autumn 2023
BIO-3032 Big data and Artificial intelligence for environmental, ecological and biological science: an introduction - 10 ECTS

  1. Startsida
  2. Emnekatalog
  3. Big data and Artificial intelligence for environmental, ecological and biological science: an introduction

Type of 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

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.

Course overlap

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

Course content

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.

Recommended prerequisites

BIO-1007 Quantitative Methods

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

Language of instruction and examination

English

Teaching methods

Several teaching methods are used in the course. These include lectures (40 hours), flipped classrooms (20 hours), team-based and group projects (40 hours). This is combined with reading, videos, quizzes, group assignments and individual exams (oral & written) (ca. 200 hours).

Information 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

Error rendering component

More info about the assignment

  • Written group assignments/reports (20% of final grade)
  • Individual written assignment/reports (20% of final grade

For the assignments, criteria will be defined for each assessment and will be made available for the students.

The students have to deliver the group assignment and the individual written assignment in order to be allowed to take the oral and written exams.

Re-sit examination

Re-sit exam is offered to those who did not pass the ordinary exams. This applies only to students that have already delivered the group assignment/report, but they still need to complete the individual written assignment/report.
  • About the course
  • Campus: Tromsø |
  • ECTS: 10
  • Course code: BIO-3032
  • Tidligere år og semester for dette emnet