Data science and bioinformatics for fisheries and aquaculture
About the 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.
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 program, 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.
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 analysis and inference
- can approach problem solving in groups
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
Prerequisites
Obligatorisk forkunnskapskrav
FSK-1120 Aquatic biology for Fisheries and aquaculture scienceor BIO-1501 Aquatic Ecologyor BIO-2018 Genetics, molecular biology and bioinformatics
Teaching methods
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.Schedule
The schedules are normally finalized and published well in advance of the start of the semester, often a few weeks beforehand. This gives students the opportunity to organize their studies and prepare for upcoming activities.
It is recommended to check the schedule regularly, as changes may occur.
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.
Examination
| Off campus exam | Duration: 1 Weeks Weighting: 2/5 |
Grade: A–E, fail F |
| Oral exam | Duration: 35 Minutes Weighting: 3/5 |
Grade: A–E, fail F |
To take an examination, the student must have passed the following coursework requirements
| Assignments | Grade: Approved – not approved |
| Seminar attendance | Grade: Approved – not approved |
Everything you need to know about before, during, and after the exam; registration, absence, appeals, and diplomas: UiT Exams homepage
More info about the coursework requirements
Assignments: Delivery of 7 assignments (individual submissions) in digital format, either the development of a short script or the result of a data analysis steps. Approved is required to write and defend Off campus exam.
Seminar attendance: Candidates are required to attend at least 2 out of 3 seminars.
More info about the oral exam
Home exam: The written home exam will consist of a report solving practical problems (as a group of two persons).
Oral exam: The individual oral exam entails explaining the concepts used to solve parts of the home exam with a short presentation and interpretations of scripts learned during the course. Up to 35 minute duration.
