Course Recommendations

All courses in the GESIS Fall Seminar are stand-alone and can be booked separately – feel free to mix and match to build your own personal Fall Seminar experience! Below, you can find some of our recommendations for possible course sequences.

If you are starting from scratch and would like to go from zero to hero in Computational Social Science with either R or Python, we suggest taking one of these course sequences:

Week 0 (31 Aug-01 Sep): R 101 (online workshop)
Week 1 (05-09 Sep): Introduction to Computational Social Science with R
Week 2 (12-16 Sep): Automated Web Data Collection with R
Week 3 (19-23 Sep): Network Analysis in R

Week 0 (31 Aug-01 Sep): Python 101 (online workshop)
Week 1 (05-09 Sep): Introduction to Computational Social Science with Python
Week 2 (12-16 Sep): Automated Web Data Collection with Python
Week 3 (19-23 Sep): Introduction to Machine Learning for Text Analysis in Python

For participants looking to expand their toolbox and workflows for handling and analyzing digital behavioral data, we recommend the following courses:

Week 1 (05-09 Sep): Tools for Efficient Workflows, Smooth Collaboration and Optimized Research Outputs
Week 2 (12-16 Sep): Big Data Management and Analytics

If you are planning to collect and analyze your own digital behavioral data, have a look at these courses:

Week 2 (12-16 Sep): Automated Web Data Collection with R or Automated Web Data Collection with Python
Week 3 (19-23 Sep): Introduction to Machine Learning for Text Analysis in Python or Automated Image and Video Data Analysis

In case you are interested in working with large amounts of visual or textual data, you might want to combine:

Week 2 (12-16 Sep): Big Data Management and Analytics
Week 3 (19-23 Sep): Introduction to Machine Learning for Text Analysis in Python or Automated Image and Video Data Analysis

If you want to study social networks, you might find these two courses interesting:

Week 2 (12-16 Sep): Automated Web Data Collection with R
Week 3 (19-23 Sep): Network Analysis in R

If you need any advice on which course(s) might be the right one(s) for you, please do not hesitate to contact us. We’re happy to help!