https://github.com/linogaliana/python-datascientist

Dépôt associé au cours Python pour data scientists (ENSAE 2e année)
https://github.com/linogaliana/python-datascientist

data-science jupyter jupyter-notebook machine-learning opendata python teaching

Last synced: about 20 hours ago
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Dépôt associé au cours Python pour data scientists (ENSAE 2e année)

• Host: GitHub
• URL: https://github.com/linogaliana/python-datascientist
• Owner: linogaliana
• License: other
• Created: 2020-07-16T13:29:53.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-12-26T15:47:02.000Z (1 day ago)
• Last Synced: 2024-12-26T16:32:10.300Z (1 day ago)
• Topics: data-science, jupyter, jupyter-notebook, machine-learning, opendata, python, teaching
• Language: Python
• Homepage: https://pythonds.linogaliana.fr/
• Size: 481 MB
• Stars: 116
• Watchers: 2
• Forks: 45
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md
  • Citation: CITATION.cff

Awesome Lists containing this project

README

        
# Data science with Python 

[![build-doc Actions Status](https://github.com/linogaliana/python-datascientist/actions/workflows/prod.yml/badge.svg)](https://github.com/linogaliana/python-datascientist/actions)












[![DOI](https://zenodo.org/badge/280161677.svg)](https://zenodo.org/badge/latestdoi/280161677)

> [!NOTE]  

> This is the English 🇬🇧🇺🇸 version of the `README`. If you want to see the French 🇫🇷 version, you can click on the link below:

> 

> [![fr](https://img.shields.io/badge/lang-fr-red.svg)](https://github.com/linogaliana/python-datascientist/blob/main/doc/README-fr.md)



This GitHub repository 

stores the source files used to build the site

.

It contains the entire course *Python for Data Science* 

that I teach in the second year (Master 1) at [ENSAE](https://www.ensae.fr/).

> [!NOTE]  

> A guide to assist potential contributors is available by clicking the button below:

> 

> [![`CONTRIBUTING.md`](https://img.shields.io/badge/CONTRIBUTING-fr-red.svg)](https://github.com/linogaliana/python-datascientist/blob/main/doc/CONTRIBUTING-fr.md)

## Syllabus

The syllabus is available [on the ENSAE website](https://www.ensae.fr/courses/1425-python-pour-le-data-scientist) and on the [course website](https://pythonds.linogaliana.fr/).

Overall, it offers a very comprehensive content that can satisfy both beginners in data science and those looking for more advanced content:

1. __Data Manipulation__: standard data manipulation (`Pandas`), geographical data (`Geopandas`), data retrieval (web scraping, API)...

1. __Data Visualization__: classic visualizations (`Matplotlib`, `Seaborn`), cartography, interactive visualizations (`Plotly`, `Folium`)

1. __Modeling__: machine learning (`Scikit`), econometrics

1. __Text Data Processing (NLP)__: introduction to tokenization with `NLTK` and `SpaCy`, modeling...

1. **Introduction to Modern Data Science**: cloud computing, `ElasticSearch`, continuous integration...

The content of this site is based on open data, whether French data (mainly from the central platform [`data.gouv`](https://www.data.gouv.fr) or the website of [Insee](https://www.insee.fr)) or American data.

A good complement to the website's content is the course we give with Romain Avouac ([@avouacr](https://github.com/avouacr)) in the final year at ENSAE, more focused on the production of data science projects: [https://ensae-reproductibilite.github.io/website/](https://ensae-reproductibilite.github.io/website/)



Testing Python examples



You can use a personal installation of `Python` or shared servers. On the website, a series of buttons are available to easily test the examples on `Jupyter` notebooks in the configuration that suits you best.



Here are, for example, these buttons for the Numpy tutorial: