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.
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.
Selected Topics in Data Science
Information Science is an interdisciplinary research field primarily addressing the retrieval, analysis, manipulation, storage, usage, and dissemination of information. The topic suggestions in this seminar are motivated by real world problems and recent research trends, such as detecting plagiarism in scientific documents, analyzing differences and frames in news coverage, exploring new applications of cryptocurrencies. Seminar participants will gain an overview of the state-of-the-art technologies for different Information Science problems. They will be able to describe the current research trends, research challenges, and the predominant approaches for tackling these research challenges. 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.