Introduction to Python for Data Sciences

Franck Iutzeler

Outline

Chap. 1 - Introduction to Python

Basics

Chap. 2 - Python for Scientific Computing

NumPy and co.

Chap. 3 - Data Handling with Pandas

3.1 - Pandas

3.2 - Dataframes

3.3 - Fancy Visualization with Seaborn

Chap. 4 - Machine Learning with ScikitLearn

4.1 - Scikit Learn

4.2 - Supervised Learning

4.3 - Unsupervised Learning

4.4 - Going Further

Credits

I am greatly indebted to many people and resources for this course

• Alexandre Gramfort for his kind permission tu use some material from his course at Telecom Paris and his scikit-learn tutorial at SciPy 2016.
• Adeline Leclerc Samson for giving me the opportunity of teaching this course.
• Anatoli Juditski for his advice and the conception of the Operation research course.
• The Python Data Science Handbook by Jake VanderPlas, see on GitHub.
• Many users feedbacks.
• the various docs and examples on the websites of Numpy, Scipy, CvxOpt, Pandas, Scikit Learn, TensorFlow
  • Numpy's quickstart tutorial
  • [Numpy for Matlab users](https://docs.sci