Datafication and digitalisation have revolutionised what we can observe: scholars are increasingly social media data, digital traces, various databases – both quantitative and qualitative. At the same time, the data sets we work on have become increasingly larger, meaning that our traditional data analysis approaches are not sufficient. Furthermore, methodological development is being conducted to create innovative approaches, promising better ability to make predictions from the data, and hopefully also help us to find explanations why society and humans do what they do.
I’m naturally speaking about big data, machine learning, and about digital humanities and computational social sciences overall. There is a need for an extensive competence development for us all:
- Do you plan to use large scale datasets (“big data”) in your research project and need some competence development before you are ready to engage big data?
- Have you been dreaming to learn more about machine learning and data science practices to help you rethink how research is conducted?
- Do you think your next career step is to move into data sciences?
We have you covered!
Our one-week intensive online course Data science takes place from August 30 to September 3rd. The course is now open for registration, both for students and faculty:
- PhD and Master students: Register via SISU (any of the course codes: SOST-930, SOST-931 or SOST-932, course name Research methodologies and methods III: Data science).
- Postdocs, university lectures, professors, and other faculty: Register via Success Factors, course name Computational Social Science: Data science crash course).
For full credit, we expect you to have basic level knowledge of R and/or Python (i.e., knowing how to run code). Faculty members may also audit the course by skipping the laboratory exercises and only focus on new opportunities provided by data science methods into their research by reading papers and doing non-programming homework assignments.
The course is organised by the Digital and Computational Methods Lab at the Helsinki Social Computing Group, Centre for Social Data Science, Faculty of Social Science.