The capstone is the most important deliverable in the Google Data Analytics Certificate. It's the project you'll put in your portfolio, reference in interviews, and link on your resume. Done well, it's a powerful demonstration that you can actually do the work — not just pass quizzes about it. Here's how to approach it strategically.
Choosing Your Case Study
The certificate offers two pre-built case studies (Cyclistic bike share and Bellabeat wellness products) plus the option to design your own. If you're short on time, use a provided case study. If you want to stand out, use a public dataset from Kaggle, Google Dataset Search, or data.gov that's relevant to your target industry.
The best capstone topics are ones you can speak about confidently in an interview because they genuinely interest you. A marketing analyst candidate doing a case study on retail sales data speaks more naturally than someone who chose it randomly.
Structure Your Analysis Like a Professional
Every strong capstone follows a clear analytical framework: Ask (what business question are you answering?), Prepare (describe your data source and any limitations), Process (what cleaning did you do and why?), Analyze (what patterns did you find?), Share (what visualizations tell the story?), Act (what recommendations do you make?).
This is literally the framework taught in the certificate — use it explicitly in your write-up. It shows you internalized the curriculum, not just completed it.
Presentation Tips
Present your capstone as a written report or a slide deck — both work. Include: an executive summary of your findings at the top (not the bottom), at least 3 visualizations created in Tableau or R, a clear statement of your recommendations, and a brief description of your data cleaning steps. Keep it under 20 slides or 10 pages — brevity signals confidence.
Where to Host It
Host your capstone on GitHub (Jupyter notebook or R Markdown), Tableau Public, or a personal portfolio site. GitHub is the most common and most respected by hiring managers. A public GitHub repository with a well-written README signals professionalism before they even read your code.