Grade 11 — Data scientists I (uncertainty, time and space)¶
Tools: pandas, SQL, folium/GeoPandas, seaborn, Git · Units: 8 · Lessons: 24 (+ projects)
Grade 11 adds uncertainty, time and space: working with bigger tables, SQL window functions, bootstrap and permutation ideas, regression with interpretation, seasonality, simple geospatial thinking, and reproducible workflows with Git. Aligned as an enrichment bridge to NEB Computer Science (databases, programming), not a replacement. Notebooks run offline on synthetic tables or on data generated inside the notebook.
| Unit | Title | Lessons | Track | Project |
|---|---|---|---|---|
| 1 | Bigger data and performance basics | 3 | B (code) | Make it faster, prove it |
| 2 | SQL: joins and window functions (lite) | 3 | B (code: SQL) | Top districts per province |
| 3 | Uncertainty: bootstrap and confidence intervals | 3 | B (code) | How sure are we? |
| 4 | Is the difference real? Permutation tests | 3 | B (code) | Chance or real? |
| 5 | Regression with interpretation | 3 | B (code) | Explain it, don't just fit it |
| 6 | Time series and seasonality | 3 | B (code) | Climate or weather? |
| 7 | Maps and geospatial thinking | 3 | B (code, light) | Where is it worst? |
| 8 | Reproducibility, Git and the climate project | 3 | B (project) | Climate or weather? |
Every chapter now has detailed explanations with worked examples (all Python and SQL in the chapters is run in CI by
tools/check_chapter_code.py), common mistakes, a unit summary, practice exercises (★/★★/★★★, answers in the
teacher guide) and exercises with real data from docs/datasets/free-datasets.md,
each with an offline fallback. Unit 8 ends with a "ready for Grade 12?" checklist.
Flagship project: Climate or weather? — detect a trend in Nepal climate data, with uncertainty
Each unit folder holds: chapter.md (student), teacher-guide.md, project.md, quiz.yml, datasets.yml
(and notebooks/ where code is used). Nepali chapters: see community/translations/STATUS.md.