Day 01 – Part 1: Python Basics — Agenda¶
Python is the lingua franca of data science. Before you touch NumPy, Pandas, or any machine learning library, you need to own the language itself. This session builds that foundation — the part every experienced practitioner wishes they'd nailed from day one.
Session Overview¶
| # | Topic | File | Estimated Time |
|---|---|---|---|
| 1 | Python Introduction | 01-python-introduction.md |
20 min |
| 2 | Variables & Data Types | 02-variables-and-data-types.md |
30 min |
| 3 | Control Flow | 03-control-flow.md |
30 min |
| 4 | Functions | 04-functions.md |
30 min |
| 5 | Lists, Tuples & Dictionaries | 05-lists-tuples-dictionaries.md |
30 min |
| 6 | Practice Problems | 06-practice-problems.md |
45 min |
| 7 | Interview Questions | 07-interview-questions.md |
20 min |
| 8 | Cheat Sheet | 08-cheat-sheet.md |
Reference |
Total active learning time: ~3.5 hours (excluding breaks)
Learning Objectives¶
By the end of this session, you will be able to:
- Explain what Python is, how it runs, and why it dominates data science
- Declare variables correctly and understand Python's dynamic type system
- Work confidently with all four core scalar types:
int,float,str,bool - Write
if/elif/elsebranches andfor/whileloops with real control logic - Use
break,continue,enumerate(),zip(), and list comprehensions fluently - Define functions with positional, default,
*args, and**kwargsparameters - Write docstrings and understand the LEGB scope rule
- Manipulate lists, tuples, dictionaries, and sets for data processing tasks
- Solve beginner-to-intermediate coding problems with clean, readable code
Setup Checklist¶
Before starting, confirm your environment is ready:
- [ ] Python 3.10 or later installed — run
python --versionto check - [ ] VS Code with the Python extension, OR Jupyter Notebook, OR Google Colab
- [ ] You can run
print("hello")without errors
Tip
If you are completely new to Python, Google Colab requires zero installation — go to colab.research.google.com and start a new notebook. You can follow along without installing anything locally.
Difficulty & Time Estimates¶
| Segment | Difficulty | Reading Time | Exercise Time |
|---|---|---|---|
| Python Introduction | Beginner | 20 min | — |
| Variables & Data Types | Beginner | 25 min | 10 min |
| Control Flow | Beginner–Intermediate | 25 min | 15 min |
| Functions | Intermediate | 25 min | 20 min |
| Lists, Tuples & Dictionaries | Intermediate | 30 min | 20 min |
| Practice Problems | Mixed | — | 45 min |
How to Use These Notes¶
Each file is self-contained but builds on the previous one. Work through them in order.
Every code block is executable as-is — copy it into a Python file or Jupyter cell and run it. The # Output: comments show exactly what you should see. If your output differs, that discrepancy is worth investigating before moving on.
The callout boxes throughout the notes follow this convention:
[!info]— background context and definitions[!tip]— practical habits used by working data scientists[!warning]— common mistakes that cause subtle bugs[!success]— key takeaways worth remembering