Collective Behavior & Social Movements  
This course explores the origins, dynamics, and consequences of social movements. We examine a wide range of topics including: the emergence of movements, recruitment, interactions between social movements and the general public or political officials, tactics, and the factors contributing to the success and failure of movements.

Justice Studies  
This course explores economic, social, and criminal justice issues by means of sociological, philosophical, and legal perspectives and methodologies. Students critically assess the obstacles and opportunities central to the pursuit of justice in the United States. In this class, we will critically interrogate the meaning of "justice" in an attempt to answer the question: "justice for whom?"

Graduate Statistics  
This course provides students with advanced analytical skills necessary for understanding, interpreting and drawing conclusions from statistical analysis of data. We briefly review univariate and bivariate statistics before covering multivariate methods, including ANOVA (RMANOVA, MANCOVA); OLS regression, logistic regression (binary, ordered, multinomial), and count models (Poisson and negative binomial). Advanced methods such as event history/survival analysis, time series analysis, multilevel modeling, and structural equation models are introduced.

Statistics for the Social Sciences  
This course provides students with the skills necessary for understanding, interpreting and drawing conclusions from statistical analysis of data. We cover univariate and bivariate statistics, including probability and the normal curve; measures of central tendency, variation/dispersion, and confidence intervals; comparing means and proportions for two groups (t-tests); comparing means for more than two groups (ANOVA); correlation, and regression.

Social Research Methods  
An introduction to methods of sociological research. This course is designed to help students critically evaluate research and conduct research of their own. Topics covered include the ethics of research, the relationship between theory and research, variables and measurement, causality, types of research (qualitative fieldwork and interviews, content analysis, and quantitative analysis), and the writing of research.

Exercise: Using Machine Learning to Predict Legalization  
An brief introduction to machine learning applied to a specific research problem: predicting statewide legalization in the U.S.