spring 2022
FSK-2053 Data science and bioinformatics for fisheries and aquaculture - 10 ECTS

Last changed 17.06.2022

Application deadline

Applicants from Nordic countries: 1 December for the spring semester. Exchange students and Fulbright students: 1 October for the spring semester.

Type of course

This is an elective course in the bachelor’s programme in Fisheries and Aquaculture science. The course is also available as a singular course. 

Admission requirements

Higher Education Entrance Qualification (generell studiekompetanse) or prior learning and work experience (realkompetanse).

Application code (Nordic applicants): 9199

Recommended prerequisite: Broad knowledge about fisheries, aquaculture production and the biology related to these industries, e.g. from the FHV, or general biology from a biology programme, is an advantage. Some previous experience in programming languages (e.g. R) and as a user of Linux systems is also an advantage, but it is not required.

Course content

The objective of this course is to teach students how modern data science tools can be used to solve problems and provide solutions for fisheries, aquaculture and within biology. Practical knowledge and experience with data science tools are key in today’s science, industry, and management and a much requested skill. The course  provides basic training in programming languages (e.g. R) and statistics. The students acquire skills and competence in the use of remote computing environments and online data resources.

The course focuses on revisiting examples given in past courses, to illustrate how science, industry, and management can utilize data science to solve problems related to human impact and changing ecosystems. We will also explore molecular genetic and ecological problems that can be solved with bioinformatics. The course will also include traditional statistical methods used in data science and we will learn ways to visualize results. The use and data-mining from open-data platforms, such as Barentswatch or NCBI Genbank, will also be covered and used throughout the course to solve relevant problems.

Objectives of the course

A candidate who has completed his or her qualification should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge The candidate

  • understands the foundations of data science and its potential applications in biology, fisheries, and aquaculture
  • has broad knowledge of the importance of genetic and biodiversity information for the management of natural ecosystems, fisheries and aquaculture
  • has knowledge on organizing and structuring different kinds of data to facilitate the analysis and inferring useful information

Skills The candidate

  • can write simple scripts to analyze and visualize data
  • can retrieve public data from on-line data repositories
  • can analyze genetic sequences using bioinformatics tools
  • can access remote servers and computing resources

Competence The candidate

  • can implement data science approaches to solve practical problems
  • can apply data science as a leveraging tool to address current societal challenges
  • can exchange opinions and experiences with others with a background in the field, thereby contributing to the development of good practice

Language of instruction and examination

Given the importance of English for data science and bioinformatics, the course will be taught in English. Some international public resources, databases and software tools will be used which are exclusively available in English. The students can use either English or Norwegian for writing their exercises and written home exam.

Teaching methods

The teaching will be a combination of theoretical lectures and practical exercises, with seminars to discuss results of different approaches to relevant practical problems. An important part of the sessions will be taught in the computing classroom, so the students can have access to online resources and tools. The course will also focus on learning academic methods in a student friendly environment.

Information to incoming exchange students

This course is available for inbound exchange students.

This course has academic prerequisites. Please see the «Admission requirements» section for more information.

Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: INBOUND STUDENT MOBILITY: COURSE COORDINATORS AT THE FACULTIES | UiT


The exam consists of 2 parts, each counting 50 % of the final grade.

  • Five exercises in computer format, either development of short scripts or results of data analyses (counts 50 %)
  • Written home exam consisting of solving a practical problem and reporting it as short report of max 3500 words (including tables, figures, scripts, but excluding references) (counts 50 %)

The students will get feedback on their 5 exercises and written home exam.

The grading scale is A - F, where F is fail.

Both parts must be passed in order to pass the course.

There will not be a re-sit examination for students that did not pass the previous ordinary examination. 


  • About the course
  • Campus: Tromsø |
  • ECTS: 10
  • Course code: FSK-2053
  • Tidligere år og semester for dette emnet