Introduction to Computational Social Science with R

About

The Digital Revolution has produced unprecedented amounts of data that are relevant for researchers in the social sciences, from online surveys to social media user data, travel and access data, and digital or digitized text data. How can these masses of raw data be turned into understanding, insight, and knowledge? The goal of this course is to introduce you to Computational Social Science with R, a powerful programming language that offers a wide variety of tools, used by journalists, data scientists and researchers alike. Unlike many introductions to programming, e.g., in computer science, the focus of this course is on how to explore, obtain, wrangle, visualize, model, and communicate data to address challenges in social science. The course emphasizes the theoretical and ethical aspects of CSS while covering topics such as web scraping (obtaining data from the internet), data cleaning (getting raw data into a table or otherwise easy-to-analyze format) and visualization (i.e. drawing bar, line, scatter plots and more from data), automated/computational text analysis (i.e. using the computer to find patters in text or sort documents into categories), machine learning (i.e. training algorithms on annotated data and generalizing patterns to unseen data), network analysis (i.e. examining relationships among entities, such as persons, organizations, or documents) and agent based modeling (i.e. simulating interactions between such entities). The course will be held as a blended learning workshop with video lectures focused on theoretical background and demonstrations accompanied by live online sessions where students can ask questions and work through projects together.

Course leader

Johannes B. Gruber, University of Amsterdam, Netherlands.

Target group

You will find the course useful if:

- you have taken, e.g., a statistics course, know a little bit of R, and now want to explore computational methods, data science or one of the approaches listed above.

Course aim

By the end of the course, you will:

- be able to define what constitutes the field of computational social science;
- have a high-level overview of the approaches utilized in computational social science, including advantages and shortcomings;
- have a basic knowledge and hands-on experience of how to apply the approaches and what tools are considered state-of-the-art;
- be equipped to deepen their knowledge on the theory and practice of computational social science.

Fee info