Introduction to Computational Social Science
This course 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 first briefly introduced conceptually and then relevant foundational and applied research papers are discussed. Selected readings accompany each section and additional recommended readings are provided that allow students to delve deeper into selected topics. Where possible, relevant data and applications are illustrated with practical hands-on examples. The objective of the course is to familiarize students with quantitative approaches at the intersection of social science research and computational methodologies while reflecting on their strengths and limitations. As part of the seminar, students develop the concept for a research design using computational social science methodologies. The course is a conceptual introduction, no specific technical skills are required.
Big Data Analysis
This block course provides a basic introduction to big data and corresponding quantitative research methods. The objective of the course is to familiarize students with big data analysis as a tool for addressing substantive research questions. The course begins with a basic introduction to big data and discusses what the analysis of these data entails, as well as associated technical, conceptual and ethical challenges. Strength and limitations of big data research are discussed in depth using real-world examples. Students then engage in case study exercises in which small groups of students develop and present a big data concept for a specific real-world case. This includes practical exercises to familiarize students with the format of big data. It also provides a first hands-on experience in handling and analyzing large, complex data structures. The block course is designed as a primer for anyone interested in attaining a basic understanding of what big data analysis entails. There are no prerequisite requirements for this course.
Advanced Quantitative Methods: Agent-Based Computational Modeling
This course aims to familiarize participants with the methods of computational agent-based modeling (ABM). Computational modeling techniques are commonly not part of the standard repertoire of quantitative analyses in the social sciences. They have increasingly gained prominence though as powerful techniques that can effectively compliment more standard approaches such as regression analyses, in particular, in settings characterized by complex systemic interactions. The course will first introduce the theoretical foundations of the technique in the field of complexity theory. Students will then learn the methodology of agent-based modeling. The course covers classical examples such as Schelling’s model of segregation and then familiarizes students with more complex recent models. Students will then learn about the state-of-the-art approach of evidence-driven modeling, i.e., embedding and validating agent-based models using empirical data. The course places a particular emphasis on highlighting strengths and weaknesses of computational modeling approaches for quantitative analyses and will carefully place the methods in the context of other quantitative techniques more commonly used in the social sciences. Students will also gain first-hand experience in implementing and applying the methods learned through practical programming assignments and a coding project in Python or R. The course therefore assumes that students have some programming background.