GESIS Training News

The GESIS Fall Seminar 2025 takes place from 01 to 26 September 2025 and offers a variety of introductory and advanced courses in computational social science methods. Some courses are held in-person in Mannheim, others online – and keep an eye out for our blended learning format! The Fall Seminar targets researchers who want to collect and analyze data from the web, social media, or digital text archives. All courses feature an interactive mix of lectures and hands-on exercises, giving participants the opportunity to apply these methods to data. Please find our full course program below or on our website.

Week 1 (01–05 September)

Introduction to Computational Social Science with R [online blended learning]

Johannes B. Gruber (GESIS)    

Introduction to Computational Social Science with Python [online blended learning]

John McLevey (Memorial University)   

Week 2 (08–12 September)

Web Data Collection with R [online]

Iulia Cioroianu (University of Bath)

Web Data Collection with Python [online]

Iulia Cioroianu (University of Bath)

Week 3 (15–19 September)

Introduction to Machine Learning for Text Analysis with Python [Mannheim]

Rupert Kiddle & Damian Trilling (Vrije Universiteit Amsterdam)

Computer Vision for Image and Video Data Analysis with Python [Mannheim]

Andreu Casas (Royal Holloway University of London)

Advanced Methods for Social Network Analysis with R [Mannheim]

Lorien Jasny (University of Exeter)

Week 4 (22–26 September)

From Embeddings to LLMs: Advanced Text Analysis with Python [Mannheim]

Hauke Licht (University of Innsbruck)

Agent-Based Computational Modeling [Mannheim]

Daniel Mayerhoffer (University of Amsterdam)

Causal Machine Learning [online]

Marica Valente (University of Innsbruck)

ECTS Credits & More

For those with no prior experience in R or Python, or for those who would like a refresher, we are also offering two online pre-courses: Introduction to R (25-27 August) and Introduction to Python (25-28 August).

All courses are stand-alone and can be booked separately – feel free to mix and match to build your own personal Fall Seminar experience that perfectly suits your needs and interests. There is no registration deadline, but places are limited and allocated on a first-come, first-served basis. To secure a place in the course(s) of your choice, we strongly recommend that you book early.

Thanks to our cooperation with the a.r.t.e.s. Graduate School for the Humanities at the University of Cologne, participants can obtain a certificate acknowledging a workload worth 2 ECTS credit points per one-week course. More information is available here.

For detailed course descriptions and registration, please visit our website and sign up here! If you are looking for recommendations on which courses to combine, we have put together a helpful guide for you here.

Working with geospatial 🌍 or digital trace data 💻? Managing complex data sets ⚙️? Building custom models 🤖? Whatever your current challenge in computational social science and advanced programming, our upcoming workshops are here to help you master the tools and techniques you need.

Learn how to Preprocess and Analyze Web Tracking Data or design effective Mobile Data Collection studies using smartphones. Dive into Advanced Geospatial Data Processing or Spatial Regression Analysis to understand how place influences people’s actions. If you are into custom modeling, deepen your knowledge with Advanced Bayesian Statistical Modeling in R and Stan or explore the power of Adapters to efficiently train large language models.

Want to take your results further? Learn to create beautiful and professional figures in our Applied Data Visualization with R workshop or build your own interactive dashboards with Shiny. And if your code needs a boost, sharpen your skills in Advanced R Programming or Data Management, Advanced Programming and Automation with Stata.

For additional details, registration, and our complete workshop program, visit our workshop website or have a look at the complete program below.

The GESIS Summer School 2025 takes place from 23 July to 15 August 2025 – most courses will be held onsite at GESIS Cologne, and some online via Zoom, or in a hybrid format, where onsite or online participation is possible. Join lecturers and participants from diverse fields and all over the world at Europe's leading summer school in survey methodology, research design, and data collection and analysis. There is no registration deadline, but places are limited and allocated on a first-come, first-served basis.

