We will be posting all lecture materials on the course syllabus. In addition, they will also be listed in the following publicly visible Google drive folder.
Here is a collection of resources that will help you learn more about various concepts and skills covered in the class. Learning by reading is a key part of being a well rounded data scientist. We will not assign mandatory reading but instead encourage you to look at these and other materials. If you find something helpful, post it on Piazza, and consider contributing it to the course website.
As a data scientist you will often need to search for information on various libraries and tools. In this class we will be using several key python libraries. Here are their documentation pages:
Because data science is a relatively new and rapidly evolving discipline there is no single ideal textbook for the course. Instead we plan to use reading from a collection of books all of which are free. However, we have listed a few optional books that will provide additional context for those who are interested.
Introduction to Statistical Learning (Free online PDF) This book is a great reference for the machine learning and some of the statistics material in the class
Data Science from Scratch (Available as eBook for Berkeley students) This more applied book covers many of the topics in this class using Python but doesn’t go into sufficient depth for some of the more mathematical material.