Grade 10 — Data analysts (Python and analysis)¶
Tools: Python (pandas, matplotlib), SQL, Streamlit · Units: 8 · Lessons: 24 (+ projects)
Grade 10 moves from wrangling to analysis in Python: the pandas toolkit on a messy air-quality series, distributions and
outliers, relationships and the line of best fit, time series, a first honest look at what a model is (k-nearest neighbours, unplugged
then coded), AI literacy and ethics, and a small Streamlit dashboard. Notebooks run offline on synthetic look-alike tables;
swap in real data with tools/fetch_data.py after checking the licence.
| Unit | Title | Lessons | Track | Project |
|---|---|---|---|---|
| 1 | pandas deep-dive: cleaning a real-world series | 4 | B (code) | Clean series report |
| 2 | Distributions and outliers | 3 | B (code) | What does a typical day look like? |
| 3 | Relationships and the line of best fit | 3 | B (code) | Two variables, one honest line |
| 4 | Time series basics | 3 | B (code) | Climate or weather? |
| 5 | What is a model? (and a first look at machine learning) | 3 | A then B | Model vs baseline |
| 6 | AI literacy and ethics | 3 | A (no-code) | AI charter and audit |
| 7 | Dashboards with Streamlit | 3 | B (code) | Mini dashboard |
| 8 | Project: Air in my city | 2 | B (project) | Air in my city |
Every chapter now has detailed explanations with worked examples (all Python in the chapters is run in CI by
tools/check_chapter_code.py, except Streamlit app code), 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 11?" checklist.
Flagship project: Air in my city — analyse a year of PM2.5 and publish a mini dashboard
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.