DAT390 Data Science Seminar
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Showing course contents for the educational year 2018 - 2019 .
Course responsible: Hans Ekkehard Plesser
ECTS credits: 10
Faculty: Faculty of Science and Technology
Teaching language: EN
(NO=norsk, EN=Engelsk)
(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: Study year 2018-2019
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.
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.
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 students can also request appointments with the lecturer in his/her office on pre-arranged times and via email.
Syllabus:
To be defined individually at the beginning of the course.
Prerequisites:
DAT200, INF200, INF221, INF230, MATH280
Recommended prerequisites:
DAT300 should be taken simultaneously
Mandatory activity:
Participation in all seminar meetingsOral presentationWritten report
Assessment:
Evaluation based on oral presentation, written report and participation in seminar discussions. Pass/Fail.
Nominal workload:
Seminar meetings 16 hours; mentoring 12 hours; 272 hours self study
Entrance requirements:
GSK
Type of course:
8 x 2 hours seminar meeting
Note:
This course is offered only to students in data science who are going to write their master thesis during the following spring term.
Examiner:
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: Bestått / Ikke bestått