Subject Code: DATA4100 · Four Credit Points
Hands-on experience with industry-leading tools — Tableau and Power BI — paired with advanced analysis techniques including correlation, charting, segmentation, and clustering. Students also develop storytelling and creative communication skills to present complex visualisations to diverse stakeholders.
| Week | Topic |
|---|---|
| Week 1 | Interactive and real-time visualisation for business insight |
| Week 2 | Statistical methods for summarising and visualising data |
| Week 3 | Performing analytics, UX and report writing |
| Week 4 | IT systems to assist data visualisation — immersive environments & database models |
| Week 5 | Communicating visualisations and storytelling · A1 Due (Infographic Flyer) |
| Week 6 | Making visualisations more effective |
| Week 7 | Revision Week |
| Week 8 | Assessment 2 in class · A2 Due (Group Project Slides) |
| Week 9 | Advanced visualisations and comparison of analytics & visualisation platforms |
| Week 10 | Transforming data and drill-through dashboards |
| Week 11 | Creating visualisations in Python; comparing visualisation platforms |
| Week 12 | Modelling and dashboards in Python |
| Week 13 | Written report due · A3 Due (Individual Report) |
A 2-page infographic flyer analysing retail consumer behaviour from 2022–2025. Minimum four visualisations required. Submitted as .docx.
10-slide group presentation. Develop 3 hypotheses, test them with data, build a narrative story, and recommend business next steps. Completed in-class.
1,500-word individual report covering customer behaviour, fleet insights, 2026 demand forecasting, and future IT/AI applications.
| Section | Marks | Fail (0–49%) | Pass (50–64%) | Credit (65–74%) | Distinction (75–84%) | HD (85–100%) |
|---|---|---|---|---|---|---|
| Part A Visualisation |
10 | Missing or confusing charts | Basic, partially relevant | Accurate, well-labelled | Clearly present patterns & trends | Highly effective; strong visual narrative |
| Part B Insights |
10 | Not supported by data | Some key points, not justified | Clear links to data; reasonable narrative | Thoughtful, data-driven insights | Insightful, compelling, fully data-grounded |
| Part C Presentation & Design |
10 | Disorganised, poor layout | Basic legibility but cluttered | Structured; some layout attention | Strong design; adds to narrative | Professional, engaging, excellent formatting |
A2 – Group Slides (30 Marks)
| Section | Marks | HD Expectation |
|---|---|---|
| Part A Hypotheses & Insights |
15 | Insightful hypotheses; deep data-driven support |
| Part B Presentation & Storytelling |
15 | Outstanding narrative; engaging and insightful |
Groups of 3–4. Dataset distributed in class (Week 8). No verbal presentation required.
A3 – Individual Report (40 Marks)
| Section | Marks | HD Expectation |
|---|---|---|
| Part A Data Analysis & Visualisation |
15 | Highly engaging data narrative; excellent visualisations |
| Part B Insights & Recommendations |
15 | Compelling, actionable, evidence-based recommendations |
| Part C Forecasting & Future Outlook |
10 | Sophisticated AI/ML understanding; innovative strategy insights |
Same dataset as A2. 1,500 words ±10%. Forecasting 2026 rental demand required.
No Generative AI allowed. Assessment showcases individual knowledge and skills only. Unauthorised use may result in a mark of zero and/or academic misconduct proceedings.
May be used for research and content generation. All AI output must be paraphrased and referenced. Appendix required for sections >30 words drawn from AI. No copy-paste permitted.
Assessment fully integrates Generative AI. Students are taught and assessed on its use. Full referencing and an AI collaboration appendix are required.
Late Submission Penalties
| Days Late | Penalty |
|---|---|
| 1–9 days | 5% deducted per calendar day |
| 10–14 days | 50% deducted from total marks |
| After 14 days | Mark of zero (unless special consideration approved) |
Submissions within 24 hrs of deadline are considered 1 day late.
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