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.
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,
Table.read_table?). This will show you that function’s documentation.
Official resources for you to refer to:
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. Only the first link will contain any code that uses the
datascience library that we use in this course. As a reminder, you’re not required to look at any of these; only look if you think they’ll help you.
- Computational and Inferential Thinking, the textbook for Data 8 at UC Berkeley, overlaps with our course significantly, though there’s a lot in there that we won’t cover and there’s a lot that we’ll cover that won’t be in there.
- 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 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.
Throughout the semester, if you find any external resource (especially one that isn’t linked above) particularly helpful, please let us know!