Norwegian

Course description

• Course content
• Learning outcome
• Admission
• Prerequisites

• Teaching
• Examination

Course content

The course offers students to learn how to use academic knowledge, skills and competence in a relevant work situation. The host institution could be a relevant research institution, private company, or ministry/directorate. The course administrators will provide help in finding such a host institution. The course involves 100 hours of practice, normally one day a week. The practice work shall be supervised by a contact person in the host institution. The contact person should have a higher education in Data Science or a related field and a research- or innovation profile within the institution.

There will be a signed contract between the host institution, UiO and the student which describes the responsibilities on each part. The host institution has the responsibility to follow of the learnings goals listed in the Learning outcome paragraph, while the course administrators and the intern supervisor has the responsibility for quality control through regularly meetings (at least 4 times during the course period).

The course will be a trial experiment spring 2019.

Learning outcome

When completing the course, you will:

• Have acquired practical work experience within the Data Science field
• Have acquired experience in putting your theoretical knowledge within Data Science into practice
• Have increased awareness about your own competence, and be better prepared for the transition from student life to career
• Be able to apply your knowledge and skills to current challenges within the field of Data Science
• Have experience in presenting and communicating methods and results both to specialists and the general public

Admission

To be admitted to this course, you must be admitted to the Master?s programme in Data Science.

The internship is unpaid and the student can not have paid work in the company during the practice period.

Prerequisites

Formal prerequisite knowledge

Students have to have passed the following courses to be admitted to the course STK-IN4355:

• STK-IN4300 ¨C Statistical Learning Methods in Data Science
• IN-STK5000 ¨C Adaptive methods for data-based decision making

Teaching

Placement in a company, a total of 100 hours, normal 1 day a week.
In addition, there will be an introductory writing course (common to all master students) which is highly recommended.

Examination

The exam will consist of 3 presentations:

• First presentation and a short (max 1 page) description of the project, early in the semester
• Second presentation and a short (max 1 page) summary midway with status
• Final presentation and a short written report (max 10 pages)

 

All presentations and reports are considered pass / fail. All presentations and reports must be completed in one and the same semester.

 

Presence

• The practice is mandatory and the student must report absence due to illness and the like. Absence due to illness must be documented by doctors note
• Absence must not be larger than 15%

Completion of tasks

• The student must demonstrate the ability and willingness to do the tasks that he /she is required during the internship period.
• The student must also show respect for and follow the procedures and work routines within the host institution

Expertise

• The student must demonstrate academic insight and knowledge in his or her subject

Communication and cooperation

• The student must demonstrate the willingness and ability to communicate with the company's employees
• The student must show willingness and ability to work with the supervisor and other project staff.

Guidance and self-esteem

• The student must show the will and ability to receive counseling.

Language of examination

You may write your examination paper in Norwegian, Swedish, Danish or English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Explanations and appeals

• Explanation of grades and appeals

Resit an examination

Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester.

Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.