Python Programming for Scientific Computing and Data Science

An 18-lecture initiation to Python for students in mathematics, physics, engineering, and data science — from the shell to real data pipelines. All content lives in interactive Jupyter notebooks.

What this course covers

Foundations — the shell, Python fundamentals, git, files.
Core language — containers, control flow, functions, OOP, generators, decorators.
Scientific stack — NumPy, matplotlib, pandas, APIs, data formats.

Where to start

📚 Lectures

Eighteen self-contained lectures with runnable code. Begin at Lecture 0 and build up, or jump to a specific topic from the sidebar.

Lecture 0 — The Shell

🧪 Practicals

Short guided exercises to consolidate each week’s material.

Practical_1: Variables and Assignments

🏆 Challenges

Three graded programming challenges — beginner, intermediate, advanced — to sharpen problem-solving.

PyPro: Challenge 1

How to use this book

Read online

Every lecture renders as an HTML page with a searchable sidebar. Nothing to install.

Lecture 0 — The Shell

🚀 Run on Colab

Click here — or the rocket icon at the top of any lecture — to open in Google Colab. No local Python needed.

https://colab.research.google.com/github/ai-technipreneurs/python_website/blob/main/notebooks/00-shell.ipynb

🔧 Run locally

Clone the repo, follow the Prerequisites lab, then launch Jupyter or JupyterLab.

Prerequisites

🐙 Contribute

Spot an issue or want to add something? Open an issue or PR on GitHub.

https://github.com/ai-technipreneurs/python_website

Curriculum outline

Part I — Foundations

• Lecture 0 — The Shell

• Lecture 1 — Introduction to Python

• Lecture 2 — Variables, Types, and Assignment

• Lecture 3 — Strings and Files

• Lecture 4 — Version Control with Git

Part II — Core Language

• Lecture 5 — Iterable Objects and Containers

• Lecture 6 — Flow Control and Loops

• Lecture 7 — Functions, Modules, and Packages

• Lecture 8 — Recursion and Lambda

• Lecture 9 — Errors and Debugging

Part III — Scientific Stack

• Lecture 10 — NumPy, Part 1

• Lecture 11 — NumPy, Part 2

• Lecture 12 — Matplotlib

Part IV — Advanced Python

• Lecture 13 — Object-Oriented Programming

• Lecture 14 — List Comprehensions and Generators

• Lecture 15 — Pandas for Data Analysis

• Lecture 16 — Decorators and Context Managers

• Lecture 17 — Data Formats and APIs

Note à l’attention des étudiants francophones

Pour les étudiants francophones, les ressources suivantes se révèlent particulièrement utiles :

• In2Py accompagné de ses cahiers virtuels

• Atelier-3P

• Le tutoriel Python officiel

Ouvrages de référence :

• Au cœur de Python

• Programmation Python

Useful resources

• Software Carpentry — Python lessons

• How to Think Like a Computer Scientist

• The Python Tutorial (docs.python.org)

• Dive Into Python 3

Credits

Author

Yaé Ulrich Gaba  ·  LinkedIn  ·  Website

Technical support

This Jupyter Book was built with the support of the technical team at ai.technipreneurs: Awadi Katanga and Domini Leko.

Template credit

The book structure is adapted from Joaquin Vanschoren’s ML course template.