´╗┐´╗┐ GESIS Training Courses

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Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Claudia O'Donovan-Bellante
Tel: +49 621 1246-221

Introduction to Computational Social Science with Python

Mannheim B6, 4-5
General Topics
Course Level
Software used
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Additional links
Lecturer(s): Prof. Dr. Milena Tsvetkova, Dr. Patrick Gildersleve

About the lecturer - Prof. Dr. Milena Tsvetkova

About the lecturer - Dr. Patrick Gildersleve

Course description

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.
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.

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)

  • Learning objectives

    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
    Organizational structure of the course
    The course will consist of two three-hour-long sessions. The morning session will use interactive instruction to introduce participants to the topic, demonstrate the new methods, and facilitate discussion. The afternoon session will make use of guided hands-on exercises with real-world data to practice the new material. Participants will work individually, in pairs, and in groups and the lecturers will be available throughout both sessions for consultation and support.


    This is an introductory course and no prior experience with programming is required. A basic understanding of statistics and some scripting experience (e.g., from building web pages or statistical analysis programs such as Stata) will be helpful but not needed.
    Software and hardware requirements
    Participants should bring their own laptops and pre-install the following software/packages:
  • Anaconda
  • Git  
  • Recommended related courses
  • Python 101 (Workshop, online, 31.08. - 01.09.2022)
  • Automated Web Data Collection with Python (Fall Seminar, Mannheim, Week 2)
  • Introduction to Machine Learning for Text Analysis with Python (Fall Seminar, Mannheim, Week 3)