Social Data Analysis

Course content

This course introduces paradigmatic theories, concepts, and methods for the social scientific study of human behaviour social networks and cultural ideas. Through a combination of lectures, seminars and exercises, the courses shows how classic social science problems can be investigated a 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 a concrete research design.

Education

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:


Knowledge

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

 

Skills

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

 

Competencies

  • Formulate feasible and relevant social data science research questions.
  • Develop a state-of-the-art social data science research design, including research questions, the operationalizion of relevant theories and methods, and in keeping with best practices.

A combination of lectures introducing central theories and methods of behaviour, networks and ideas, with seminars, including student presentations and group discussions of syllabus, as well as experience-based learning (e.g. in situ or online experiments with students and other exercises).

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

Written
Oral
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
ECTS
7,5 ECTS
Type of assessment
Written assignment
Written assignment authored by groups of 3-4 students. The assignment should take the form of a fully-fledged research design for a social data science study with a feasible scope, including research questions, operationalization of theories and methods, and in keeping with best practices.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
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