power BI, conducted through the evaluative frameworks introduced in workshops rather than a feature checklist. Benefits and limitations are weighed across data connectivity, calculation capability, visual flexibility, licensing and cost, collaboration and governance, and organisational fit. The section ends with a reasoned recommendation of one platform for the applied work that follows, with the trade-offs of that choice acknowledged openly. The fourth and largest activity is the applied project. A dataset is selected from an approved open source and its provenance, quality and limitations discussed. The target business, its stakeholder groups and the varying analytical literacy of the intended audience are analysed, because these determine what the dashboard must show and how plainly. The report then walks through the full development sequence — data preparation and cleaning, chart selection and justification, layout and interactivity, iteration in response to identified weaknesses — with screenshots evidencing each stage. Findings are reported and translated into specific, evidence-based decisions the business could take on the strength of them. The submission follows the prescribed structure with title page, contents, introduction, business problem, main analysis, findings, Harvard reference list and appendices, presented as a single file.
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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 / 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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