Reproducible Notebook & Project Template (practical folder structure)

Exploratory findings are only useful when others can reproduce them. This template is a minimal structure you can copy for every exploration so code, data selection, and conclusions are clear.

Suggested folder layout

• /project-name/
  • README.md — brief purpose and how to run the analysis
  • data/ — references to queries or sample CSVs (do not store sensitive full extracts)
  • notebooks/ — reproducible notebooks (Jupyter, Observable, or RMarkdown)
  • scripts/ — SQL queries or scripts used to extract data
  • figures/ — saved charts used in reporting
  • results/ — CSV summaries, test outputs, or model snapshots
  • notes.md — one-sentence observation, hypotheses, validation plan, and owner

Notebook structure (recommended sections)

1. Purpose & one-line observation — short context and what you noticed.
2. Data sources — exact queries, table names, and time windows.
3. Sanity checks — row counts, nulls, schema notes, and any filtering.
4. Exploration — charts and tables that reveal the pattern. Keep visualizations labeled and reproducible.
5. Sensitivity checks — alternate aggregations, outlier handling, and cohort slices.
6. Hypotheses — list plausible explanations and what you'd expect to observe if each were true.
7. Validation plan — a short, executable plan and acceptance criteria.
8. Conclusions & next steps — recommended follow-up with owner and priority.

Practical tips

• Keep queries parameterized so time windows or cohort definitions are explicit and adjustable.
• Prefer small, descriptive CSVs in data/ for reproducibility rather than full production extracts.
• Add a short run script (scripts/run.sh) that recreates figures from raw queries to final plots.
• Tag notebooks with a status: exploratory, validated, rejected, or in-progress.

Using a predictable structure makes it easy for others to review your work, reproduce results, and integrate validated findings into experiments, dashboards, or policy changes.