1. Introduction to Numpy
2. Introdution to Pandas
3. Basic Data Visualisation
4. Machine Learning with Scikit-Learn

1. Numpy Challenges
2. Scipy Sparse Challenges
3. Pandas Challenges
4. [Machine Learning Case Study & Challenges](./sklearn/case study)

Differently from other sections, Data Science Challenges will be provided as Jupyter Notebooks. If you don't know what a Jupyter notebook is, or how to use it, please take a look at this quick introductory tour: IPython Notebook Beginner Guide.

For additional details and materials on Jupyter and IPython, I suggest this Jupyter Notebook the Definitive Guide:

• What is a Jupyter Notebook
• Practical Introduction
• Notebook Examples

If you want an introductory overview of Python for Data Science, I strongly recommend
Scipy Lecture Notes: a community driven project where you can find tutorials (for non-experts) on the scientific Python ecosystems.

Additional Books for further readings:

• Scipy and Numpy
• Python Data Science Handbook
• Elegan Scipy
• Python for Data Analysis
• Introduction to Machine Learning with Python
• Building Machine Learning Systems with Python

• Materials in the numpy section is adapted and updated from https://github.com/leriomaggio/numpy_euroscipy2015 © 2015-2018 Valerio Maggio.

• The materials in the pandas section has been mostly taken and adapted from https://github.com/alanderex/pandas-pydata-2017 © 2015-2017 Alexander C.S. Hendorf, Königsweg GmbH, Mannheim

  • The Pandas anti-patterns section has been adapted from the fantastic tutorial @ PyCon Otto by Pietro Battiston Pandas, the wrong way source: https://pietrobattiston.it/python:pycon

• The materials in the matplotlib section has been mostly taken and adapted from https://github.com/matplotlib/AnatomyOfMatplotlib