Fall Term 2019

Computational Social Science

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. 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.


Introduction to Computation for the Social Sciences

This lecture serves as an introductory course to computer science and programming for a social science audience. The main emphasis of the course is on providing students with a good conceptual understanding of fundamental principles in computer sciences and of basic programming concepts. Topics covered range from basic principles of information coding, computer systems and information storage, to data types, data structures, algorithms, different programming paradigms and database systems. Concepts are taught “in context” throughout the lecture, i.e., students will learn concepts and directly apply them in programming exercises structured along relevant social science applications. The lecture will rely on Python as teaching language.


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 problems. 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. These exercises are designed to familiarize students with the format of big data and to gain a first, hands-on experience with potential applications for large, complex data in policy-relevant settings. The block course is designed as a primer for anyone interested in attaining a basic understanding of what big data analysis entails and does not entail technical training for scripting etc. There are no prerequisite requirements for this course.