From 11 to 29 September 2023, the GESIS Fall Seminar in Computational Social Science took place in Mannheim. Participants could choose from a variety of introductory and advanced courses on computational social science methods and techniques.
In “Introduction Computational Social Science with R” taught by Aleksandra Urman (University of Zurich) and Max Pellert (University of Mannheim), participants learned about the foundations of working with digital behavioral data and gained an overview of different methods, techniques, and tools for data collection and analysis. Those who wanted to dig deeper into the collection of digital behavioral data could delve into web scraping methods and how to work with APIs in courses on “Automated Web Data Collection.” Allison Koh (Hertie School of Governance Berlin) and Hauke Licht (University of Cologne) taught the course with R, while our GESIS colleagues Felix Soldner, Jun Sun, and Leon Fröhling facilitated the Python equivalent. Focusing on the analysis of textual data, Damian Trilling and Anne Kroon (both University of Amsterdam) gave an “Introduction to Machine Learning for Text Analysis in Python.” In “Social Network Analysis with R,” Michał Bojanowski (Kozminski University and Universidad Autònoma de Barcelona) showed participants how to manage, visualize, and analyze social network data. More advanced participants had the chance to learn about word embeddings and state-of-the-art large language models like BERT from Hauke Licht (University of Cologne) and Jennifer Scurrell (ETH Zurich) in “From Embeddings to Transformers: Advanced Text Analysis with Python,” while Andreu Casas (Vrije Universiteit Amsterdam) and Felicia Loecherbach (New York University) shared their expertise on how to use computer vision and machine learning for “Automated Image and Video Data Analysis.”
Organized as an in-person event, participants much enjoyed the social aspects of the Fall Seminar with its ample networking opportunities as well as the chance to explore the city of Mannheim.
Not only the social environment but also all courses were rated very highly by participants: More than 9 out of 10 participants stated they were (very) satisfied with their course, and a whopping 96% said they would recommend GESIS Training events. Participants particularly emphasized the vast knowledge of the lecturers, their readiness to give individual feedback and support, and the hands-on nature of the courses. They also greatly appreciated the “friendly and productive” atmosphere and that the courses provided “a solid starting point on how to approach [CSS] issues” with a “good mix of theories and practice.” To quote one participant in “Introduction to Machine Learning for Text Analysis in Python”: “The trainers are amazingly experienced in the field and they held the course in a very interactive-didactive way. It was a privilege having the opportunity to take a training with them.” A big THANK YOU to our fantastic lecturers for their commitment and to our participants for placing their trust in us!
Save the date: The next GESIS Fall Seminar in Computational Social Science will take place from 9 to 27 September 2024 in Mannheim. We will announce the full program in Spring 2024.
