Digital Strategies for Social Science Research

Aim

After completion of the course, the student should on an advanced level be able to:

• design a strategy to locate digital data relevant to social science research questions, while understanding potential merits and limits of these data;
• identify and evaluate ethical issues related to the use of digital data sources;
• collect, store and format digital data using appropriate software and programming tools;
• explore and analyze digital data with various statistical and computational techniques, from descriptive statistics to machine learning;
• compare classic statistical and machine learning tools for constructing social scientific measures based on digital data.

Content

This course introduces the theories and practices of digital social sciences. The course considers the respective relevance of various digital data sources (sensors, surveys, internet-based media platforms, etc.) for social scientific purposes. In hands-on computer labs, the functioning and structure of the World Wide Web and its mark-up languages are examined. Programming tools for extracting information from these structure, especially the manipulation of text and text files, are introduced. Finally, training in the use of common statistical and computational tools to explore and analyze data (descriptive statistics, dimensionality reduction, clustering) is provided, including a comparison between classic statistical methods and machine learning.