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. 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.
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 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.
Cryptocurrencies and Blockchain in Practice
Seminar participants will explore and apply current research approaches in the cryptocurrency and blockchain field. Topic suggestions include, but are not limited to: Blockchain for Science, Smart Contracts, Trusted Timestamping, and Privacy Preserving Recommendation Systems. For the theoretical research project, each participant will perform an in-depth literature review on current research approaches that address a particular research task. The participants will present their findings in an academic paper and a 30 minute long presentation during one of the seminar sessions. For the practical research project, participants will pick a real-world application problem. The participants will design and implement an application that addresses the identified problem; strong implementation skills e.g. Java, Python, Angular2, Solidity are here a prerequisite.