Introduction to Computational Social Science

Catalog description

This course offers an interdisciplinary introduction to the growing field of computational social science, where methods from data science, computer science, and the social sciences converge to examine large-scale social phenomena. Students will learn to collect, clean, and analyze various sources of data—such as complex survey data, social media, network data, and text data—using tools like Python or R. Core topics include text analysis, network analysis, machine learning, and data visualization. The course will focus on the applications of these methods to answer questions in social sciences. Emphasis will also be placed on ethical considerations in data collection and usage, as well as on critical interpretation of computational findings. By the end of the course, students will be prepared to design and conduct their own data-driven social research projects, applying computational methods to real-world problems.