Capstone Project & Professional Assessment

This is where everything comes together. You will run a complete, end-to-end analytics project on real data and present it like a professional — the single most valuable piece in your portfolio. Choose a domain you care about: finance, healthcare, retail, HR or sports.

1. Frame the question. Pick a domain and write one clear business question your analysis will answer (e.g. ‘Which factors drive customer churn, and what should we do?’).
2. Source real data. Collect a genuine dataset — a public CSV, an API, a Kaggle dataset, or a database (Modules 1–2).
3. Clean & wrangle. Fix types, handle missing values, merge tables and reshape into a tidy, analysis-ready form (Module 3).
4. Explore (EDA). Profile the data, study distributions, detect outliers and correlations, and surface at least five evidence-backed insights (Module 4).
5. Visualise. Build clear charts and at least one interactive dashboard (Streamlit / Tableau / Power BI) that answers the question (Modules 5 & 8).
6. Add analytics depth. Apply statistics or a simple predictive/forecasting model where it strengthens the conclusion (Modules 6 & 9).
7. Write the report. Produce a written report using Question → Finding → Evidence → Recommendation, with the headline insight first.
8. Present it. Record a 10-minute walkthrough explaining the problem, your approach and your recommendation for a non-technical audience.
9. Publish. Push the notebook, data dictionary, dashboard link and report to a well-documented GitHub repository (Module 10).
10. Pass the final assessment below to complete the course and earn your certificate.

A final assessment covering the whole course. Pass it (70%+) — together with completing every module — to earn your Professional Certificate in Data Analytics using Python.

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