Teaching

I teach classes on Computational Social Science, Research Methods in general, and Innovation/Sociology of 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.

  • Syllabus: Google Doc link

• SI 611 Computational Social Science: "This course introduces the field of computational social science, which combines social theory with data science methods. First, you’ll learn to think like a social scientist – by drawing on building blocks such as preferences, norms, biases, and social networks, you will turn hunches about human behavior into answerable research questions and testable explanations. Second, you’ll learn to test those explanations using quantitative methods including description, regression, machine learning, natural-language and image processing, and causal inference methods like experiments and quasi-experimental designs. Classes will blend discussions of influential papers with hands-on Python labs, where you’ll analyze real-world data from governments and platforms such as Reddit and OkCupid. By the semester’s end you’ll have a toolkit of methods and theories for analyzing and explaining human behavior and be a sharper consumer of research and current events."

  • Syllabus: Google Doc link

• 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."

  • Syllabus: revising

• 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."

  • Syllabus: Google Doc link