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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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Business Ethics / Corporate Social Responsibility
2,500 words
Success Through Business Ethics (BS846) — Individual Report: Ethical Failure in an FMCG Brand, Moral Philosophy, Decision-Making and Leadership Response
This Level 7 individual report examines a real fast-moving consumer goods brand that was found to have engaged in unethical business practices between 2000 and 2023, and works through why the failure happened rather than simply recounting what occurred. The brand is selected on the basis that sufficient documented evidence exists about both the company and its external environment — regulatory findings, investigative journalism, financial disclosures and NGO reporting — so that every analytical claim can be supported rather than assumed. The introduction establishes what business ethics means as an academic field, drawing on recognised sources, before introducing the chosen brand and setting out concisely what the unethical practices were, when they came to light and who was harmed. The first and largest analytical section applies three moral philosophical bases from the module to the case. Each is used as a lens rather than described in isolation: the utilitarian calculation the company appears to have made and where its accounting of harm was deficient, the duty-based obligations to consumers, workers or communities that were breached regardless of outcome, and the character and organisational culture questions that virtue ethics raises about how such decisions became normal internally. The evaluation is genuinely critical — it identifies where the company's conduct could be partially defended under one framework while failing badly under another, which is where the analytical marks sit. The second section takes one of the two theoretical approaches offered and applies it in depth. Where ethical decision-making is chosen, the report traces the sequence of judgements that produced the outcome, examining awareness, intent, organisational pressure and the moral intensity of the issue. Where social accounting is chosen, it assesses what the company disclosed about its social and environmental impact against what was actually occurring, and what that gap reveals about the purpose its reporting served. The third section evaluates leadership. It characterises the prevailing leadership style from the evidence and then focuses on the response once the practices were exposed — whether leaders denied, deflected, settled quietly or accepted accountability — supported by tables, figures and quoted material showing the consequences in share price, revenue, regulatory penalties and documented social or environmental harm. Three concrete recommendations follow, each derived directly from a failure identified in the preceding analysis and justified specifically for this brand rather than offered as generic good practice, with a short conclusion drawing the sections together. Harvard referencing is applied in text and in an alphabetised reference list.
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Data Management / Business Analytics
2,500 words
Data Design Management (BS514) — Data Strategy Consultancy: Relational Database Design, SQL Implementation and Pipeline Transformation
This Level 7 assessment places the writer in the role of a Data Strategy and Analytics Consultant appointed by an organisation operating in a realistic industry sector. The brief is entirely simulated, so the work sets out a defensible set of assumptions about the organisation's data environment before any design begins, and populates the resulting database with synthetic but realistic records. The deliverable is a slide deck carrying full explanatory notes, submitted as a single PDF, and weighted across three connected tasks. Task one establishes the business case. It describes how the chosen organisation currently collects, stores and uses data across customer interactions, sales transactions, operational processes and digital channels, and identifies where that fragmented picture costs the business in efficiency, resource use, retention and decision quality. A SWOT analysis benchmarks the organisation against a named real-world competitor in the same sector, drawing on publicly available market information rather than assertion. The section closes with a critical evaluation of modern relational database advancements — cloud-hosted SQL services, distributed architectures and data warehousing — assessed not in the abstract but against what each would actually change about this organisation's business model. Task two carries the heaviest weighting and is the technical core. Key business entities are identified from the scenario, a current-state data flow diagram traces how data moves from collection points through to storage and reporting with the existing ETL approach made explicit, and a future-state ER diagram is then built with full attributes, primary and foreign keys, relationships and cardinality. The design is normalised to third normal form with the decomposition reasoning shown. Implementation follows in SQL: tables created with appropriate integrity constraints, at least ten realistic sample records inserted per table, and five business questions answered through working queries — highest-performing product or campaign, average conversion by category, workload distribution across staff, accounts with overdue or pending items, and most effective service channel. Outputs accompany every script. A transformation demonstrating query optimisation is included with before-and-after samples so the improvement is evidenced rather than claimed. Task three steps back to the technology decision. Two widely used data processing platforms are compared in tabular form across integration, cleaning, transformation and automation capability, judged specifically against this organisation's constraints, with a reasoned justification for the tool finally selected. The transformed dataset is then used to answer two management-level questions — where investment should be prioritised and how retention might be improved from observed behavioural patterns — each interpreted briefly and tied back to a concrete recommendation. Slide structure follows the prescribed layout, SQL scripts sit in the notes section, and the complete script file is reproduced in the appendix.
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