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
Peer feedback (Students give each other feedback)
ECTS
7,5 ECTS
Type of assessment
Portfolio
Written examination
Type of assessment details
Portfolios in combination with written assignment, in groups. Portfolios in combination with written assignment. Three portfolio assignments for Behaviour, Networks, and Ideas, respectively, will be produced during the course, which are handed in as the final exam along with max. 3 pages of a more general reflection
and discussion. Each portfolio may include, among other elements, a detailed discussion and
comparison of the mandatory literature.
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

Kursusinformation

Language
English
Course number
ASDK20005U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 2
Capacity
70 students.
Studyboard
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Morten Axel Pedersen   (3-72667545787469667833707a336970)
Saved on the 06-07-2022

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Courseinformation of students