Data Project Documentation Template

A production-ready GitHub template for documenting data analysis, SQL, dashboard, pipeline, and machine learning projects - with built-in examples, narrative guidance, and adaptable structure.

What This Is

This is a cloneable template repository designed to help data analysts, scientists, and engineers document their projects the way senior practitioners do - with clarity, structure, and storytelling.

Every section includes:

• Plain-language instructions on what to write
• Examples of strong vs. weak documentation
• Guidance on when to keep or delete a section

It works for any kind of data project:

• SQL analysis
• Python / R exploratory analysis
• Dashboards (Tableau, Power BI, Looker)
• Data pipelines / ETL workflows
• Machine learning / predictive modeling
• Mixed-method projects

Why This Exists

After hundreds of portfolio reviews, the pattern was clear: the projects that stand out aren't always the most technically sophisticated - they're the ones where the README tells a clear story.

Most data professionals never learn how to document their work. This template fixes that.

How to Use It

1. Click "Use this template" (green button at the top)
2. Name your new repo after your actual project
3. Copy the contents of README_TEMPLATE.md into your new project's README
4. Delete folders and sections you don't need
5. Remove all placeholder text and comments before publishing

Full walkthrough: HOW_TO_USE.md

What's Inside

The template includes:

• README_TEMPLATE.md - The fill-in-the-blank documentation template
• project_metadata.yml - Optional machine-readable metadata
• Complete folder structure - Pre-built folders for data, notebooks, scripts, queries, reports, visuals, and docs
• .gitignore - Pre-configured to exclude data files

Who This Is For

• Beginners building their first portfolio project
• Early-career analysts cleaning up existing projects
• Career switchers who have technical skills but need to frame their work
• Experienced practitioners who want a consistent structure

Features

✅ Section-by-section guidance with examples
✅ Supports SQL, Python, R, dashboards, pipelines, ML
✅ Includes ERD section for SQL projects
✅ Pre-built .gitignore for data projects
✅ Optional YAML metadata for portfolio automation
✅ Delete-what-you-don't-use philosophy

License

MIT License - use this however you want.

Credits

Created by Issy BI

If this helped you, consider starring the repo or sharing it with someone building their data portfolio.