Learn to analyze economic concepts using Python and data science techniques in this intermediate-level 4-week course.
Learn to analyze economic concepts using Python and data science techniques in this intermediate-level 4-week course.
This intermediate course, part of Berkeley's Data Science and Economics series, combines economic theory with practical Python programming applications. Students explore advanced economic concepts including production theory, utility maximization, and income inequality through data science approaches. The curriculum features hands-on programming challenges implementing Cobb-Douglas functions, constrained optimization, and analysis of international development data, providing a unique bridge between economic theory and computational methods.
Instructors:
English
English
What you'll learn
Implement Cobb-Douglas production and consumption functions in Python
Analyze utility maximization and constrained optimization problems
Calculate income inequality measures including Lorenz Curves and Gini Coefficients
Evaluate international development trends using data analysis
Develop demand curves from utility functions
Assess national production accounts through computational methods
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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There are 4 modules in this course
This comprehensive course bridges economics and data science through practical Python applications. Students learn to implement economic models computationally, covering topics from production theory to international development. The curriculum includes hands-on programming with Cobb-Douglas functions, utility maximization problems, and analysis of inequality measures. Special attention is given to real-world applications using actual economic datasets, making abstract concepts concrete through computational methods.
Production and Cobb-Douglas
Module 1
Utility Maximization
Module 2
Inequality
Module 3
International Development
Module 4
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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