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 purposes. 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 register for the course once in the course of the Master’s degree programme.
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.
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
Competences:
- 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.
When registered you will be signed up for exam.
- Full-degree students – sign up at Selfservice on KUnet
The dates for the exams are found here Exams – Faculty of Social Sciences - University of Copenhagen (ku.dk)
Please note that it is your own responsibility to check for overlapping exam dates.
- ECTS
- 30 ECTS
- Type of assessment
-
Home assignment
- Type of assessment details
- Individual or in groups of two. If two internees are working on
the same topic, at the same company, and with the same supervisor
they can write their exam together.
Lenght of the home assignment: 20 pages for one student.
If two internees write the assignment together the assignment must be at maximum 25 pages. - Examination prerequisites
-
All students must on two occasion submit preliminary considerations regarding their social data scientific assignment and recieve feedback from the internal supervisor.
- Aid
- All aids allowed
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
- Re-exam
-
The second and third examination attempts are conducted in the same manner as the ordinary examination.
Criteria for exam assessment
Students are assessed on the extent to which they master the learning outcome for the course.
To obtain the top grade “12”, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.
To obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of the knowledge, skills and competencies listed in the learning outcomes.
- Category
- Hours
- Practical Training
- 650
- Exam
- 175
- English
- 825
Kursusinformation
- Language
- English
- Course number
- ASDK20012U
- ECTS
- 30 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
- Kristoffer Langkjær Albris (17-7279707a7b766d6d6c793568736979707a477a766b687a35727c356b72)
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