By the end of the course, students can write small programs (less than hundred lines) which help them to collect, process and analyze data through computational means. There are three sub goals for this course
This is an hands on-course. The course focuses on building programming skills. Students are expected to work on 70+ programming exercises and readings. The workload of this course can be heavy.
This year, the course is organised compleatly online.
For admnistrative details and enrollment, see
I highly recommend that students will also join the more research design oriented course introduction to computational social science.
We use flipped classroom approach in this course. That means we expect students to familiarize with the course material on their own before lectures. These pre-assignments follow The assigned papers and materials regarding Python, During the lectures, we discuss and explore the assigned papers together and provide time to work on the exercises. Our empahsis in the class is to work on assigned programming exercises. Matti will be there to help you, but for most parts this will be independed work.
The course provides hands-on skills for applied computational social science approaches. Course examines
Beyond these, students familiarize themselves to published articles which have utilized these methods and examine their use further.
The course is graded on pass/fail -scale. This aims to motivate everyone to take the course, as it will not impact your grade point average.
Passing grade on this course requires
Read Chapter 2.
Read deconstructed cases from Chapter 3, including the original papers.
Read deconstructed cases from Chapter 4, including the original papers.
Read Chapter 5.
Read deconstructed cases from Chapter 6, including the original papers.
Read deconstructed cases from Chapter 7, including the original papers.
Read Chapter 8.