Data Collection, Processing and Analysis (30 ECTS)

Course content

The purpose of this course is to provide students with an opportunity for collecting and working with data that is relevant in relation to the Master’s thesis. The course consists in participating in a data collection project such as running an experiment or scraping data from the internet. This includes preliminary processing and basic analysis.

Students are only allowed to pass this course once in the course of the Master’s degree programme.


Elective course offered by 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.


NOTE: This is the course description for Data Collection 30 ECTS. The information in this course description is ONLY applicable to you if you are registered for 30 ECTS. If you are registered for 15 ECTS, please see the course description here: https:/​/​​course/​asdk20009u/​2023-2024

Learning outcome

Learning outcome:

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


  • Describe the use of different methods for doing research within social data science and the knowledge they produce.
  • Define of theoretical terms and research themes that can be used to understand relevant social data science problems within an empirical material.



  • Design large scale data collection process taking a point of departure in an independent problem formulation.
  • Independently and critically collect relevant empirical material.
  • Adjust the problem statement and research question and academically account for the adjustments.
  • Systematically organize and structure the empirical material in accordance with research ethics.
  • Document the collected data and account for how it has been structured.



  • Assess problem statement and research questions in relation to the empirical material from different perspectives.
  • Discuss ethical implications in regard to the data collection.
  • Contemplate and assess the potential for applying the data for commercial and/or political purposes.
  • Reflect critically on the methodological and analytical process of collecting data and applying it for and research purposes.

This course is conducted primarily as an independent study. At the beginning of the semester, the Head of Studies assigns students into supervision clusters.

In the course of the semester students must participate in workshops, organised by the cluster supervisor, focusing on presenting their social data science material and analysis. The output from the workshops are portfolio items.

Continuous feedback during the course of the semester
Type of assessment
Type of assessment details
Written exam (portfolio) submitted individually or in groups. Students in the same group must be enrolled for the same number of ECTS. The portfolio exam must contain all the portfolio assignments handed in during the course and an overview of the collected data material.
The written portfolio assignment must be no longer than 20 pages when written by 1 student and 30 pages when written by two students, who write together.

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
No 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
  • Guidance
  • 12
  • Exam Preparation
  • 812
  • English
  • 824


Course number
Programme level
Full Degree Master

1 semester

Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Kristoffer Langkjær Albris   (17-7279707a7b766d6d6c793568736979707a477a766b687a35727c356b72)
    Department of Anthropology
Saved on the 21-09-2023

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