DAT390 Data Science Seminar
Showing course contents for the educational year 2019 - 2020 .
Course responsible: Kristin Tøndel
ECTS credits: 10
Faculty: Faculty of Science and Technology
Teaching language: EN
Teaching exam periods:
The starts in the autumn parallel.
The course will be taught / censored in the autumn parallel.
Course frequency: Annually
First time: Study year 2018-2019
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.
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.
Independent study of relevant material with mentoring as well as presentation for and discussion with your fellow students.
Information about potential mentors is available on the https://www.nmbu.no/en/studies/study-options/master/master-of-science-in-data-science/programme-structurewebsite for the Master in Data Science study program.
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 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
DAT300 should be taken simultaneously
Participation in all seminar meetingsOral presentationWritten report
Evaluation based on oral presentation, written report and participation in seminar discussions. Pass/Fail.
Seminar meetings 16 hours; mentoring 12 hours; 272 hours self study
Type of course:
8 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 checks the internal censor's assessment of a random selection of candidates as a calibration at certain intervals in line with the faculty's guidelines for censoring.
Examination details: Continuous exam: Passed / Failed