1. Why teach data science at school — and what "foundation" means

Why now, why here

• Data shape decisions about Nepal. Census results decide seats and budgets; health-post records decide where vaccines go; rainfall and river data decide flood warnings; remittance and price data shape household lives. Citizens who cannot read data critically can be misled; citizens who can, can hold decisions to account.
• The school curriculum already touches data, but thinly. Grade 8 Maths teaches mean, median, mode and pie charts; optional Computer Science in Grades 9–10 introduces databases and Python; NEB Grades 11–12 Computer Science covers databases and programming. What is mostly missing is the investigative core: forming questions, collecting fairly, judging uncertainty, interpreting honestly and thinking about people and fairness.
• AI tools are now everywhere. Students who understand how models learn from data, why they make mistakes and whom they can harm will use AI more wisely — and some will build better tools for Nepal.
• Jobs and further study. Data skills are valued in government, health, banking, development, agriculture, journalism, research and technology, in Nepal and abroad.

What "data science foundations" are

Not a list of software commands. Five strands that grow together:

1. Statistical thinking — variability is everywhere; questions about groups; distributions; comparing fairly; uncertainty; the difference between pattern and cause.
2. Data handling — tidy data, cleaning, documentation, databases, reproducibility.
3. Computational tools — spreadsheets, SQL, Python, from unplugged to code.
4. Modelling — from a line of best fit to honest machine learning with baselines and validation.
5. Communication and ethics — claims with evidence and limits; consent, privacy, fairness, who is missing.

A student has a foundation when they can run a small investigation from question to honest conclusion, in any of the tools they know, and can explain each step. The following chapter shows how each strand climbs from Grade 8 to Grade 12.

Principles behind this curriculum

• Investigation first, tools second. The PPDAC cycle (Problem, Plan, Data, Analysis, Conclusion) is the same from Grade 8 to 12; tools are added only when the thinking needs them.
• Spiral, not silo. The same ideas return each year at greater depth (rates in Grades 8, 9 and 12; uncertainty from "is a bigger sample better?" in Grade 8 to the bootstrap in Grade 11).
• Local data and contexts. Districts, belts, monsoon, air quality, migration; synthetic tables shaped like Nepal; real public data for exercises.
• Low-tech first. Every concept has an unplugged or paper version. Computers deepen learning; they are not a prerequisite for starting.
• Simulation before formulas. Shuffling slips and drawing samples builds intuition that formulas then summarise.
• Honesty and people. Every lesson ends with "Who or what is missing from this data?"
• Open and adaptable. CC BY 4.0: translate, localise, print, improve.

Where it fits in the timetable

Host	How
Compulsory Maths (Grade 8–10)	Track A lessons replace or extend the statistics chapters (Grade 8 Units 1–7; Grade 9 Units 1, 2, 7; Grade 10 Units 2–4 on paper)
Science	measurement, variability, experiments, climate and air (Grade 8 Units 2–5; Grade 10 Units 3–4; Grade 11 Units 3–4)
Social Studies / Health	census, migration, health data, ethics (Grade 8 Units 6–8; Grade 9 Unit 7; Grade 10 Unit 6; Grade 12 Units 5, 7)
Optional Computer Science (Grade 9–10)	Track B units: SQL, Python, pandas, dashboards
NEB Computer Science (Grade 11–12)	enrichment alongside DBMS and programming units
Clubs, extra-curricular, summer camps	projects and capstones

The policy brief and competency map for the Curriculum Development Centre are in docs/for-cdc/.