Your portfolio is what separates you from the hundreds of other people who also completed the Google Data Analytics Certificate. A strong portfolio demonstrates that you can actually do the work — and it's often the difference between getting an interview and getting ignored. Here's exactly how to build one from scratch.
What Goes in a Data Analytics Portfolio
A strong entry-level portfolio needs 3–5 projects minimum. Each project should demonstrate a different skill or tool: one SQL-focused analysis, one spreadsheet/Excel project, one Tableau dashboard, and your GDA capstone. If you add an R or Python project, that's a bonus that will differentiate you further.
Where to Find Data for Projects
Public dataset sources: Kaggle.com (hundreds of cleaned, pre-structured datasets), Google Dataset Search (datasets.google.com), data.gov (US government open data), Our World in Data (global statistics), and industry-specific portals like the CDC for healthcare data or the BLS for labor market data.
Choose topics that relate to your target industry. Applying to marketing roles? Build a project around customer segmentation or ad performance data. Going for finance? Use stock data or economic indicators.
How to Structure Each Project
Every portfolio project should have: a clear problem statement (what question are you answering?), a description of your data source, documented cleaning steps, your analysis code or queries, at least 2–3 visualizations, and a brief narrative of your findings and recommendations.
Host everything on GitHub with a detailed README. This is the standard — recruiters and hiring managers will look at your GitHub profile.
Building Your Portfolio Website
A personal portfolio website isn't required but is a significant differentiator. Free options like GitHub Pages, Notion, or Google Sites can work. For a more professional result, a simple one-page site with your name, bio, skills, and project links — built with basic HTML/CSS — takes a weekend and lasts throughout your career.