Data 88E: Economic Models

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Course Description

UC Berkeley Data 88E: Econonomic Models aims to motivate and illustrate key concepts in economics through a series of exercises and examples that use Python Jupyter notebooks. The class covers concepts from introductory economics, microeconomic theory, econometrics, development economics, environmental economics and public economics. The course provides data science students a pathway to apply Python programming and data science concepts within the discipline of economics. The course will also gives economics students a pathway to apply programming to reinforce fundamental concepts and to advance the level of study in upper division coursework and possible thesis work.

Offerings

Each offering includes links to assignments, slides, and readings. You are welcome to use any of the materials you find.

About the course

This course is offered in partnership with the Data Science Undergraduate Studies, College of Computing, Data Science, and Society. This course was built as a Connector Course to the popular UC Berkeley Data 8 Course. Connector Courses are an in depth exploration of domain applications based on the concepts and tools learned in Data 8.

This course designed to bridge the gap between data science and economics by using real-world datasets and Python programming to illustrate key economic concepts. The course is intended for students from both data science and economics backgrounds, giving them a chance to apply computational tools to economic models.

The course covers a range of topics including:

  • Introductory Economics
  • Microeconomic Theory
  • Econometrics
  • Development Economics
  • Environmental Economics
  • Public Economics

Students work with Python in Jupyter Notebooks to explore economic concepts such as supply and demand, market equilibrium, utility, game theory, and more. The course emphasizes how economic decisions are influenced by real-world data and policy interventions, making it a practical intersection of economics and data science

In addition, the course has been particularly valuable for data science students looking to apply their programming skills in economics and for economics students who want to reinforce their learning with computational tools, potentially aiding in advanced coursework and thesis preparation.

This course is intented for lower-division students and as of 2024 is offered in the Fall. If you are interested in our upper division course that grew out of this course in 2022 in partnership with the Economics Department, please check out Economics 148, which is currently offered each Spring Semester.

Python Packages

As a connector course to the main Data 8 course, Data 88E labs mostly use a specific Python package developed at UC Berkeley for teaching. This package is known as datascience tables. Furthermore the notebooks also make extensive use of a Python package for automatic grading called otter-grader.

GitHub Repository

The official GitHub organization for Data 88E contains the course materials, Jupyter notebooks, and other resources used in the course. All of the labs and projects can be found here, as well as the source material for the textbook.

edX Course

We are proud to announce that Data 88E, is now offered on edX, divided up into three segments. There are professionally produced videos, a guide through the material with online quizzes, and a set of labs.
Data 88E on edX.

External to UC Berkeley

If you are external to UC Berkeley, some of the links on this website may lead to UC Berkeley only material requiring authentication. You should be able to find the lectures slides, and textbook from the website. The lecture notebooks, labs and projects should be findable on Github - where you can download them and open either locally, or via something like Google Colab. Unfortunately the videos are not shareable at this time, but a series of videos can be found at the EdX course. If you are an educator we make many resources available around sharing the Data 8 material Zero to Data 8.

We are always interested to know about other educators who are innovating in this space! Please reach out if you are teaching in this space, interested to collaborate, have questions about the material Github Discussions.

A few colleagues that we would love to highlight are