About this course

The students acquire knowledge about current topics in data science through individual work, including the study of scientific publications, recent monographies, analysis projects or other suitable methods. They systematise and share the knowledge in oral and written form.

Learning outcome

You acquire in-depth knowledge about a specific topic in data science, you learn to present knowledge in written and oral form according to the standards of the field, and will gain an overview of current developments in data science.

  • Learning activities

    Independent study of relevant material with mentoring as well as presentation for (and discussion with) fellow students. The students choose a topic the latest by the start of the course, preferably relevant for their master thesis. The students prepare a presentation and a written report on the topic, and present the presentation to their fellow students. The students attend the presentations and scientific discussions following the presentations.

    Recommendation: Do discuss the choice of topic and its relevance with your prospective Master thesis advisor beforehand.

  • Teaching support

    Online learning resources will be connected to lectures and exercises through the course webpages in Canvas. The writing center can be utilised as a support during the writing process. The students get an introduction to making presentations and written material, how to refer to published material and re-use of material. The students get comments on a draft report and a nearly finished report.

  • Syllabus

    To be defined individually at the beginning of the course.

  • Recommended prerequisites

    DAT200, INF200, INF221, INF230, MATH280

    DAT300 can be taken simultaneously

  • Assessment method

    The grade in the course is set on the basis of a term paper. Letter grades (A-F).

  • About use of AI

    K2. Specified use of AI. It is allowed to use AI for all purposes except for generating any material submitted for grading.

    Descriptions of AI-category codes.

  • Examiner scheme

    External sensor to be used only if (and to the extent) mandated by law pursuant to uhl. § 3-9.

  • Mandatory activity

    • Contribution to academic discussion (at seminar meetings)
    • Preliminary Report (first and second draft)
    • Oral presentation

    Presentation and contribution to academic discussion must be passed.

  • Notes

    This course is offered to students who are going to write a master thesis oriented towards data science during the following spring term.

  • Teaching hours

    Seminar sessions 12 x 2 hours

    Writing lab/support and good practice 12 x 2 hours

    Meeting/discussion with supervisors 2 hours

  • Admission requirements

    REALFAG