This is an hands on-course. The course focuses on building programming skills. It does so by asking students to work on 70+ programming exercises and readings. The workload of this course can be heavy.
I highly recommend that students will also join the more research design oriented course introduction to computational social science provided at Period I.
See course schedule, location and other administrative details from University of Helsinki course page.
By the end of the course, you can write small Python code "snippets" to collect and process data. For this purposes, we have three different goals for the course:
We have about three hours on Friday mornings. The first hour (8.15-9.00) is reserved for students to work on the exercises and receive help and instructions from Matti. The next hour, 9.00-10.00 is time for all students, engaging into discussion about the articles and reviewing common challenges. This in most cases will take less than one hour, but kindly reserve a hour timeslot for this. Following this, 10.00-10.45 is again reserved for tutoring and working on the problem exercises together.
We use flipped classroom approach in this course. That means we expect students to familiarize with the course material, that is the assigned papers and materials regarding Python, on their own before lectures. During the lectures, we discuss and explore the assigned papers together and provide time to work on the exercises.
Programming exercises train computational thinking and Python programming in practice. To struture the course, each exercise week has a deadline.
Case studies present examples of recent research applying computational social science.
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
However, the web is full of Python 2.7 materials. Different people like different types of materials so if you find both of these bad for your style of finding the information, I recommend you go Googling to look for something more suitable for your taste :) (Also, just Googling the problem might help you.)
As such, the course has no mandatory prerequisites. However, if you have not taken the Introduction to Computational Social Science, I strongly encourage you to read extra materials. (Extra materials to be added.)
For each case study, prepare to discuss the research question they had and the computational method they applied. For each article,
identify tools and frameworks they use in that research.
Focus on the methods, not the results. This is a methods class and the articles may be somewhat boring results wise.
To be updated.
We read case studies per lecture