Welcome to Python for Data Science

About

This is a collection of my personal notes for Data Visualization in Python. Originally I had kept these in a collection of Jupyter notebooks, but it will be much more useful to just put them online so I can reference them more easily. This is merely a collection of my personal notes for my personal use, but if you find them useful too that is a bonus.

I've compiled these notes from a variety of sources, but they are most heavily influenced by Jose Portilla's Udemy course Python for Data Science and Machine Learning. These are my notes from his course.

The necessary prerequisites are NumPy and matplotlib. If you are unfamiliar with these libraries we cover these in detail here.

Acknowledgements

• Pacific Institute for the Mathematical Science (PIMS), Compute Canada and Cybera for creating Syzygy and hosting Jupyter notebooks for thousands of students and researchers across Canada. Check syzygy.ca for a list of Canadian Universities that offer a syzygy server to their students, or go to pims.syzygy.ca to login with a google account.
• Jupyter, Python and SciPy developers for creating transformative open source tools.
• Google CoLab for allowing everyone access to Jupyter notebooks with loads of precofigured libraries.
• MkDocs developers and Martin Donath for creating a Material Design theme for MkDocs.
• Jose Portilla for creating the Udemy course Python for Data Science and Machine Learning. These are my notes from his course.
• Patrick Walls for creating the wonderful Mathematical Python book.
• Python for Everybody: Exploring Data in Python 3 by Charles Severance (and his Coursera course of the same name)
• Cracking Codes with Python, by Al Sweigart