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.

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.
  • 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.
  • Submit a social data scientific assignment for the exam.

 

Workload: 

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

Total: 377 hours.

 

Extent of working hours 30 ECTS:

Working hours at the internship site: 650 hours. 

Internship report and social data assignment, including preliminary considerations: 175 hours. 

Total: 825 hours. 

 

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.

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.

Moreover, students enrolled for 30 ECTS credits must on two occasions submit considerations regarding their social data scientific assignment to the internal supervisor.

Oral
Individual
ECTS
15 ECTS
Type of assessment
Portfolio
Exam form – 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 form – Social data scientific assignment (Only students signed up for academic internship 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.
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).

 

Assessment 15 ECTS credits

The exam is graded on a pass/fail basis.

The exam is graded by an internal examiner.

 

Assessment 30 ECTS credits

The exam is graded in accordance with the Danish 7-point grading scale.

The exam is graded by an internal examiner.

  • 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
Studyboard
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Friedolin Merhout   (8-686f67746a71777642757165306d7730666d)
Saved on the 02-02-2022

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