This applied research seminar introduces students to the field of computational social science. It covers four core research areas in the field: automated data extraction, social complexity, computational simulations and social network analysis. Each topic is introduced over several sessions. Assigned readings cover foundational work and key methodological contributions as well as current examples from social science research. The course highlights technical strengths and limitations of the various approaches introduced. It also critically reflects on where and how specific computational approaches can contribute to answering substantial social science research questions. It further provides an overview of existing tools implementing the various approaches discussed. As part of the seminar, students pursue an independent research project using computational social science approaches. For students in the SEDA master, the course is recommended to satisfy the Data Analysis Project requirement. There are no strict formal prerequisite requirements for this course but good programming skills and a strong background in (quantitative) research methods and statistics are expected.