Introduction to Computational Social Science with Python

The course provides an introduction to the basic computational tools, skills, and methods used in Computational Social Science using Python. Python is the most popular programming language for data science, used widely in both academia and the industry. Students will learn to use common workflow and collaboration tools, design, write, and debug simple computer programs, and manage, summarize, and visualize data with common Python libraries. The course will employ interactive tutorials and hands-on exercises using real social data. Participants will work independently and in groups with guidance and support from the lecturers. The practical exercises are designed to demand more autonomy and initiative as the course progresses over the five days, culminating in an open-ended group project in the last afternoon session.

Course leader

Prof. Dr. Milena Tsvetkova, Dr. Patrick Gildersleve

Target group

Participants will find the course useful if they:
- Have no or limited technical and computational background
- Have a background in one of the social sciences (sociology, political science, psychology, etc.)
- Would like to pursue research or professional career in computational social science or social data science (e.g., in academia, think tanks, government, NGOs, social media companies, tech startups)

Course aim

By the end of the course participants will:
- Possess an understanding of the tools, methods, tasks, and goals of Computational Social Science
- Design procedures and algorithms to solve data analysis tasks
- Write simple programs in Python
- Work confidently with pandas, matplotlib, seaborn, and other popular Python modules and libraries for data science
- Use bash, Jupyter Notebook, and GitHub to write, run, collaborate on, and share programming code