Course code DAT390

DAT390 Data Science Seminar

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Showing course contents for the educational year starting in 2020 .

Course responsible: Kristin Tøndel
ECTS credits: 10
Faculty: Faculty of Science and Technology
Teaching language: EN
(NO=norsk, EN=Engelsk)
Teaching exam periods:

The starts in the autumn parallel.

The course will be taught / censored in the autumn parallel.

Course frequency: Annually
First time: 2018H
Course contents:
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 form and orally according to the standards of the field, and will gain an overview over current developments in data science.
Learning activities:

Independent study of relevant material with mentoring as well as presentation for and discussion with your 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 a scientific discussions following the presentations.

Information about potential mentors is available on the for the Master in Data Science study program.

Teaching support:

Machine learning / deep learning is a subject that constantly evolves, and 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, referring to published material and re-use of material. The students get comments on a draft report and a nearly finished report.

The students can also request appointments with the lecturer in his/her office on pre-arranged times and via email.

To be defined individually at the beginning of the course.
DAT200, INF200, INF221, INF230, MATH280
Recommended prerequisites:
DAT300 should be taken simultaneously
Mandatory activity:
Participation in all seminar meetingsOral presentationWritten report
Evaluation based on oral presentation, written report and participation in seminar discussions. Pass/Fail.
Nominal workload:
Seminar Meetings 26 hours; mentoring 12 hours; 262 hours self study
Entrance requirements:
Type of course:
13 x 2 hours seminar meeting
This course is offered to students who are going to write a master thesis oriented towards data science during the following spring term.
An external censor will participate together with the internal censor in forming the evaluation guidelines. The external censor evaluates all written reports.
Examination details: Continuous exam: Passed / Failed