Course code STIN100

STIN100 Biological Data Analysis

There may be changes to the course due to to corona restrictions. See Canvas and StudentWeb for info.

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Showing course contents for the educational year 2018 - 2019 .

Course responsible: Jon Olav Vik, Torgeir Rhodén Hvidsten
Teachers: Hilde Vinje, Jon Olav Vik, Simen Rød Sandve
ECTS credits: 10
Faculty: Faculty of Chemistry, Biotechnology and Food Science
Teaching language: NO
(NO=norsk, EN=Engelsk)
Limits of class size:
Teaching exam periods:
This course starts in the Fall semester. This course has teaching/evaluation in the Fall semester.
Course frequency: Annually
First time: 2018H
Course contents:
Biology generates increasingly larger amounts of data that cannot be analyzed manually. To extract new insight from big data sets, modern biologists need to acquire knowledge and skills in computer programming and basic data analysis. This course provides basic skills in the programming language R and introduces the student to common methods for visualization and analysis of multi-dimensional biological data.The course is organized around supervised student groups analysing relevant data sets.
Learning outcome:

KNOWLEDGE: The students will acquire basic knowledge in handling, visualizing and analysing multi-dimensional biological data. Through examples, they will learn how some of the most important biological data sets are generated and how this data should be preprocessed to correct for systematic errors. Students will be able to explain principles behind basic methods for data visualization and analysis.

SKILLS: The students will acquire basic skills in the programming language R. They will be able to write programs that perform basic data processing tasks and simple visualization and data analysis. Emphasis will be put on the ability to interpret results from applying different data analysis methods.

Learning activities:
Some lectures, but emphasis will be put on practical group exercises using computers.
Teaching support:
Teaching materials will be available in Canvas.
Lecture notes, exercises and handouts. Textbook will be announced at the start of the course, if applicable.
Recommended prerequisites:
Mandatory activity:
There will be compulsory assignments for the students to hand in and have approved before being evaluated.
Group work: Students write a paper based on analysis of a relevant data set (counts 100%). Pass/Fail.
Nominal workload:
Lectures: 12 hours. Exercises: 52 hours. Individual study: 236 hours.
Entrance requirements:
MATRS - General admission requirements, and R1 or (S1+S2) or similar mathematical skills
Type of course:
Lectures: 2 hours per week (6 weeks). Exercises: 4 hours per week.
An external examiner grades 25 selected examination papers.
Examination details: Continous exam: Bestått / Ikke bestått