Elementary Social Data Science

Course content

This course provides students with a general introduction to the research process in social data science. The course introduces central concepts and research methods in relation to the planning and conduction of research in the field of social data science. The course is structured in three constitutive blocks.

The first block provides an introduction to the different forms of data (e.g., data from quantitative and qualitative research, “big data”) and prominent research designs.

The second block introduces different data collection methods (e.g., found data, surveys, and experiments).


The third block introduces principles and methods on how to establish high data quality (e.g., high validity and reliability) as well as how to treat data from various sources (e.g., transforming and structuring data).

In all, the course introduces the students to basic techniques, methods and principles of social data science research to prepare them for and complement the advanced computational techniques, statistical methods and social science theories taught in subsequent courses.


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:


  • Explain the principles of empirical social science informing both quantitative and qualitative research.
  • Account for a broad variety of data collection methods used in the social sciences, as well as their strengths and weaknesses.
  • Account for basic methods how to process and treat data for further analyses.
  • Explain common criteria for high-quality, replicable social science research.



  • Develop social science research designs.
  • Collect primary data to answer research questions using survey and experimental methods.
  • Collect secondary data to answer research questions from online sources using web scraping, online archives, and APIs.
  • Evaluate data quality and prepare data for further statistical analyses.



  • Evaluate and critically reflect on published social science research by applying the highest international standards.
  • Identify opportunities to use digital data sources.
  • Plan and conduct high-quality social data science research projects, encompassing the research design, data collection, and data preparation stages.

Lectures, seminars, group-work and exercises.

Book chapters and scientific articles related to the course content. Students have to prepare lectures/exercises by reading about 50-100 pages per week. Readings will be provided by the teachers.

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Type of assessment details
Portfolio exam written in groups. The portfolio consists of revisions to the earlier assignments that have been handed in and must be submitted by the end of the course. The final grade results from the combined assessment of the three assignments.
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
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
  • 66
  • Exam
  • 0,5
  • English
  • 206,5


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 1
80 students.
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Lau Lilleholt Harpviken   (3-6f6f6d4373767c316e7831676e)
Saved on the 21-09-2023

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