Academic internship (15 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.

 

Workload:


Working hours at the internship site: 327 hours
Internship report, including preliminary considerations: 50 hours

Total: 412 hours

 

Education

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 15 ECTS. The information in this course description is ONLY applicable to you if you are registered for 15 ECTS. If you are registered for 30 ECTS, please see the course description here: https:/​/​kurser.ku.dk/​course/​asdk20012u/​2023-2024

Learning outcome

Learning outcome 


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


Knowledge

  • Identify and refer to relevant theories and methods in a practical context.

 

Skills

  • Independently summarize and analyse a practical case in a well-structured written report.
  • Independently identify and select relevant theories and methods to examine a practical case.

 

Competencies

  • Critically reflect upon the acquired insights into and practical experience with the execution of work tasks relevant to social data science.
  • Discuss empirical implications with data collection at the internship site with reference to literature and experiences from the study program

 

 

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.

Oral
Individual
ECTS
15 ECTS
Type of assessment
Portfolio
Type of assessment details
Academic internship report.

Internship report submitted individually, maximum 5 standard pages.
The exam is graded as pass/fail. 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).

 

Aid

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
Re-exam

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
  • 327
  • Exam
  • 50
  • English
  • 377

Kursusinformation

Language
English
Course number
ASDK20011U
ECTS
15 ECTS
Programme level
Full Degree Master
Duration

1 semester

Placement
Autumn And Spring
Studyboard
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Friedolin Merhout   (8-6a7169766c73797844777367326f7932686f)
Saved on the 16-01-2024

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