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Resources
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Resources to help you throughout the semester.

Resources 💻

Textbook

We've got a textbook for this course! It's written by our wonderful uGSI Rohan Jha, and is a massive resource to build a solid foundation of data science for economists. We'll make sure to link relevant readings as the course progresses, but feel free to read ahead.

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:

Past Exams

Here is a collection of our prior exams.

Semester Midterm Final
Spring 2024 Exam (Solutions, Reference Sheet) None
Spring 2023 Exam (Solutions) Data Task, Conceptual (Data Task Solutions, Conceptual Solutions)

Midterm

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

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