Build your data science skills: enrol to the computational social science courses

Are you planning a research project exploring large-scale online datasets? Do you think qualitative research might need to be supplemented with computational text analysis? Do you need new research skills for the era of big data?

Centre for Social Data Science organises three courses focused on computational social science, which are open to the faculty and staff as well.

During fall

CSS01: Introduction to Computational Social Science (5 credits)

The course provides an overview of an emerging scholarly focus, 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 for computational social sciences. By the end of the course,

  • students are able to develop research designs which benefit from computational social science methods
  • students are able to critically evaluate research plans or published papers which use computational social science methods in terms of feasibility, validity, reliability, ethics and contribution.

CSS02: Introduction in programming for social science

Students learn to write small programs which help them to collect, process and analyse data through computation. There are three sub-goals for this course

  • developing skills in computational thinking here understood as the process where the real-world problem is presented as coding problems
  • introducing students to a programming language and developing elementary skills for them to help implement computational thinking to some of the real-world problems
  • discuss and deepen the applications of computational social science in practical research, that is existing literature and different applications they have in social science and developing sociological imagination

During spring

CSS03: Data science and machine learning for social science

Students learn how to use machine learning methods for qualitative and quantitative datasets. Previous experience in R or Python (e.g. CSS01 and CSS02) is required.)