Resources

Notes, Links, and More

This page will be “under construction” throughout the semester. We will add resources here as appropriate.

Notes

• Computing Setup Guide

Additional Resources

Below here we will collect free resources that could be useful for machine learning, data science, and related topics. Some recourses could be useful for refreshing background knowledge, while others help explorer content beyond the scope of the course. We’ll try to add some commentary that helps you understand how you might benefit from each item. Consider these resources as a good start to a virtual data science bookshelf that you should add to over time.

You are not expected to engage with any of these materials! Readings that are required to complete CS 307 will be noted as such elsewhere, and will mainly consist of the notes above.

As the list grows, we might attempt to organize and categorize items, but for now, they’ll simply appear in an unordered list.

• Book: Think Python by Allen Downey
  • A fantastic quick introduction to both Python and programming in general. As a data scientist, you will spend more time using tools like numpy, pandas, and scikit-learn, but it would be wise to have a general understand of Python the language. While the text directly teaches Python, it indirectly teaches you to “think like a computer scientist,” as the subtitle suggests. If you’ve done some programming, but never thought about higher-level programming patterns, this book is a great fit for you.
• Book: Python for Data Analysis by Wes McKinney
  • Ever wanted to know everything about pandas? This book, written by the original pandas developer overviews numpy then dives into the details of pandas.
• Documentation: The Markdown Guide
  • Markdown has quickly become a lingua franca for writing in data science. It’s used in Jupyter notebooks, Quarto documenting, GitHub README files, and more. This documentation website can serve both as a learning tool, and later as a quick reference guide.