Learn essential economic concepts through data science applications and Python programming, focusing on demand, supply, and market equilibrium.
Learn essential economic concepts through data science applications and Python programming, focusing on demand, supply, and market equilibrium.
This innovative course bridges the gap between economics and data science, offering a unique approach to learning fundamental economic concepts through programming. Based on UC Berkeley's connector course, students learn to analyze and visualize economic principles using Python programming. The course covers key microeconomic concepts including demand, supply, equilibrium, and taxes, while simultaneously developing practical programming skills. Through hands-on applications, students learn to create visualizations of economic concepts, code simple equations, and analyze real-world economic data. This interdisciplinary approach provides a modern perspective on traditional economic theories, making it ideal for students interested in both economics and data science.
Instructors:
English
English
What you'll learn
Understand basic economic concepts through programming applications
Visualize demand and supply curves using real data
Develop Python coding skills for economic analysis
Create graphs for microeconomic concepts
Apply programming to solve economic problems
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
This course combines economic theory with practical data science applications. Students learn fundamental economic concepts while developing programming skills in Python. The curriculum covers essential microeconomic principles including demand, supply, market equilibrium, and taxation, all taught through a computational lens. Students learn to visualize economic data, code economic models, and analyze market dynamics using modern programming tools.
Fee Structure
Instructor

6 Courses
A Pioneering Leader in Data Science Education and Curriculum Development
Eric Van Dusen serves as a Lecturer and Tech and Outreach Lead in Data Science Undergraduate Studies at UC Berkeley's College of Computing, Data Science, and Society, where he has transformed data science education over the past decade. After earning his BS from UC Berkeley and PhD in Agricultural and Resource Economics from UC Davis, with specializations in International Development and Econometrics, he has built an impressive career combining economics and data science education. Since joining Berkeley's Data Science program in 2017, he has pioneered innovative curriculum development, including creating new courses that bridge data science with economics and international development. As Director of Curriculum in the Division of Data Science and Information, he has led significant initiatives including the development of Data Science Modules, Connector courses, and the expansion of data science offerings across disciplines. His work has been instrumental in making data science education more accessible, particularly through the implementation of cloud-based infrastructure using Jupyter notebooks. Van Dusen's impact extends beyond Berkeley through his leadership in the National Workshop on Data Science Education and the California Alliance for Data Science Education, where he works to make data science education broadly accessible to community colleges and other institutions. His international experience spans projects in Mexico, Central America, Uzbekistan, and Kenya, contributing to his unique perspective on applying data science to real-world challenges.
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