Six-hour learning plan

From suspicious spreadsheet to useful public tool.

The curriculum follows the way responsible data work actually happens: question, clean, test, explain and only then publish.

Interrogate the dataset

Provenance, collection method, missing context, ethical use and the difference between a claim and an assumption.

A clean project structure, environment file, source notes and a notebook that another person can rerun.

Types, nulls, duplicates, outliers and a short cleaning log explaining every material choice.

Choose one useful question, test it, use Plotly accessibly and avoid charts that overstate the evidence.

Create a focused Streamlit app with context, filters, clear units and an honest limitations section.

Basic pytest checks, peer review, README improvement, licensing and a short public demo.

Before the event