How to Build a Stand-Out Data Analytics Portfolio on GitHub

Recruiters want proof you can solve real business problems with data, not just pretty dashboards. Skip fancy websites—GitHub is all you need! The key: Organize your work clearly and make your ReadMe page shine with a professional summary, methodology, results, and next steps.

What Projects to Include

EDA (Exploratory Data Analysis): Show your skill in cleaning, visualizing, and interpreting raw data.
Dashboards: Publish interactive dashboards (using Streamlit, Tableau, or Power BI) to present key findings.
Funnel Analysis: Analyze user journeys or conversion funnels for business impact.
Full-Stack Projects: Combine backend SQL queries, analysis in Python, and insights visualization.

How to Organize Your Portfolio

Use separate repos for each project.
Write a ReadMe for every project with an executive summary, skills used, detailed methodology, results, and recommendations.
Use clear folder structures for scripts, data, visuals, and documentation.
Pin your most impressive projects to the top of your GitHub profile.

Inspiration

View real portfolios like tushar2704/Data-Portfolio (GitHub) and vishnu-t-r/Data-Analytics-Portfolio-Projects (GitHub) to see organized, skill-rich examples.
Check out this portfolio example by Jess Ramos for a detailed ReadMe structure and project ideas.
For more advanced interactive portfolios, see guides to GitHub Pages or curated lists on sites like KDnuggets.

Start today—build, organize, and share your work on GitHub to get noticed by tech recruiters and land your dream analytics job!

𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐠𝐫𝐚𝐦- 𝐆𝐞𝐭 𝐏𝐥𝐚𝐜𝐞𝐝 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂'𝐬

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