The goal of data science is to use empirical information from a broad range of sources to improve our understanding of the world around us. Economists increasingly rely on the tools of data science to access novel sources of data and information, characterize the economic environment, and conduct empirical analysis. This course provides hands-on introduction to data science tools most relevant for economic analysis including data visualization, exploratory data analysis, and statistical learning. The objective of the course is to help students: (i) formulate economic research questions that can be explored using data science tools, (ii) identify sources of data and prepare data for analysis, (iii) produce persuasive visualizations, and (iv) analyze data using both classical statistics and machine learning.
The Class: Format: lecture; The course includes traditional lectures, interactive activities in both Stata and R, and in-class presentations by students. Some prior knowledge of either Stata or R is helpful, but not required.
Requirements/Evaluation: Grades are based on in-class participation and performance on two take-home exams as well as problem sets and data visualization/analysis projects.
Prerequisites: ECON 255 or STAT 201, STAT 202, or STAT 346 or permission of instructor
Enrollment Preferences: Economics majors
Distributions: Division II