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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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Data Mining / Data Science
Customer Service Escalation Risk Analytics Using Data Mining and Machine Learning
This advanced Data Mining assessment applies the Knowledge Discovery in Databases (KDD) process to a realistic customer-service risk scenario. Acting as a Data Scientist, students analyse a historical Customer Service Escalation Risk dataset to help an organisation identify early indicators of dissatisfaction and operational bottlenecks that may lead to serious or legal customer escalations. The resulting analysis is intended to support strategic decisions relating to staffing, employee training, customer-journey improvement and escalation prevention. CMP-7023B_Assessement_2 (2) The dataset incorporates customer demographics, account characteristics, communication channels, issue categories, operational measures such as waiting times, transfers and SLA breaches, behavioural indicators including sentiment and response delays, and commercial variables such as monthly fees and contract value. The target variable, escalation_level, contains four categories: No escalation, Minor escalation, Serious escalation and Legal escalation. CMP-7023B_Assessement_2 (2) Students begin with data exploration and visualisation, producing appropriate descriptive statistics and identifying patterns, distributions and potential data-quality concerns. They then perform data cleansing, transformation, feature engineering and preprocessing. Variables that may introduce leakage or unreliable predictions because of their meaning, timing or quality must be critically assessed and justified. CMP-7023B_Assessement_2 (2) The supervised-learning stage requires students to develop, tune and compare predictive models using techniques such as k-nearest neighbours, Decision Trees, Support Vector Machines, ensemble methods or neural networks. Appropriate multiclass evaluation metrics must be used, alongside interpretation of influential variables and model behaviour. CMP-7023B_Assessement_2 (2) The assessment also includes unsupervised learning, requiring comparison of clustering methods such as K-Means and hierarchical clustering after removal of the target variable. Students may apply encoding, normalisation and dimensionality-reduction methods such as PCA or t-SNE and must interpret how the resulting clusters relate to escalation behaviour. CMP-7023B_Assessement_2 (2) Overall, the project assesses independent analytical judgement, modelling justification, comparative evaluation and clear communication of actionable findings for both technical and executive audiences. CMP-7023B_Assessement_2 (2) Overview word count: approximately 340 words. AI-use note: AI tools may only assist with small, specific code snippets. Any AI-generated code must be clearly acknowledged and cited, while the analysis, coding decisions, interpretation and final evaluation must remain the student's own work. CMP-7023B_Assessement_2 (2)
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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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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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Data Mining / Data Science
Customer Service Escalation Risk Analytics Using Data Mining and Machine Learning
This advanced Data Mining assessment applies the Knowledge Discovery in Databases (KDD) process to a real-world customer-service analytics scenario. Acting as a Data Scientist, students analyse a historical Customer Service Escalation Risk dataset to help an organisation reduce serious and legal customer escalations by identifying early signs of dissatisfaction, service bottlenecks and operational risk. The findings are intended to support business decisions relating to staffing, employee training, customer-journey improvement and escalation prevention. The dataset contains information covering customer demographics, account characteristics, communication channels, issue categories, operational measures such as wait times, transfers and SLA breaches, behavioural indicators including sentiment and response delays, and commercial variables such as monthly fees and contract value. The target variable contains four escalation outcomes: No escalation, Minor escalation, Serious escalation and Legal escalation. The first stage requires data exploration, visualisation and summary, including examination of variable distributions, dataset structure, descriptive characteristics and potential data-quality issues. Students then perform appropriate data cleaning, transformation, feature engineering and preprocessing. Particular attention must be given to variables that could introduce prediction leakage because of their meaning, timing or reliability. The supervised-learning component requires development and tuning of predictive models using suitable techniques such as k-nearest neighbours, Decision Trees, Support Vector Machines, ensembles or neural networks. Models must be evaluated using appropriate multiclass metrics and compared systematically, with interpretation of influential features and model behaviour. The assessment also requires unsupervised learning. After removing the escalation target, students apply and compare clustering approaches such as K-Means and hierarchical clustering. Appropriate preprocessing, encoding, normalisation or dimensionality reduction may be used, with visualisations such as PCA, t-SNE or scatterplots used to explore cluster structure and its relationship with escalation behaviour. Overall, the project assesses the student's ability to independently design a coherent KDD workflow, justify analytical decisions, compare alternative modelling approaches and communicate actionable findings to both technical and executive audiences. Overview word count: approximately 350 words. AI-use note: the brief permits AI tools only to assist with small, specific code snippets. Any AI-generated code must be clearly acknowledged and cited, while the submitted coding, analysis, interpretation and decision-making must remain the student's own work.
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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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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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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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