Here is an overview of this year's courses:

Week 1 (23–25 July) – Short Courses

Introduction to R for Data Analysis [23–24 July]

Dennis Abel (GESIS), Franziska Quoß (GESIS)    

Introduction to Stata for Data Management and Analysis [23–24 July]

Lynn-Malou Lutz (GESIS), Lisa Jäckel (GESIS)   

Developing, Translating, and Pretesting Questionnaires for Cross-cultural Surveys [23–25 July]

Dorothée Behr (GESIS), Patricia Hadler (GESIS), Lydia Repke (GESIS)   

Week 2 (28 July–01 August)

Advanced Questionnaire Design

Marek Fuchs (Darmstadt University of Technology)

Survey Sampling and Weighting

Simon Kühne (Bielefeld University)

Designing and Implementing Web Surveys

Melanie Revilla (University Pompeu Fabra)

Causal Inference with Directed Acyclic Graphs (DAGs) [29–31 July]

Paul Hünermund (Copenhagen Business School)

Week 3 (04–08 August)

Causal Inference in the Social Sciences

Matthias Collischon (IAB Nuremberg), Florian Zimmermann (IAB Nuremberg)

Data Science Techniques for Survey Researchers

Fiona Draxler (University of Mannheim), Anna Steinberg (LMU Munich), Malte Schierholz (LMU Munich)

Introduction to Conjoint Survey Experiments

Franziska Quoß (GESIS), Lukas Rudolph (University of Konstanz)

Week 4 (11–15 August)

Missing Data and Multiple Imputation

Florian Meinfelder (University of Bamberg), Doris Stingl (LIfBi)

Introduction to Survey Design

Bella Struminskaya (Utrecht University), Camilla Salvatore (Utrecht University)

Introduction to Small Area Estimation

Angelo Moretti (Utrecht University)

Scholarships, ECTS Credits, & More

4 scholarships are available to Summer School participants for one-week courses. Grants are disbursed by the European Survey Research Association (ESRA). Apply until 27 April 2025! Thanks to our cooperation with the Center for Doctoral Studies in Social and Behavioral Sciences at the University of Mannheim, participants can obtain a certificate acknowledging a workload worth 4 ECTS credit points per one-week course. More information is available here.

You will find the full program, detailed course descriptions, and more information here.

Lorien Jasny is an Associate Professor of Computational Social Science at the University of Exeter. Her work focuses on the role of social networks in questions of public engagement, environmental management, and community health. In her research, she explores two related themes: how the structure and dynamics of inter-organizational networks affect policy change, and how the structure and dynamics of social and belief networks affect behavioral change. Substantively, she studies how people try to bring about societal change in response to political and environmental concerns. Methodologically, the need to grapple with these often-complex phenomena requires the use and development of techniques for handling large, dynamic, and relational datasets. Lorien has taught workshops in social network methods for over 10 years, including for the International Consortium for Social and Political Research (ICPSR) and the International Network for Social Network Analysis (INSNA).

Lorien is teaching the Fall Seminar course Advanced Methods for Social Network Analysis, which takes place onsite from 15 to 19 September 2025.

How did you become interested in your subject?

Lorien: I was at university with the goal of studying mathematics but, I had an interest in history. I wound up in Karen Barkey’s Historical Sociology course and loved it. She recommended I study Sociology for my degree, but I explained I was there for Mathematics. She introduced me to Duncan Watts, and I took his graduate course on mathematical models for social scientists. With that, I was hooked! I loved using mathematical models to understand society. It brought to life Isaac Asimov’s Foundation novels, which I had read as a kid.

What lessons can participants draw from your GESIS course?

Lorien: The course is really designed for students who have their own network data or social network questions they want to answer, but lack the training in the methods to accomplish these tasks. The real focus for me is on connecting the methods to the questions they can answer. So, the course provides an overview of statistical methods for social networks with a deeper dive into the details of some key models (specifically permutation tests and exponential random graph models).

What do you think is the most exciting recent development in your field?

Lorien: I think the newer models for representing multilevel and multilayer networks are incredibly exciting. Many of our social networks are more complex with different types of relationships nested together. For a long time, our methods were unable to capture this type of complexity, but we are finally starting to, and I think this will open up whole new areas of theoretical and empirical work.

We thank Lorien for her insights and look forward to her course in September.