https://github.com/vuillaut/datascience_intro
Introductive Course to Data Science
https://github.com/vuillaut/datascience_intro
Last synced: 2 months ago
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Introductive Course to Data Science
- Host: GitHub
- URL: https://github.com/vuillaut/datascience_intro
- Owner: vuillaut
- License: mit
- Created: 2023-05-05T14:21:38.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2026-04-01T14:02:23.000Z (3 months ago)
- Last Synced: 2026-04-01T14:53:13.188Z (3 months ago)
- Language: Jupyter Notebook
- Size: 86.9 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Introductive Course to Data Science
Thomas Vuillaume
contact me at firstname.name[at]lapp.in2p3.fr
## Running online
- [Google colab](https://colab.research.google.com/github/vuillaut/datascience_intro/)
- [mybinder](https://mybinder.org/v2/gh/vuillaut/datascience_intro/HEAD)
## Courses content:
### Slides
[https://vuillaut.github.io/lectures/data_science_intro/1](https://vuillaut.github.io/lectures/data_science_intro/1)
### Course 0: Python and environment setup
These are reminders of prerequisite for this course
Python basics:
- https://jckantor.github.io/CBE30338/01.02-Python-Basics.html
- https://www.kaggle.com/learn/python
Env. setup:
- [conda](https://www.anaconda.com/products/individual)
Git:
- https://education.github.com/git-cheat-sheet-education.pdf
### Part 1: coding environment and Jupyter
- Introduction and environment setup
- Working with [Jupyter](1.jupyter)
### Part 2: [Numpy](2.numpy)
### Part 3: [Pandas](3.pandas)
### Part 4: [Matplotlib](4.matplotlib)
### Part 5: [Machine learning](5.machine_learning)
### Part 6: [Practical work on a real case]
# Resources
- [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
- [Machine Learning (Lecture 1)](https://indico.cern.ch/event/619370/) --- [Michael Kagan](https://www.linkedin.com/in/michael-kagan-06292616/) (SLAC)
- [Machine Learning (Lecture 2)](https://indico.cern.ch/event/619371/) --- [Michael Kagan](https://www.linkedin.com/in/michael-kagan-06292616/) (SLAC)
- [Deep Learning and Vision](https://indico.cern.ch/event/619372/) --- [Jonathon Shlens](https://research.google.com/pubs/JonathonShlens.html) (Google Research)
- [Fidle - Deep Learning training course](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle)