Practice Python Data Analytics interviews with AI-generated questions, instant evaluation, and live in-browser code execution.
- 🤖 Claude AI generates unique questions every session — no static question bank
- 📊 10 Analytics Topics — NumPy, Pandas, GroupBy, Matplotlib, Scikit-learn, Time-series & more
- 🎯 4 Difficulty Levels — Easy → Medium → Hard → Expert (auto-advances on strong performance)
▶️ Live Python Execution — run code directly in the browser via Pyodide (no server needed)- 📝 AI Evaluation — scores your answer 0–100% with specific feedback and improvement tips
- 📈 Session Dashboard — score history, average, correct/partial/review breakdown
- 📱 Mobile-first UI — works on any modern browser, no install required
👉 yourusername.github.io/pyanalytics-quiz
| Layer | Technology |
|---|---|
| AI Questions & Evaluation | Anthropic Claude API (claude-sonnet-4-6) |
| Python Runtime | Pyodide 0.25 (WebAssembly) |
| Frontend | React 18 (via CDN, no build step) |
| Hosting | GitHub Pages |
| Fonts | Inter + Fira Code |
- Visit the live demo link above
- Enter your Anthropic API key when prompted
- Choose a difficulty level and topic
- Answer the AI-generated question in your own words or with code
- Get instant AI feedback and a score
- Run the correct solution in the live Python editor
- Move to the next question — difficulty auto-increases as you improve!
🔐 Your API key is stored only in your browser's
sessionStorageand is never sent anywhere except directly toapi.anthropic.com.
This is a single index.html file — zero dependencies, zero build step.
# Clone
git clone https://github.com/yourusername/pyanalytics-quiz.git
# Open locally
open index.html # macOS
start index.html # WindowsOr deploy to GitHub Pages in 3 clicks:
Settings → Pages → Branch: main / (root) → Save
| # | Topic |
|---|---|
| 1 | NumPy arrays & vectorisation |
| 2 | Pandas DataFrames & Series |
| 3 | Data cleaning & missing values |
| 4 | GroupBy, pivot & merge |
| 5 | Matplotlib & Seaborn visualisation |
| 6 | Statistical analysis & hypothesis testing |
| 7 | Feature engineering |
| 8 | Scikit-learn ML pipelines |
| 9 | Time-series analysis |
| 10 | Performance optimisation & big data |
Vishal Shukla — VP, Data Analytics & Business Intelligence
LinkedIn · Tableau Public
MIT — free to use, fork, and build upon.
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