Part 4 — Grade 11: Data scientists I¶
Uncertainty, time, space and reproducibility.
Grade 11 asks the scientist's question: how sure are we? You will handle bigger tables efficiently, use SQL window functions, quantify uncertainty with the bootstrap, test whether differences could be chance, interpret multiple regression carefully, measure trends with uncertainty, think geographically, and make your work reproducible with Git.
| Unit | Title | Lessons | Project |
|---|---|---|---|
| 1 | Bigger data and performance | 3 | Make it faster, prove it |
| 2 | SQL: window functions and joins | 3 | Top districts per province |
| 3 | Uncertainty: bootstrap and confidence intervals | 3 | How sure are we? |
| 4 | Is it chance? Permutation tests | 3 | Chance or real? |
| 5 | Regression with interpretation | 3 | Explain it, don't just fit it |
| 6 | Time series and seasonality | 3 | Climate or weather? |
| 7 | Maps and geospatial thinking | 3 | Where is it worst? |
| 8 | Reproducibility and a climate project | 3 | flagship project |
Tracks: all Track B (Python, SQL, Git). The ideas of Units 3–4 can be taught unplugged with slips of paper first.
Pacing: 24 lessons + projects ≈ 40 periods. Grade 11 aligns with NEB Computer Science as enrichment; see the
competency map in docs/for-cdc/.
Key idea of the year: simulation (resampling and shuffling) lets you reason about uncertainty without heavy formulas.