Topics covered so far (Weeks 1–3)
📝 = self-study: not covered live. You’ll find it in the 📝 Self-Guided Practice section at the end of that week’s slides. You are still responsible for it.
| Topic | Where |
|---|---|
| Course logistics, AI policy | Week 1 |
| R, Positron, Git/GitHub setup | Week 1 |
| Arithmetic, variables, data types and classes | Week 1 |
Vectors: subsetting, coercion, recycling, NA/NaN/Inf, set operations |
Week 1 |
📝 Linear algebra, sweep(), arrays |
Week 1, end |
| 📝 Quarto and LaTeX | Week 1, end; HW1 |
| Matrices, lists, strings, data frames | Week 2 |
Random numbers, set.seed() |
Week 2 |
| Packages, help, common errors | Week 2 |
Functions, for/while loops, vectorization, apply() |
Week 2 |
Conditionals, ifelse() |
Week 2 |
| 📝 Saving, importing, reshaping and merging data | Week 2; HW2 |
📝 Lazy evaluation, scoping, expand.grid() |
Week 2, end |
| Numerical summaries, correlation, summaries by group | Week 3 |
| Factors, contingency tables, chi-square tests, mosaic plots | Week 3 |
| Statistics vs. data mining (Donoho) | Week 3 |
| EDA: data science life cycle, Q&A workflow, PCS | Week 3; readings; HW3 |
| Base R graphics, ggplot2 | Week 3; HW3 |
| 📝 Conditional plots, chi-square by hand, Fisher’s test | Week 3, end |
| 📝 Outliers, transformations, base R graphics reference | Week 3, end |