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Information Visualisation / Data Analytics / Data Science
1,500 words
Information Visualisation Project Using Power BI and Python
Assignment overview — ready to paste This Information Visualisation project requires students to design, implement and critically evaluate effective data visualisations using two different technological approaches: Microsoft Power BI and Python. The assessment focuses on the practical application of information-visualisation principles, including data preparation, visual design, interaction, audience requirements and the extraction of meaningful patterns and insights from complex datasets. Assignment 002 Coursework 2025-… Task 1 focuses on interactive visualisation using Power BI. Students work with the UK Department for Transport's Road Safety Open Data (STATS19), which contains information relating to road traffic accidents, casualties, vehicles, locations, times and contributing factors. Students may analyse one year or multiple years of data depending on their visualisation objectives. Assignment 002 Coursework 2025-… The Power BI work requires students to identify an appropriate target audience and report type, select relevant variables, clean and transform the data, develop an appropriate data model and create analytical measures using Data Analysis Expressions (DAX). The resulting dashboard should communicate the context of the data and reveal meaningful trends, patterns and insights. Assignment 002 Coursework 2025-… Task 2 requires students to develop visualisations programmatically using Python, with Jupyter Notebook recommended as the implementation environment. Students independently select a real-world, publicly available dataset containing at least 10,000 observations and more than five variables. Unlike Task 1, visualisation tools that automatically construct visualisations, such as Tableau or Power BI, cannot be used for this component because programming is an explicit requirement. Assignment 002 Coursework 2025-… Assignment 002 Coursework 2025-… The submitted Jupyter Notebook should operate as an educational technical report explaining the selected dataset, preprocessing procedures, visualisation choices and resulting insights. Students are expected to justify their visualisation techniques, critically evaluate findings and discuss challenges encountered during development. The textual content of the notebook is limited to 1,500 words, excluding code and visualisations. Assignment 002 Coursework 2025-… The complete assessment contains several deliverables, including a maximum 6-minute Power BI demonstration video, a maximum 2-page Power BI report, the .pbix file, an 8-minute Python/Jupyter visualisation demonstration, the Jupyter Notebook, dataset and README file. All materials must ultimately be packaged into a single ZIP submission. Assignment 002 Coursework 2025-… Assignment 002 Coursework 2025-… Important: the brief does not specify a named referencing style or academic level, so I would select Not specified for those two portal fields rather than guessing. It also explicitly states that generative AI must not be used to create any part of the assessed submission, including code, debugging, writing, paraphrasing or bibliographies. Assignment 002 Coursework 2025-…
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Data 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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