Teaching

Graduate Courses

Topics in Computational Social Science Course Page

The growing availability of new streams of data, expansion of computational power and the digitalisation of our lives has created new questions and research opportunities for social and population scientists. The course will introduce students to a range of methodological and substantive topics in computational social science. We will cover topics such as digital trace and big data, machine learning, non-probability sampling, social networks, and agent-based modelling and microsimulation. The course will consist of seminar and lab sessions (taught in R), where students will engage with research in computational social science and learn to apply basic computational methods to research problems based on existing research papers.

Undergraduate Courses

Introduction to Social Networks Course Page

This course introduces the science of social networks—how people are connected and how those connections shape social behavior, opportunities, and outcomes. We will use concepts and methods from the social, natural, and mathematical sciences to define networks, analyze network data, and examine how networks are applied in both academic research and practice. The course draws on examples from public health (e.g., HIV prevention at CDC and UNAIDS), sociology (e.g., how friendship networks influence educational outcomes), and technology (e.g., the rise and diffusion of social media platforms in Silicon Valley). We will combine theory, empirical research, and hands-on data analysis to build a foundational understanding of social networks.

Workshops

Introduction to R and modern workflow Course Materials Description