All page references below are one-based PDF positions. Notebook sections use the visible headings rather than internal cell numbers. Course examples are synthetic. Added practice questions and visualisations are clearly labelled; they are not official exam questions.
| Label | Uploaded source | Main material used |
|---|---|---|
| W1 | week-1-lecture.pdf, 60 pages | Framing, claims, rows, measurement, sampling, proxies, joins, ethics and assessment |
| L1 | Lab_01_Rows_Joins_Missingness_Student(1).ipynb, 23 cells | Two-table merge, pass-rate grouping, denominator and reasoning rubric |
| W2 | Week_2_Describing_Data_SLIDES.pdf, 60 pages | Centre, spread, shape, quartiles, normal approximation and z-scores |
| L2 | Lab_02_Describing_Data_Student.ipynb, 34 cells | Personalised 240-student case, hand calculations, conventions, plots and transfer tasks |
| W3 | data-science-fundamentals-week-3.pdf, 56 pages | Quality, duplicate evidence, missingness, sensitivity, outliers and audit |
| W4 | data-science-fundamentals-week-4(1).pdf, 43 pages | Transformations, encoding, timing, pipelines, leakage and honest error |
| L4 | data-science-fundamentals-week-4-lab-instructions.pdf, 7 pages | Seven fixed practical tasks and the combined nearest-student exercise |
| W5 | data-science-fundamentals-week-5(1).pdf, 53 pages | EDA, plots, conditional rates, weighting, confounding and Simpson's paradox |
Topic-to-source locator
| Topic | Where to return |
|---|---|
| Applied exam format and weights | W1 pp. 19-20 |
| Question framing and claim ladder | W1 pp. 22-29 |
| Lifecycle, measurement and rows | W1 pp. 30-39 |
| Frames, proxies, joins and ethics | W1 pp. 42-53; L1 Parts 1-5 |
| Summary choice and hand calculations | W2 pp. 10-30; L2 Sections 2-3 |
| Distribution, bins and normal approximation | W2 pp. 33-43; L2 Sections 4-6 |
| Z-score reference, rank and specification | W2 pp. 45-50; L2 Section 7 |
| Typical use versus total workload | W2 p. 58; L2 Section 8 |
| Quality and duplicate reasoning | W3 pp. 6-21 |
| Missingness and representation | W3 pp. 22-39,45 |
| Outliers, logs and checks | W3 pp. 40-54 |
| Distance, scaling and log | W4 pp. 4-16; L4 Tasks 1-2 |
| Encoding and engineered features | W4 pp. 17-22; L4 Task 3 |
| Availability and leakage | W4 pp. 24-28,33-35; L4 Task 5 |
| Pipeline vectors and evaluation | W4 pp. 29-40; L4 Tasks 4,6-7 |
| EDA, rates, plots and unknown outcomes | W5 pp. 5-24 |
| Support example and common-mix rates | W5 pp. 26-37 |
| Course mix, causation and test boundary | W5 pp. 38-51,53 |
Final study principle: calculations earn meaning from their population, units, timing, denominator and evidence. Practise supplying those details until they become automatic.