Below is a list of some of the courses I currently teach or have taught. Syllabi are available upon request.

• Social Stratification [Description Who gets what and why? Does a person's social origin determine their social position later in life? How do some groups maintain greater access to money and power than do others? Is wealth, influence, and prestige concentrated in a small group of 'power elite'? And, when and why do the powerful fall? These are the core questions addressed by the sociological study of stratification. This class explores some of the answers with a focus on social mechanisms and processes. ]
• Sociology of Culture [Description Introduction to the ways sociologists and other social scientists think about this thing called "culture," and help students become educated observers and analysts of culture throughout their lives. Topics include: how our brains and bodies are shaped during enculturation and socialization; how art, music, fashion, and myths are produced and shape our lives; how culture diffuses and changes; and how taste, knowledge, and values relate to status, power, and inequity. ]
• Understanding Social Networks [Description Who knows who? Who knows what? Who talks with whom? Who is influential? Why are your friends more popular than you are? How do ideas, diseases, fashion trends and innovations spread through groups? How do social networks evolve? How do networks constrain and enable behavior? What are network cascades? Such questions and more can be answered using network analysis. Network analysis is a fast‐growing interdisciplinary field aimed at understanding simple and high dimensional networks, from both a static and a dynamic perspective. This class will explore key concepts and methods for understanding networks and how they shape social life. Hands-on analysis in the R statistical computing environment will be integral to the course, though no prior coding experience is expected. ]
• Computational Text Analysis [Description The focus of this course is on understanding and developing some of the fundamentals for designing and conducting computational text analysis projects from a social science perspective. We will also touch on some of the more advanced topics in this rapidly growing field, such as sentiment analysis, classifiers, structural topic modeling, text networks, and word embeddings. Hands-on analysis in the R statistical computing environment will be integral to the course, though no prior coding experience is required. ]