GESIS Training Courses

Scientific Coordination

Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Loretta Langendörfer M.A.
Tel: +49 221 47694-143

Introduction to Computational Social Science with Python

Dr. Orsolya Vásárhelyi, Luis Natera

Date: 13.09 - 17.09.2021 ics-file

Location: Online via Zoom

About the lecturer - Dr. Orsolya Vásárhelyi

About the lecturer - Luis Natera

Course description

This course provides a highly interactive introduction to Computational Social Science using Python. Python is the most popular programming language of data science, used in natural language processing, machine learning, and artificial intelligence. This five-day course is designed for social scientists who would like to conduct data collection, analysis, and modelling with Python and attend further courses within the GESIS Fall Seminar in Computational Social Science. Classes focus on hands-on exercises, therefore participants are required to watch pre-recorded videos about the theoretical background that is needed to start your journey in computational social science (e.g.: software installation, data ethics, tips on how to visualize data).


Target group

  • Class is specifically designed for participants with no (or limited) technical background who would like to start their journey to analyze data using Python.
  • Participants have various backgrounds usually within the social sciences (e.g.: sociology, political science, psychology)
  • Course is recommended but not limited to professionals working/planning to work with data in NGOs, research institutes and universities in any level (Ph.D. candidates, postdocs, professors)

  • Learning objectives

    By the end of the course participants will:
  • Understanding of what Computational Social Science is
  • Introductory skills to programming in Python
  • Basic data collection methods: scraping, APIs
  • Basic data analysis using pandas (e.g.: data frames)
  • Data visualization
  • Ethical concerns of working with data
    Organisational Structure of the Course
    The course is structured around 30 minutes to 1.5 hours of preparation time, which includes following tutorials and listening to pre-recorded lectures, and 3 to 4 hours of lab sessions depending on the preparation load. Total hours of the course do not exceed 5 hours daily, including preparation time, however we expect participants to arrive to the class prepared.
    Lab sessions are structured the following way: At the morning sessions, the instructor showcases an example of the given topic by writing code live with the help of participants. During this time, the other instructor is available online for debugging, and helping participants to follow the class. At the afternoon sessions, participants work on projects in groups and later demo their solution to the class. Lecturers will be available for individual consultations on participants' projects outside of class hours.


    No programming experience needed. A basic understanding of statistics and quantitative data analysis will be helpful. Prior experience with writing code (e.g., syntax for statistical analysis programs) is a plus.
    Software requirements
    Participants should install Python on their computers prior to the course. Participants need to have a Google account, because we will use Google Colab and Google Drive throughout the course. For communication purposes a specific Slack channel will be established, where participants are invited to join. Classes will take place via Zoom.