Spring Term 2019

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.
 

Advanced Methods | 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. There are no prerequisite requirements for this course.
 

PhD Workshop | Spatial Data Analysis

This workshop introduces spatial data analysis and its applications in quantitative social science research and is intended for PhD researchers and postdocs. The first part will cover some of the foundations of spatial data analysis including basic concepts and definitions but also common methodological challenges (e.g., MAUP, aggregation problems). The remainder of the workshop then focuses on practical challenges for using spatial data, including integration of different spatial data types, the proper handling of event data and their deduplication. And in the last part we cover a number of more recent techniques for quantitative inference in highly disaggregated spatial settings and discuss associated best practices. Many of the examples are drawn from research on sub- national dynamics of conflict where spatial data has been extensively used in recent years but they translate to any other comparable empirical setting. Each of the sections of the workshop consists of a condensed lecture-style introduction followed by a practical session in R. This is an applied workshop and you are encouraged to bring your own projects using spatial data and tackle them as part of the workshop. For those requiring credits from the workshop, there will be the option to hand in a small final assignment that will be graded on a pass/fail basis.

Syllabus