1. Data Science vs Data Analytics
1.4 Map of This Course
| Chapters | Theme |
|---|---|
| 0–2 | How to read, roles, Jupyter setup |
| 3–4 | Python + NumPy essentials |
| 5–11 | pandas: select, group, join, clean, dates, reshape |
| 12–15 | EDA, matplotlib, seaborn, storytelling |
| 16 | End-to-end Blinkit Maharashtra project (no model) |
| 17–20 | Practice, interview Q&A, cheat sheet, glossary |
AI/ML, sklearn, deep learning and NLP are not in this list on purpose.
Thodkyaat sangaycha tar (quick recap)
- Analytics = what/why with summaries; this course uses Python for that depth
- Excel / Power BI / Jupyter serve different delivery needs
- Always know the grain of one row before you sum or count
- No ML in v1 — focus on clean → EDA → viz → story
Samajla ka? Aata pudhe jaauya setup kade — Python, Jupyter aani packages.