linogaliana/python-datascientist

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Dépôt associé au cours Python pour data scientists (ENSAE 2e année)

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README.md

Data Science with Python

Lino Galiana • Data scientist, Insee (French national statistical institute)

Course given in two top French engineering schools (ENSAE and ENSAI) and available for self-paced learning.

[!NOTE]
This is the English 🇬🇧🇺🇸 version of the README. To see the French 🇫🇷 version, click here:

About

This repository hosts the source files for my Python for Data Science, a hands-on course designed to take students from first contact with Python to practical data science workflows.

It is taught in two French engineering schools in 2n year (Master 1):

ENSAE since 2021
ENSAI from 2026

The material combines explanations, examples, and exercises, with an emphasis on reproducible and real-world datasets.

All chapters (notes, examples, and exercises available as Jupyter notebooks) are available at https://pythonds.linogaliana.fr/.

📌 License and attribution

This course is released under the Creative Commons CC BY-NC-SA license .

If you use this course material, please cite:

Galiana, Lino. 2025. Python pour la data science. https://doi.org/10.5281/zenodo.8229676

@book{galiana2025,
  author = {Galiana, Lino},
  title = {Python pour la data science},
  date = {2025},
  url = {https://pythonds.linogaliana.fr/},
  doi = {10.5281/zenodo.8229676},
  langid = {fr}
}

🎨 Gallery

A few examples of figures produced during the course (click to open the course website):

More examples

📖 Course content

This course is suitable for both beginners and advanced learners.

The syllabus below is fully clickable and collapsible.

1. Getting started: why Python for data science?

🔗 https://pythonds.linogaliana.fr/en/content/getting-started/

Getting a functional Python environment for data science
How to deal with a data set
Python basics

2. Data wrangling

🔗 https://pythonds.linogaliana.fr/en/content/manipulation/

Numpy, the foundation of data science
Introduction to Pandas
Data wrangling with Pandas
Spatial data with GeoPandas
Webscraping with Python
Retrieving data with APIs
Mastering regular expressions
Importing data from Parquet and S3

3. Data visualisation and communication

🔗 https://pythonds.linogaliana.fr/en/content/visualisation/

Building graphics with Python
Introduction to cartography

4. Modeling

🔗 https://pythonds.linogaliana.fr/en/content/modelisation/

Why preprocessing matters
Evaluating model quality
Introduction to classification
Introduction to regression
Feature selection
Clustering

5. Natural Language Processing (NLP)

🔗 https://pythonds.linogaliana.fr/en/content/nlp/

Cleaning and structuring texts
Bag-of-words approach
Text embeddings

🔗 Resources

The course content relies heavily on open data, including French datasets (from data.gouv and Insee) and American datasets.

Complementary course with Romain Avouac (@avouacr):
https://ensae-reproductibilite.github.io/website/

🚀 Accessing the course online

[!TIP]
Run examples instantly on SSP Cloud or Google Colab. Here is an example for Pandas chapter:

🤝 Contributing

I welcome contributions!

[!NOTE]
See the guide for contributors: