Summary

The GESIS Fall Seminar in Computational Social Science features introductory and advanced courses on collecting and analyzing digital behavioral data from the web, social media, or digital archives. Courses are taught by leading experts in the field and offer plenty of opportunities to learn from and connect with other like-minded researchers. Lectures in each course are complemented by hands-on exercises, giving you the opportunity to directly implement your newly acquired skills and knowledge.

Description

If you are new to computational social science, you may want to get an overview of debates and methods in the field in one of our blended learning introductions to computational social science with R or Python. If you plan to collect your own textual or visual data from the web, have a look at web data collection with R or Python, or learn how to collect data with smartphones and analyze the resulting data with intensive longitudinal methods. For those interested in data analysis, we offer courses on machine learning for text analysis for beginners, cutting-edge text analysis methods, computer vision, causal machine learning, and agent-based modeling.

We are happy to assist you!

Should you be unsure which course is the right one for you, please do not hesitate to contact us.

For those without prior experience in R or Python and those who would like a refresher, we additionally offer two introductory online pre-courses for R and Python in the week before the start of the Fall Seminar – join those for optimal preparation! 

All courses in the GESIS Fall Seminar are stand-alone and can be booked separately – feel free to mix and match courses to build your own personal Fall Seminar experience! Contact us if we can assist with tailored recommendations for possible course combinations or if you have any other questions. Learn more about our course fees and ECTS credits on our website.

Course overview

Week 1 (31 August-04 September)

Introduction to Computational Social Science with R (blended learning online course)

Introduction to Computational Social Science with Python (blended learning online course)

Week 2 (07-11 September)

Web Data Collection with R (live online course)

Web Data Collection with Python (live online course)

Week 3 (14-18 September)

Introduction to Machine Learning for Text Analysis with Python (in-person course)

Agent-Based Modeling & Simulation (in-person course)

Computer Vision for Image and Video Data Analysis with Python (live online course)

Week 4/5 (21-29 September)

From Embeddings to LLMs: Advanced Text Analysis with Python (in-person course)

Mobile Data Collection and Analysis: Intensive Longitudinal Methods (in-person course)

Causal Machine Learning (live online course)

Reasons to attend

As a graduate or PhD student, postdoc, junior or senior researcher, or computational social science professional, you will:

• learn about state-of-the-art techniques and methods of computational social science and obtain a comprehensive overview of different computational methods
• receive high-quality training from leading experts in the field of computational social science
• apply what you learn immediately through our hands-on focus
• acquire essential skills in designing and implementing your computational social science projects
• expand your academic and professional network by connecting with peers from diverse disciplines and around the globe to discuss research and share ideas