Arrive & set up
Install check, dataset briefing, teams of 3–4.
A practical community workshop · Bhopal, India
Turn messy public data into something people can use.
A free, six-hour Python workshop where 60 learners clean a real dataset, explain their decisions, and publish a small interactive data app.
bhopal_data.csv100% Python
Raw rows → clear evidence → a public app
07 FEB 2027Sunday · Bhopal
60 SEATSFree · approval-based
6 HOURS10:00 AM–4:30 PM
Not a lecture
You leave with a working project, not a folder of slides.
We work in small groups using Python, pandas, Jupyter, Plotly, Streamlit, Git and pytest. Mentors help beginners without taking the keyboard away.
Read the full learning plan →The day
One honest workflow, end to end.
Read before coding
Ask who collected the data, what is missing, and what it can actually prove.
Clean with pandas
Handle types, missing values, duplicates, and suspicious rows.
Lunch & review
Compare choices with another team and document trade-offs.
Find the useful story
Test questions, build clear charts, and reject misleading views.
Ship a Streamlit app
Turn the notebook into a small interactive public-data tool.
Peer checks & demos
Run basic tests, improve the README, and share what changed.
What counts as finished
Small, useful and inspectable.
- 01A cleaned dataset with a plain-English data dictionary
- 02A reproducible Python notebook with documented decisions
- 03One focused Plotly view and a small Streamlit application
- 04A public Git repository with setup steps and basic tests
Who this is for
Curious learners with basic Python—not only experts.
Best for students, data learners and early-career developers who can write a small Python script. You should bring a laptop and be ready to collaborate.
No sales pitches, paid courses or speculative project ideas. We are here to learn responsible data work and publish it openly.
Private registration · opens soon
Tell us what you want to learn and build.
Applications will ask for your Python comfort level, GitHub profile and one public-data question you care about. Selection will balance readiness, learning need and diverse backgrounds.