The course provides an overview to the emerging field of computational social science. Through exploring computational social science methods and their use in social sciences today, this course helps students to engage with questions on research design. We focus on four methodological approaches: machine learning, network analysis, simulations and interactive systems research methods. Beyond this, we discuss ethics, validity and integration of computational social science students.
The goal is that
This year, the course is organised compleatly online.
For admnistrative details and enrollment, see
We recommend that students also participate in Introduction to programming. The courses are designed to support each other.
Pre-readings help students to orient to class content and allows us to have more indepthful discussions. We will use the book "Coding Social Science. Understanding and Doing Social Science."
Lectures are not mandatory but I make no guarantees that the slides make sense without being present in the lecture (i.e.; I'll aim to make presentation slides) nor can the student join the intellectual discussions around the theme. Thus, I encourage studetns to join the lectures.
Each student writes learning diary
When writing a learning diary, avoid repeating what was said and rather weight your own opinions. We will use an online platform for these discussions and students are expected to comment the after-lecture notes by providing their insights and comments to the posts.
Plase limit the length of learning diary to 250 words just to give an idea of the size of contribution (which is small) in these learning diaries. To foster the interaction, it is mandatory to respond to at least one after-lecture notes (and I will also engage the discussion). If you miss a class, kindly write at least three comments to the posts. (No need to write an after-lecture summary, naturally.)
Also students are expected to return a final assignment see below.
20% of the grade is based on the lecture diary.
80% of the grade is based on final assignment.
For PhD students, the focus is on quality and innovativeness of the research proposal. I will use the Academy of Finland review form for the evaluation.
For Masters' student, the focus is on demosntration that you understand what takes place in the paper and can reflect those further. We will co-develop an evaluation matrix during the first draft stages of the paper.
Read Chapter 1 before the class.
Read Chapter 3 before the class.
Read Chapter 7 before the class.
Read Chapter 4 before the class.
Read Chapter 6 before the class.
Read Chapters 9 and 10 before the class.
Read Chapter 11 before the class.
Summarise three papers which use computational social science methods. Choose papers so that they use three of the four different method families discussed in the book. Find the papers yourself, do not use articles presented or discussed as cases from the book. In each summary
You will recieve instructor and peer-feedback of drafts and revisions to help you improve the summaries.
An important skill of a PhD is to apply theorethical conncepts and methods into research problems they find relevant and interesting. To train these skills, we will write a research proposal where they apply the methods or concepts presented in this course to a research question or problem they find interesting.
To help in structuring the research proposal, the Postdoctoral Researcher applications format from Academy of Finland. (It is fairly good summary how a research proposal might look like.) Follow the Academy of Finland guidelines on content, length etc. Naturally, you won't be using the Academy of Finland online template, but submit them in a single document.
You will recieve instructor and peer-feedback of drafts and revisions to help you improve the summaries.