Academic internship (30 ECTS)

Course content

The purpose of the academic internship is to provide students with an opportunity to get hands-on-experience for research and/or commercial or social purpose. Through a formalized attachment to a company, public institution, research institute or similar the student will perform tasks and at the same time be able to apply academic skills in a practical context.

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



Extent of working hours 30 ECTS:
Working hours at the internship site: 650 hours
Social data scientific assignment, including preliminary considerations: 175 hours

Total: 825 hours


The course is only open for students enrolled in the MSc programme in Social Data Science.


NOTE: This is the course description for Academic Internship 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/​asdk20011u/​2023-2024

Learning outcome

Learning outcome

At the end of the academic internship, students are able to:


  • Critically and independently reflect upon and discuss the applied social data science theories and methods to a chosen topic.
  • Account for the validity, scope and usefulness of relevant data as part of the social data scientific assignment.



  • Apply and discuss for relevant theories and methods in a practical context.
  • Independently summarize and analyse a topic in a well-structured written assignment.
  • Carry out and implement social data science-based analysis in a practical context



  • Independently identify and select relevant theories to examine a practical case
  • Gauge and evaluate the relevance of methods for collecting and analysing data for practical cases.
  • Formulate a comprehensive research design to investigate the chosen case
  • Independently analyse and apply academic literature relevant to a specific problem statement

This course is conducted primarily as an independent study.

Internal supervisor
Students enter into supervision agreement with one of the full-time teachers who are involved in the Master’s degree programme in Social Data Science or an affiliated part-time lecturer, a PhD student or a postdoc. The supervisor is responsible for approving and monitoring the academic internship, and for ensuring that the learning outcome is achieved.

External supervisor
Students must be assigned an external supervisor employed at the place of the academic internship. The external supervisor continuously develops and evaluates the academic internship together with the student in accordance with the expected learning outcome.

In the course of the academic internship, the student will:
On one occasion, submit preliminary considerations regarding their academic internship report and receive feedback from the supervisor, as well as submit an academic internship report for the exam.

Students will also:
On two occasions, submit preliminary considerations regarding their social data scientific assignment and receive feedback from the supervisor, as well as submit a social data scientific assignment for the exam.

Type of assessment
Type of assessment details
Academic internship report.

Social data scientific assignment
Social data scientific assignment submitted individually, maximum 20 standard pages.
The exam is graded according to the Danish 7-point grading scale. The exam is graded by the internal supervisor.
Exam registration requirements

All students must on one occasion submit considerations regarding their academic internship report to the internal supervisor, and document that the number of working hours has been completed (e.g. academic internship contract).

Moreover, students must on two occasions submit considerations regarding their social data scientific assignment to the internal supervisor.


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.

Censorship form
No external censorship

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

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
  • Practical Training
  • 650
  • Exam
  • 175
  • English
  • 825


Course number
Programme level
Full Degree Master

1 semester

Autumn And Spring
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Friedolin Merhout   (8-7a8179867c83898854878377427f8942787f)
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