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README.md

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# Data Science with Python <img height="28" width="28" src="https://cdn.simpleicons.org/python/00ccff99" />
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# Data Science with Python <img height="28" width="28" src="https://cdn.simpleicons.org/python/00ccff99" /> <a href='https://pythonds.linogaliana.fr/'><img src='https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/kiddos.png' align="right" height="100" /></a>
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[![DOI](https://zenodo.org/badge/280161677.svg)](https://zenodo.org/badge/latestdoi/280161677)
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[![Production deployment](https://github.com/linogaliana/python-datascientist/actions/workflows/prod.yml/badge.svg)](https://github.com/linogaliana/python-datascientist/actions/workflows/prod.yml)
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------------------------------------------------------------------------
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[Lino Galiana](https://www.linogaliana.fr/) • Data scientist, [Insee](www.insee.fr) (French national statistical institute)
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Course given in two top French engineering schools ([ENSAE](https://www.ensae.fr/) and [ENSAI](https://ensai.fr/)) and available for self-paced learning.
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------------------------------------------------------------------------
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> [!NOTE]
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> This is the **English 🇬🇧🇺🇸 version** of the `README`.
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> To see the **French 🇫🇷 version**, click here:
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> This is the **English 🇬🇧🇺🇸 version** of the `README`. To see the **French 🇫🇷 version**, click here:
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>
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> [![fr](https://img.shields.io/badge/lang-fr-red.svg)](https://github.com/linogaliana/python-datascientist/blob/main/doc/README-fr.md)
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## 📚 About
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## About
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This <img height="18" width="18" src="https://cdn.simpleicons.org/github/00ccff99" /> repository hosts the source files for my [**Python for Data Science**](https://pythonds.linogaliana.fr/), a hands-on course designed to take students from first contact with `Python` <img height="18" width="18" src="https://cdn.simpleicons.org/python/00ccff99" /> to practical data science workflows.
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This repository <img height="18" width="18" src="https://cdn.simpleicons.org/github/00ccff99" /> contains the source files for the course **Python for Data Science** <img height="18" width="18" src="https://cdn.simpleicons.org/python/00ccff99" /> taught in the second year (Master 1) at [ENSAE](https://www.ensae.fr/).
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It is taught in two French engineering schools in 2n year (Master 1):
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- **ENSAE** since **2021**
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- **ENSAI** from **2026**
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The material combines explanations, examples, and exercises, with an emphasis on reproducible and real-world datasets.
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All chapters (notes, examples, and exercises available as Jupyter notebooks) are available at https://pythonds.linogaliana.fr/.
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<details>
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<summary><strong>📌 License and attribution</strong></summary>
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This course is released under the **Creative Commons CC BY-NC-SA** license <img height="18" width="18" src="https://cdn.simpleicons.org/creativecommons/00ccff99" />.
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If you use this course material, please cite:
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Galiana, Lino. 2025. <em>Python pour la data science</em>. https://doi.org/10.5281/zenodo.8229676
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```bibtex
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@book{galiana2025,
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author = {Galiana, Lino},
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title = {Python pour la data science},
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date = {2025},
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url = {https://pythonds.linogaliana.fr/},
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doi = {10.5281/zenodo.8229676},
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langid = {fr}
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}
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```
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</details>
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The course website is available here:
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🌐 **<https://pythonds.linogaliana.fr/>**
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---
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## 🎨 Gallery
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Some visualizations produced during the course:
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A few examples of figures produced during the course (click to open the course website):
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<p align="center">
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig1.png" alt="Figure 1" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig7.png" alt="Figure 7" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig3.png" alt="Figure 3" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig8.png" alt="Figure 8" width="100"/></a>
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<br/>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig5.png" alt="Figure 5" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig6.png" alt="Figure 6" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig2.png" alt="Figure 2" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig4.png" alt="Figure 4" width="100"/></a>
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<br/>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig13.png" alt="Figure 13" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig9.png" alt="Figure 9" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig14.png" alt="Figure 14" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig11.png" alt="Figure 11" width="100"/></a>
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<br/>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig15.png" alt="Figure 15" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig16.png" alt="Figure 16" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig10.png" alt="Figure 10" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig12.png" alt="Figure 12" width="100"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig6.png" alt="Top 50 French velib stations" width="120"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig13.png" alt="Correlation matrix" width="120"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig2.png" alt="Forest map" width="120"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig14.png" alt="Waffle chart" width="120"/></a>
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</p>
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<details>
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<summary><strong>More examples</strong></summary>
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<p align="center">
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig5.png" alt="Population map" width="120"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig1.png" alt="Top carbon emission cities" width="140"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig7.png" alt="Leaflet map example" width="140"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig3.png" alt="Pandas structure" width="140"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig8.png" alt="Haute Garonne map" width="140"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig4.png" alt="Spillhaus projected map example with Python" width="120"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig9.png" alt="Bulbizarre scrapped image" width="120"/></a>
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<a href="https://pythonds.linogaliana.fr"><img src="https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/screenshots/fig11.png" alt="Velib time use" width="120"/></a>
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</p>
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</details>
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## 📖 Course content
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This course is suitable for **both beginners and advanced learners**.
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This course is suitable for **both beginners and advanced learners**.
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The syllabus below is fully clickable and collapsible.
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<details>
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> [!NOTE]
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> See the guide for contributors:
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>
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> [![`CONTRIBUTING.md`](https://img.shields.io/badge/CONTRIBUTING-fr-red.svg)](https://github.com/linogaliana/python-datascientist/blob/main/doc/CONTRIBUTING-fr.md)
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