Vår 2024

FSK-2053 Data science and bioinformatics for fisheries and aquaculture - 10 stp

The course is administrated by

Norges fiskerihøgskole

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.

Course contents

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

The course focuses on illustrating how science, industry, and management can utilize data science to solve problems related to human impact and changing ecosystems. We will also introduce molecular genetics 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, NCBI Genbank and the BOLD database, will also be covered and used throughout the course to solve relevant problems.

The course is divided into two main modules: Data science and bioinformatics and includes theory and extensive hands-on practical sessions.

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.

Objective 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

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 extensive hands-on practical exercises, with seminars to discuss the applications of data science and bioinformatics to address the practical problems in aquaculture and fisheries. 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.