DATA ANALYSIS WITH PYTHON

This course is meant to provide an introduction to regression and applied statistics for the social sciences, with a strong emphasis on utilizing the Python software language to perform the key tasks in the data analysis workflow. Topics to be covered include various data structures, basic descriptive statistics, regression models, multiple regression analysis, interactions, polynomials, Gauss-Markov assumptions and asymptotics, heteroskedasticity and diagnostics, data visualization, models for binary outcomes, models for ordered data, first difference analysis, factor analysis, and cluster analysis. Through a variety of lab assignments, students will be able to generate and interpret quantitative data in helpful and provocative ways. Only relatively basic mathematics skills are assumed, but some more advanced math will be introduced as needed. A previous introductory statistics course that includes linear regression is helpful, but not required.