ASDK20012U Academic internship (30 ECTS)

Volume 2021/2022
Education

It is possible to replace subject elements corresponding to 15 or 30 ECTS credits on the master’s degree programme in Social Data Science with an academic internship.

 

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

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:

The academic internship amounts to 15 or 30 ECTS.


Extent of working hours 15 ECTS:
Working hours at the internship site: 327 hours
Internship report, including preliminary considerations: 50 hours

Total: 412 hours

 

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

Total: 825 hours

Learning Outcome

Learning outcome (15 ECTS)


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

 

Learning outcome (30 ECTS)


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


Knowledge

  • 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.

 

Skills

  • 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

 

Competencies

  • 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 signed up for academic internships worth 30 ECTS credits 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.
  • Category
  • Hours
  • Practical Training
  • 650
  • Exam
  • 175
  • Total
  • 825
Oral
Individual
Credit
30 ECTS
Type of assessment
Portfolio
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.

Social data scientific assignment (Only students signed up for academic internships worth 30 ECTS credits)
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 enrolled for 30 ECTS must on two occasions submit considerations regarding their social data scientific assignment to the internal supervisor.

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 assesment

The exam will be assessed on the basis of the learning outcome (knowledge, skills and competencies) for the course.