Some courses are held in-person in Mannheim, others online – and keep an eye out for our blended learning format! If you want to collect or analyze data from the web, social media, or digital archives, learn from leading experts in the field, and connect with other like-minded researchers, the Fall Seminar is the right place for you! All courses feature an interactive mix of lectures and hands-on exercises, giving you the opportunity to directly apply your newly acquired skills and knowledge to real-world data. Please find our full course program below or on the website. 

Program

Week 1 (31 August–04 September)

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

Claudia Wagner, Sebastian Stier, Aleksandra Pawlik, Arnim Bleier, Judith Gilsbach, Simon Kruschinski, Jula Lühring, Paul Balluff, Chung-hong Chan, Gabriella Lapesa & Ahrabhi Kathirgamalingam (all GESIS)

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

Claudia Wagner, Sebastian Stier, Aleksandra Pawlik, Arnim Bleier, Haiko Lietz, Paul C. Bauer, Vigneshwaran Shankaran, Julia Romberg, Maximilian Mauer & Gabriella Lapesa (all GESIS)   

Week 2 (07–11 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 (14–18 September)

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

Sjoerd Stolwijk (Utrecht University) & Rupert Kiddle (Vrije Universiteit Amsterdam)

Agent-Based Computational Modeling [Mannheim]

Daniel Mayerhoffer (University of Amsterdam)

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

Andreu Casas (Royal Holloway University of London)

Week 4/5 (21–25/29 September)

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

Hauke Licht (University of Innsbruck)

Mobile Data Collection and Analysis: Intensive Longitudinal Methods [Mannheim]

Lukas Otto (GESIS) & Julius Klingelhoefer (Friedrich-Alexander-Universität Erlangen–Nürnberg)

Causal Machine Learning [online]

Melissa Newham (ETH Zurich)

ECTS Credits & More

For those without any prior experience in R or Python and those who would like a refresher, GESIS additionally offer two online pre-courses, Introduction to R (24–27 August) and Introduction to Python (25–27 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. 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 GESIS website and sign up here! Feel free to contact GESIS if you have any other questions.