I teach classes on the sociology of science/innovation and computational social science. Below is a bit of information and links to syllabi:

  • SI 840 Research Methods: A Ph.D. level introduction to research design. Examines various quantitative and qualitative research methods (experiments, surveys, simulations, interviews, ...) with illustrations drawn from specific studies. Discusses problem selection, data collection, data analysis, and research evaluation. Develops a researcher-level appreciation of the strengths and weaknesses of problem-method combinations. Co-taught with Mark Ackerman. My part of the course focuses on the computational social science toolkit.

  • SI 699 Big Data Analytics: A Master's level course developing a capstone project. "This course will require students to demonstrate mastery of data collection, processing, analysis, visualization, and prediction. To develop these skills students will work on semester-long projects that deal with large or industry-scale data sets, and solve real-world problems. Aligned with best industry practices, students will be expected to work in a fast-paced, collaborative environment, while demonstrating independence and leadership. Students must be able to create and use tools to handle very large transactional, text, network, behavioral, and/or multimedia data sets."

  • SI 710 Science of Science: "This doctoral seminar examines science as an institution, drawing on research from sociology, economics, history, philosophy, and interdisciplinary approaches. We will explore what, if anything, makes science a special institution that’s different from others, how knowledge accumulates, what determines the rate and direction of that accumulation, how science influences the broader society and economy and how they influence science."