By Prerna Joshi | #25DaysOfDataTech | #PythonToProduction
Most beginners learn Python in a scattered way — random tutorials, disconnected examples, too many topics at once.
This repository fixes that.
Here, you’ll find a clear, structured set of Jupyter notebooks that build the strong Python foundation you need before jumping into Pandas, SQL, ML models, RAG systems, APIs, or cloud engineering.
If you skip fundamentals today, you will regret it later.
So go slow. Learn at your own pace. Build it right.
This repo contains 9 fully explained notebooks, covering the Python concepts that matter the most in real-world analytics and ML workflows.
Each notebook is practical, example-rich, and beginner-friendly.
| Notebook | Description |
|---|---|
| 01_python_basics_variables_datatypes.ipynb | Introduction to Python, variables, data types, numbers, expressions — your essential building blocks. |
| 02_python_operators_controlflow.ipynb | Conditionals (if/elif/else), boolean logic, comparison operators — the logic behind every program. |
| 03_python_loops_in_depth.ipynb | For loops, while loops, break/continue, nested loops, clean looping patterns. |
| 04_functions_and_scope_LEGB.ipynb | Functions, parameters, return values, scope resolution (LEGB), modular code design. |
| 05_core_data_structures.ipynb | Lists, tuples, sets, dictionaries — your everyday data containers in analytics and ML. |
| 05.1_collections.ipynb | Counter, defaultdict, deque, namedtuple, OrderedDict — when you need more power than built-ins. |
| 06_strings_text_processing.ipynb | Text cleaning, string methods, slicing, formatting — extremely useful for data cleaning. |
| 07_functional_programming.ipynb | Lambdas, map/filter/reduce, immutability, list/dict comprehensions — the Pythonic way to transform data. |
| 08_object_oriented_programming.ipynb | Classes, objects, methods, attributes — the useful OOP you’ll actually use in ML workflows. |
| 09_idioms_and_best_practices.ipynb | Pythonic writing, readability, performance patterns, clean code principles. |
Understand Python’s core building blocks to avoid hidden bugs later.
Write clean, efficient logic — essential for all automation and data manipulation.
A Python superpower: shorter, faster, cleaner transformations.
Build reusable components, modular pipelines, and cleaner notebooks.
Read/write files, process logs, parse text — real tasks in data engineering.
Learn the practical parts: classes, objects, __init__, methods.
Useful for:
- ML model wrappers
- Config loaders
- Data pipelines
- API structures
Write code that looks like a professional wrote it.
This repo is perfect for:
- Beginners learning Python from scratch
- Students preparing for data roles
- Aspiring Data Scientists & ML Engineers
- Anyone transitioning from software/SAP to data (like my own journey!)
- Researchers needing clean Python workflows
- Professionals brushing up fundamentals
- Start with Notebook 1 if you’re a beginner.
- Move at your own pace — fundamentals need time.
- Run every cell and experiment with variations.
- Add your own notes inside the notebook.
- Apply concepts to small projects to retain them better.
To run notebooks:
pip install jupyterlabInstall basic dependencies:
pip install numpy pandas matplotlibI'm learning in public and sharing everything I know through this series.
If this repo helped you:
⭐ Star the repo
📘 Share it with someone starting their data journey
💬 Connect with me on LinkedIn — Prerna Joshi
Your support motivates me to build more beginner-friendly, real-world content.