5. Building a portfolio and finding your community¶
Why a portfolio¶
Degrees and certificates say what you studied; a portfolio shows what you can do. Employers, universities and scholarship committees increasingly look at public projects. Your Grade 9–12 projects are already the start of one.
What a good portfolio project looks like¶
- a real question that matters to someone (ideally about Nepal or your community);
- real data, credited and licensed properly;
- a clear README: question, data, method, results, limits, how to run it;
- clean, reproducible code (Grade 11 Unit 8), with a fixed seed and requirements file;
- honest results, including what didn't work;
- one or two good charts with action titles;
- a short write-up (blog post or PDF) a non-expert can read.
Three to five strong projects beat twenty half-finished ones.
Project ideas using Nepali data¶
| Idea | Data (Appendix A) | Skills |
|---|---|---|
| Kalimati vegetable price seasons and festival effects | RD-08 | time series, seasonality, dashboards |
| How has Kathmandu's air changed since 2015? | RD-12, RD-06 | cleaning, trends with uncertainty, weather confounders |
| Aftershock patterns of the 2015 earthquake | RD-05 | time, maps, distributions (sensitively) |
| Is the monsoon changing in my district? | RD-06, RD-07 | anomalies, bootstrap trends |
| Census 2021: which districts grew and shrank, and why? | RD-13, RD-11 | rates, maps, regression |
| Access to health posts in my municipality | RD-16, RD-15 | geospatial, distances, honest maps |
| Glacial lakes and flood risk | RD-14 | geospatial, communication of risk |
| Nepal in South Asia: health and wealth over 60 years | RD-01, RD-02, RD-03 | visualisation, regression |
| A Nepali-language text project (e.g. classifying news headlines you collect) | your own data | text processing, ML, ethics of collection |
Where to publish¶
- GitHub (or GitLab, Codeberg): code, notebooks, README. A tidy GitHub profile with a short bio and pinned projects is your shop window.
- Kaggle: public notebooks on public datasets.
- A simple website or blog: GitHub Pages is free; write-ups with charts.
- This curriculum's student gallery (
community/student-gallery/) for school projects, with consent.
Never publish personal data, restricted datasets (e.g. DHS microdata) or anything a client has not approved.
Communities¶
Learning with others keeps you motivated and connects you to opportunities. Examples of organisations and communities active in Nepal's data and technology space (activities change — check their current programmes before joining):
- Open Knowledge Nepal — open data, data literacy, the Open Data Nepal portal.
- Kathmandu Living Labs — open mapping, civic technology, OpenStreetMap work in Nepal.
- CoSoG Nepal — computer science and programming for school students.
- Code for Nepal — data and digital literacy projects.
- NAAMII (Nepal Applied Mathematics and Informatics Institute for Research) — AI research and schools/workshops.
- Python, data and AI meetups in Kathmandu and other cities; university clubs (computer, statistics, robotics).
- Women in tech groups and mentoring programmes.
- Global online communities: Kaggle discussions, Stack Overflow, the scikit-learn and pandas communities, PyData, OpenStreetMap.
How to participate well: ask clear questions (what you tried, the error, a small example), answer beginner questions when you can, contribute documentation fixes to open-source projects, volunteer on mapathons, and present your projects at meetups.
Contributing to open source¶
You can start small: fix a typo in documentation, translate a page into Nepali, add an example. This curriculum itself
welcomes contributions (CONTRIBUTING.md). Open-source contributions are public evidence of skill and collaboration.