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Real-time Database Sync
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 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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