✍ Exercises¶
Forty-two worked questions designed for deliberate practice. Each answer stays collapsed until you choose to reveal it, so the page works as a workbook rather than a solution sheet.
Exercise Dashboard¶
| Chapter | Focus | Questions | What you will practice | Start |
|---|---|---|---|---|
| 01 | Data & Attribute Types | 5 | Distinguishing nominal, ordinal, interval, and ratio data | Practice · Review |
| 02 | Descriptive Statistics | 4 | Frequency tables, quartiles, covariance, Pearson and Spearman correlation | Practice · Review |
| 03 | Multivariate Analysis | 8 | Summary matrices, plot selection, correlations, heatmaps, and mosaic plots | Practice · Review |
| 04 | Data Quality & Preprocessing | 5 | Missing values, binning, encoding, normalization, and sampling | Practice · Review |
| 05 | Clustering | 3 | Distance measures, k-means iterations, linkage, and density-based clusters | Practice · Review |
| 06 | Frequent Pattern Mining | 4 | Support, confidence, lift, Apriori pruning, and pattern types | Practice · Review |
| 07 | Classification | 5 | Tree splits, k-NN, Naive Bayes, evaluation metrics, and cross-validation | Practice · Review |
| 08 | Mixed Review | 8 | Selecting and sequencing techniques across the complete workflow | Practice · Review |
Note
Chapters 01–06 are adapted from the solved lecture assignments in 05-resources/Class Exercises/ (not published — see Resources); Chapter 06's source material is filed there as "assignment 08" (Epub/arules association rules) since it followed the course's own numbering rather than this site's chapter order. Chapter 07 and the Chapter 08 mixed review are newly authored to complete sequential coverage of all 8 chapters.
The Three-Pass Method¶
- Solve: answer every question without opening the solution, even if you have to make an informed guess.
- Diagnose: reveal the answer and label misses as a concept gap, calculation error, or interpretation error.
- Retrieve: revisit only the relevant note section, then answer the question again from a blank page.
Finish with the matching randomized chapter quiz. A score is useful, but being able to explain why the distractors are wrong is the stronger test.
Tip
Keep a one-page error log. Record the concept, your mistaken reasoning, and the rule or example that corrects it. Review that page before the mixed exercise.
Suggested Checkpoints¶
| After | You should be able to |
|---|---|
| Chapters 01–02 | Choose valid summaries and calculations for a given data type |
| Chapters 03–04 | Design an exploratory analysis and prepare its input data |
| Chapters 05–06 | Select a discovery method for groups or co-occurrence patterns |
| Chapters 07–08 | Build an evaluation plan and justify the full analytical workflow |
The answer blocks use GitHub-supported <details> sections, so these exercises work both on the course site and directly in the repository.