Resources and Readings

The Resources page on the course web site has a more complete list of resources that I will update throughout the semester.

Textbook

Our primary textbooks are:

Python for Data Analysis (2nd Edition) by Wes McKinney (O’Reilly, ISBN 978-1491957660)

Think Like a Data Scientist by Brian Godsey (Manning, ISBN 978-1633430273)

A good additional data science text is:

A Hands-On Introduction to Data Science by Chirag Shah (Cambridge)

If you want a more thorough treatment of the core Python language traditional book format, I recommend:

Learn Python the Hard Way by Zed Shaw

Online Readings

Throughout the semester, I will assign various readings from the Internet and research papers. These will be posted to the appropriate week’s page the course web site.

Software

We will be using Python with the PyData tools (Pandas, Numpy, Scipy, etc.). The easiest way to install the required software is to install Anaconda Python. The various Python libraries we use each have their own documentation.

Due to wide variations in system configurations, I cannot provide support for debugging Python installations other than Anaconda (and other Conda distributions, like miniconda and miniforge). I have made sure that the course examples and assignments work with packages readily installable from Conda on all major platforms (Windows, Linux, and MacOS, the latter on both Intel and Apple Silicon processors).

Further information about software, and links to documentation, can be found in the course resources.