DATA SCIENCE FOR AGRICULTURAL AND APPLIED ECONOMICS

Prerequisites & course information

Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Summer 2026 Learning Outcomes: 1. Use Python and R to load, clean and visualize data. Audience: Graduate 2. Prepare and manipulate/transform data to support analysis in Python and R. Audience: Graduate 3. Create effective visualizations to communicate complex data. Audience: Graduate 4. Implement Simple Linear Regression in Python and R. Audience: Graduate 5. Apply the above R and Python methods to public-use datasets related to agricultural and applied economics. Audience: Graduate Learning Outcomes: 1. Use Python and R to load, clean and visualize data. Audience: Graduate 2. Prepare and manipulate/transform data to support analysis in Python and R. Audience: Graduate 3. Create effective visualizations to communicate complex data. Audience: Graduate 4. Implement Simple Linear Regression in Python and R. Audience: Graduate 5. Apply the above R and Python methods to public-use datasets related to agricultural and applied economics. Audience: Graduate