Master economic concepts through practical data science applications, including inequality analysis, econometrics, and financial modeling using Python.
Master economic concepts through practical data science applications, including inequality analysis, econometrics, and financial modeling using Python.
This advanced course bridges economics and data science, teaching students to apply Python programming to analyze real-world economic phenomena. The curriculum covers sophisticated topics from upper-division economics courses, presented through an applied lens with practical datasets. Students learn to implement key economic concepts using Python, including the Lorenz Curve, Gini Coefficient, and financial API integration. The course emphasizes hands-on experience with econometric tools, environmental economics applications, and financial analysis, providing a unique blend of theoretical understanding and practical implementation.
Instructors:
English
English
What you'll learn
Master game theory and oligopoly analysis using Python
Implement inequality measures like Lorenz Curve and Gini Coefficient
Conduct econometric analysis using statsmodels package
Analyze randomized controlled trials in development economics
Create environmental economic models for emissions analysis
Calculate present value and financial metrics
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
The course combines advanced economic concepts with practical data science applications. Through five comprehensive modules, students explore topics like game theory and oligopoly, inequality measurements, international development, econometrics, and environmental economics. The curriculum emphasizes hands-on programming experience using Python to analyze real-world economic datasets and implement economic models. Students learn to use various tools and packages for economic analysis, including statsmodels for econometrics and financial APIs for market data analysis.
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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