Computational Social Science - JSM176

• Title: Computational Social Science
• Guaranteed by: Department of Sociology (23-KS)
• Faculty: Faculty of Social Sciences
• Actual: from 2023
• Semester: summer
• E-Credits: 3
• Examination process: summer s.:
• Hours per week, examination: summer s.:0/2, C [HT]
• Capacity: unknown / unknown (unknown)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Teaching methods: full-time
• Guarantor: doc. Mgr. Jakub Grygar, Ph.D.
• Class: External course, not for registration

Annotation

Last update: doc. Mgr. Jakub Grygar, Ph.D. (26.03.2023)

The course is lectured dr. Mikołaj Biesaga (University of Warsaw)

This course is an introduction to data science for social scientists. During the course students will learn, using practical examples, how new computational methods may be applied to social psychology and social sciences in general and how they can be used to study phenomena that are hard to track with traditional methods. After the course a student should know what the available tools are, how they work, and how they might be applied to answer questions social scientists may ask. The course will cover basic concepts of computational social science such as how to use external data sources (primarily web-based), most important web data formats, popular computational tools and environments, working with APIs, webscraping, and Natural Language Processing (NLP). Each topic will be illustrated with real-life examples, and students will have the possibility to not only learn basic concepts and see real-world applications but also apply the methods in practice working on very simple examples.

By the end of the semester students should be able to:
- understand basic concepts of computational social science.
- communicate with data scientists / computer programmers etc. (using adequate vocabulary).
- understand advantages, challenges, and limitations of computational methods in social sciences.
- formulate research questions that can be addressed with computational methods and/or data extracted from existing web-based data sources.
- plan research using computational methods (especially webscraping, web API data extraction, and natural language processing)
- use materials from the course to scrap a website, work with a simple API, and perform basic Natural Language Processing.