Big data and Artificial intelligence for environmental, ecological and biological science: an introduction
About the course
Master course for biology students - principally aimed at MSc-students specializing in "Ecology and sustainability".
The course is available as a singular course.
Minimum number of students: 4
This course passed the quality control of the EU (EIT Deep Tech) with a quality seal.
The course provides an introduction to Big Data and AI, focusing on data extraction, analysis, and predictive analysis. Students will learn about different data formats and techniques for converting data, as well as testing, correlation, clustering, and data visualization. The course covers open-access data and FAIR principles and uses real-world big data sets related to environmental, ecological and biological sciences. Students will create AI algorithms to analyze big data. The course emphasizes collaboration and group work, preparing students for careers at the intersection of science and society.
Admission requirements
Local admission, application code 9371 - Master`s level singular course.
Admission requires a Bachelor`s degree (180 ECTS) or equivalent qualification, with a major in biology of minimum 80 ECTS.
Objectives of the course
Knowledge:
- Understand the fundamentals of big data and its role in sustainability
- Become familiar with using different data analytics tools to process and visualize big data
- Get knowledge of spatial data analysis using GIS programs
- A basic understanding of artificial intelligence (AI) for analysing big data
- Understand what are the metadata, FAIR principle and the ethical & privacy considerations in handling sensitive data
Skills:
- Use different resources and data analytics tools to analyse and visualize big data
- Use cloud-based environments to convert raw data to clean and tidy data
- Analyze big spatial data
- Apply artificial intelligence algorithms to analyse big data
Competence:
- Evaluate and visualize big data
- Develop cloud-based codes utilizing artificial intelligence algorithms to analyse big data
Prerequisites
Recommended prerequisites
BIO-1007 Quantitative Methods
Credit reduction
If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:
- BIO-8032 Advanced course on big data and AI for environmental, ecology and biology science 1 ects
Teaching methods
Language of instruction and examination
EnglishInformation to incoming exchange students
This course is open for inbound exchange students who meet the admission requirements. 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: https://en.uit.no/education/art?p_document_id=510412
Examination
| Oral exam | Date: 12.12.2025 08:00 Duration: 30 Minutes Weighting: 4/10 |
Grade: A–E, fail F |
| School exam | Date: 15.12.2025 09:00 Duration: 2 Hours Weighting: 6/10 |
Grade: A–E, fail F |
To take an examination, the student must have passed the following coursework requirements
| Written group assignments/reports | Grade: Approved – not approved |
| Individual written assignments/reports | Grade: Approved – not approved |
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