GRA 4161 Internship for Data Science for Business

GRA 4161 Internship for Data Science for Business

Course code: 
GRA 4161
Department: 
Economics
Credits: 
6
Course coordinator: 
Leif Anders Thorsrud
Course name in Norwegian: 
Internship for Data Science for Business
Product category: 
Master
Portfolio: 
MSc in Data Science for Business
Semester: 
2023 Autumn
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

The internship is an opportunity to work full-time at a company during your studies and gain professional experience in the data science for business domain. As a participant in the internship programme, you have been selected to work at a specific company based on both what the company is seeking and your Master specialization. The internship will give you valuable insights in real-world challenges in the broad area of data science, and should help you in applying your academic learning toward increasing organizational effectiveness and growth.

Note that in order to get a BI Internship approved as part of the degree, the student needs to apply according to BI’s procedures and submit a valid Learning Contract prior to starting their internship and in due time before the deadline.   

Please be aware that the Internship period will be any time between May and August, while the examination will be in the autumn semester.

Learning outcomes - Knowledge

At the end of the internship, the student should be able to grasp the specific challenges faced by the company in creating value, relate academic knowledge and learn how to collaborate in offering solutions and creating new opportunities.

At the end of the internship the student is expected to have acquire an understanding of:

  • the practical issues and dilemmas faced by the company on a daily basis
  • how the application of data science is/can be used to create business value in the particular institutional setting
Learning outcomes - Skills

At the end of the internship the student is expected to have acquire skills related to: 

  • defining and executing tasks under conditions of uncertainty, change and time pressure
  • supporting suggestions for practical solutions with sound argumentation and academic analysis
  • applying theoretical and technical knowledge to specific tasks and problems, and collaborating and working in teams
General Competence

The student, at the end of the internship, should

  • reflect on the complexity of the work environment, the market forces that drive businesses and how companies create value in view of new opportunities, innovation and growth
  • learn how to combine practical and theoretical knowledge in relation to data science to solve problems, challenges, and improve efficiency
Course content

Students will work for 8 weeks in a selected company and the tasks are assigned by each company. Students must attend work as agreed upon with the company they are assigned to. After the completion of the internship, the student will produce a presentation summarizing their overall internship experience. Each student is entitled to a maximum of 1.5 hours supervision.

As part of this course, it is compulsory to participate in an employability course. 

The internship may be paid or unpaid.

Please be aware that the Internship period will be any time between May and August, while the examination will be in the autumn semester

Teaching and learning activities

The internship is for 8 weeks, full time and a completed and an approved internship will give 6 ECTS credits. The evaluation will be based on a final oral presentation, where the students summarize their overall internship experience.  

The principal aim of the presentation is to show the student's reflections on what was learned from the internship and the practices encountered, in relation to the knowledge the student acquired during the MSc studies. More specific guidelines on what the presentation should contain will be provided, but in general they should include:

  • general information about the company and the department that the student was assigned to
  • reflections on the overall professional experience
  • information about the internship position and the work assignments
  • reflections on the experience in relation to the academic knowledge acquired during the programme
  • theories, methods, knowledge or skills acquired in the programme that were useful in carrying out the internship tasks
  • evaluation of the student's strengths and weaknesses in doing the internship, as well as useful lessons for the student's future career.
Software tools
No specified computer-based tools are required.
Additional information

All parts of the assessment must be passed in order to get a grade in the course.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41611
Grading scale:
Pass/fail
Grading rules:
Internal examiner
Resit:
Examination next semester, thereafter when next scheduled course
50No 1 Week(s)Individual Students submit a company evaluation.
Exam category:
Activity
Form of assessment:
Oral examination
Exam code:
GRA 41612
Grading scale:
Pass/fail
Grading rules:
Internal examiner
Resit:
Examination next semester, thereafter when next scheduled course
50No 1 Hour(s)Individual There will be an individual oral examination, where all the students in the course will be present.
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:50
Invigilation:No
Grouping (size):Individual
Duration: 1 Week(s)
Comment:Students submit a company evaluation.
Exam code:GRA 41611
Grading scale:Pass/fail
Resit:Examination next semester, thereafter when next scheduled course
Exam category:Activity
Form of assessment:Oral examination
Weight:50
Invigilation:No
Grouping (size):Individual
Duration: 1 Hour(s)
Comment:There will be an individual oral examination, where all the students in the course will be present.
Exam code:GRA 41612
Grading scale:Pass/fail
Resit:Examination next semester, thereafter when next scheduled course
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
Feedback activities and counselling
1.5 Hour(s)
Examination
3 Hour(s)
Individual problem solving
155.5 Hour(s)
Sum workload: 
160

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.