ASDK20005U Social Data Analysis

Volume 2020/2021

Mandatory course on MSc programme in Social Data Science at University of Copenhagen. The course is only open for students enrolled in the MSc programme in Social Data Science.


This course introduces paradigmatic social scientific theories, models, and analyses of human behaviour (e.g. dual process framework, rational choice theory, theory of planned behaviour), social networks (e.g. tie formation, network structure and position, diffusion), and cultural ideas (e.g. discourse analysis, cultural epidemiology and structuralist analysis). Through a combination of lectures, seminars and exercises, it is discussed and demonstrated how classic social science problems and theories can be solved and advanced by using data science methods, and how the study of large-scale social data can benefit from social science thinking. As such, the purpose of the course is to provide an overview of central concepts and debates within social data science research, which can serve as a general analytical backdrop for the other more technically or topically specialized courses in the degree program.

Learning Outcome


  • Account for key social science theories of behaviour, networks and ideas.
  • Explain how social data science can be used to inform, test, and develop classic social science theories.



  • Assess the relevance of social data science to advance social science theories.
  • Evaluate pros and cons of different social data science approaches to analyse social science issues.



  • Pose and formulate a relevant social data science research question.
  • Develop a state of the art social data science research design.

Book chapters and scientific articles related to the course content. The students may be asked to purchase one or two books for general background.

Teaching combines lectures on historical backgrounds and state-of-the-art material, including primary readings, with seminars, including student presentations, group discussions and Q & A and experience-based learning (e.g. in situ or online experiments with students and other exercises). The seminars will primarily focus on secondary readings.
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 70
  • Exercises
  • 42
  • Project work
  • 46
  • Exam Preparation
  • 20
  • Total
  • 206
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Written assignment
Written assignment that students are expected to write in groups of 3-4 students. The written assignment takes the form of a research design for a potential social data science study with a feasible scope.
Exam registration requirements

To be eligible for the exam in Social Data Analysis, it is a requirement that students have passed all courses on semester 1 (i.e. Social Data Science Base Camp, Elementary Social Data Science and Data Governance: Law, Ethics and Politics).

Another requirement for eligibility is that students must have passed three individual sit-in or online tests, which access their knowledge and skills with respect to theories, models and analyses of human behaviour, social networks and cultural ideas respectively.

All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship

The second and third exam attempts run identical to the ordinary exam.

Criteria for exam assesment

The exam will be assessed on the basis of the learning outcome (knowledge, skills and competencies) for the course.