Resources 💻

There is no textbook for this course. Instead, we’re designing the lectures so that they contain everything you’ll need to know for the course. With that said, many students benefit from having textbook-style readings, so we’ve linked some in the course homepage and also include a few more resources below.

Documentation

In this course, you’ll become familiar with reading lots of documentation. If you’re not sure how a function works, you should type the name of the function in a cell by itself followed by ? and run the cell (so for example, numpy.sum?). This will show you that function’s documentation.

Official resources for you to refer to:

• Python documentation
• NumPy documentation
• Pandas documentation

Exam Resources

The following resources can be helpful for you to study for exams.

Semester	Midterm	Final
Spring 2023	Exam (Solutions)

Midterm

• Review Slides
• Reference Sheet
• Example Questions (Solutions)

Final

Stay tuned!

Supplementary Readings

Each of the listed online textbooks/readings will contain some material that is not in scope for our class. They all cover the fundamentals of Python, but in slightly different ways. As a reminder, you’re not required to look at any of these; only look if you think they’ll help you.

• Learning Data Science, the textbook for Data 100 at UC Berkeley. It is an introductory textbook for data science that will be published with O’Reilly Media in 2023. It covers foundational skills in programming and statistics that encompass the data science lifecycle.
• Computational and Inferential Thinking, the textbook for Data 8 at UC Berkeley.
• Stanford’s Python Reference, a Python guide written for Stanford’s intro CS class, is a great reference if you need a refresher on how something works.
• Python Programming for Data Science and Dive Into Data Science are also good references that are written for different classes (at UBC and UCSD, respectively) that cover the material relevant in our class and more.
• Composing Programs, the textbook for CS 61A and CS 88 at UC Berkeley, covers Python from a more traditional computer science perspective rather than the data science perspective we will take; as such, only a few sub-chapters are relevant to us but you may find it useful nonetheless.
• How to Think Like a Computer Scientist is also a great reference.

Other

• Data 100 Textbook Pandas Reference Table
• Data 8 Python reference

Throughout the semester, if you find any external resource (especially one that isn’t linked above) particularly helpful, please let us know!