Foundations of Data Science for Social Scientists
This course introduces foundational concepts from computer science that are fundamental to data science approaches in the social sciences. Topics covered range from basic principles of information coding, data types and structures, algorithms and their complexity, to efficient approaches for automated data collection, storage, processing and analysis. For each topic, the course first covers the conceptual foundations from computer science before illustrating them using hands-on examples from the social sciences. Throughout the class we also cover general best practices (e.g., reproducibility, version control using Git) for data science. The course uses the R statistical programming language as teaching language and a basic familiarity is assumed.
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.