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 approaches, 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 methodologies and theories of social data science research, and with the capacity to operationalize these.

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 of how social data science approaches can improve social science theories and vice versa.

 

Skills:

  • Assess the relevance of computational social data science approaches to investigate social data science problems.
  • Identify and operationalize relevant theoretical concepts and constructs.

 

Competences:

  • Ability to perform the conceptual work necessary for social data science projects.
  • Apply best practices in operationalizing relevant social data science theories 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.

Written
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
ECTS
7,5 ECTS
Type of assessment
Home assignment
Type of assessment details
Students are placed in assigned groups of 3 to 4 at the beginning of the course. Students have to submit 4 group assignments as prerequisites to register for the exam (3 out of 4 must be approved). These have to be submitted in the assigned groups. For the final exam, students can choose to write alone, or in groups of their own choosing.

Lenght of home assignment: 15 pages
Examination prerequisites

3 out of the 4 assignments must be approved for the student to participate in the exam.

Aid
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
Exam period

Exam information:

The examination date can be found in the exam schedule    here

The exact time and place will be available in Digital Exam from the middle of the semester. 

Re-exam

The second and third examination attempts are conducted in the same manner as the ordinary exam.

Reexam info:

The reexamination date/period can be found in the reexam schedule    here

Criteria for exam assessment

Students are assessed on the extent to which they master the learning outcome for the course.

 

To obtain the top grade “12”, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.

 

To obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of the knowledge, skills and competencies listed in the learning outcomes.

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

Kursusinformation

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

1 block

Placement
Autumn And Block 2
Studyboard
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Hjalmar Alexander Bang Carlsen   (2-6a6542757165306d7730666d)
Saved on the 01-05-2025

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