Student Pages
Postgraduate course

Master's Thesis

  • ECTS credits30
  • Teaching semesterSpring, Autumn
  • Course codeDSC399K
  • Number of semesters1
  • LanguageEnglish
  • Resources

Main content

ECTS Credits


Teaching semester

Spring and autumn

Objectives and Content


The candidates who have completed their study shall have substantial founded knowledge and skills in data science. They shall have obtained a thorough introduction to scientific work methods and training in self-contained work doing extensive and demanding technical tasks. They will have developed special skills within a research field, and a good overview of other research fields.


The Programme Board and supervisors are responsible for a sufficient and well-defined pool of master projects. Offered master projects are normally results from dialogue between supervisor and student, and then approved by the Programme Board with regards to the description and scope of the project (i.e., the project should match the allocated time available). Projects can also be pre-approved by the Programme Board.

Learning Outcomes

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:


The student

  • can demonstrate a solid understanding of data science and specialized knowledge with respect to his/her master project


The student

  • is able to plan and conduct independent work under supervision and in accordance with ethical guidelines for research
  • is able to communicate both orally and in writing results obtained from both own research and other research groups
  • is able to critically evaluate experimental results (from own research and from other research groups)
  • is able to collect, analyze and implement new knowledge within data science

General competence

The student

  • demonstrates critical assessment of scientific literature and scientific thinking
  • can reflect on ethical issues raised by research in data science

Required Previous Knowledge


Recommended Previous Knowledge


Credit Reduction due to Course Overlap


Access to the Course

Access to the course requires admission to Data science, Integrated Master¿s (5 years) at The Faculty of Mathematics and Natural Sciences.

Teaching and learning methods

Individual research, supervised by an advisor.

Forms of Assessment

The Master¿s programme is finalized by an oral Master¿s degree examination after the Master¿s thesis has been submitted and approved. This exam consists of a public presentation of around 30 minutes, where the student gives an overview of the thesis. Examiner, supervisor(s) and internal examiner(s) should be present at the public presentation. An oral examination/conversation about the thesis should follow the presentation.

The thesis should have a tentative grade before the presentation. The tentative grade should not be known by the student. The presentation, along with the oral examination/ conversation, may adjust the grading of the thesis. The final grade is that which is made known to the candidate and which shows on their grade transcript.

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Assessment Semester


Programme Committee

The Programme Committee is responsible for the content, structure and quality of the study programme and courses.

Course Administrator

The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme


Course coordinator and administrative contact person can be found on Mitt UiB, or contact Student adviser