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.

You can send us changes to the course website by forking and sending a pull request to the course website github repository. You will then become part of the history of the DS100 class at Berkeley.

Web References

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.