Social Data Analysis

Course content

This course introduces theories, concepts, and methods for the social scientific study of behaviour, social networks and cultural ideas. Through a combination of lectures, seminars and exercises, the course shows how classic social science problems can be investigated by using data science methods, and how the study of large-scale digital social data can benefit from social science approaches. As such, the course provides students with knowledge about central methods and theories of social data science research, and with the capacity to operationalize these.


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.

Learning outcome

At the end of the course, students are able to:


  • Account for key social science theories of behaviour, networks and ideas.
  • Demonstrate understanding how computational methods can improve social science theories, and vice versa.



  • Assess the relevance of computational methods to investigate social data science problems.
  • Identity and operationalise relevant theoretical concepts and constructs.



  • Formulate feasible and relevant social data science research questions.
  • Apply best practices in operationalizing relevant social data science theories and methods pertaining to behaviour, networks, and ideas.

A combination of lectures introducing central theories and methods of behaviour, networks and ideas, with seminars, including student presentations and group discussions

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

Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Written examination
Written examination
Type of assessment details
Written assignment authored by groups of 3-4 students.
All aids allowed

ChatGPT and other large language model tools are permitted as a dedicated source, meaning text copied verbatim needs to be quoted, the tool cited, and generally the specific use made of them needs to be described in the submitted exam.

Marking scale
7-point grading scale
Censorship form
External censorship

An essay, written either in a group, or indvidually, on a course pertaining to the course content and prescribed literature. The subject must be pre-approved by the course lecturer(s).

Criteria for exam assessment

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

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 70
  • Exercises
  • 42
  • Project work
  • 46
  • Exam Preparation
  • 20
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 2
80 students.
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Friedolin Merhout   (8-6c736b786e757b7a4679756934717b346a71)
Saved on the 06-10-2023

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students