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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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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