Table of contents

The course is connected to the following study programs

Teaching language

English, unless all participants are Norwegian.

Prerequisites

MSc degree and basic statistical knowledge (for instance BIO403 or relevant work experience). Interested MSc students can apply to be enrolled.

Recommended previous knowledge

Experience with R or another coding language such as C, Julia, or Python.

Learning outcomes

This course is designed to provide PhD fellows with in-depth knowledge of quantitative research methods. The contents can to some degrees be tailored to the individual candidates’ needs.

After having completed the course, the students will:

  • have acquired advanced knowledge within specific themes of statistical methodology in ecology, evolution, or environmental sciences
  • have improved their programming skills
  • be able to critically evaluate advantages and limitations of different statistical methods
  • be able to prepare teaching materials for specific topics
  • have implemented routines for reproducible science

Contents

Quantitative methods used in ecology, evolution, and environmental sciences. The contents of each iteration of the course will be given well in advance of the application deadline.

Teaching and learning methods

Lectures, seminars, and practical exercises. The students will prepare and present teaching materials for a chosen topic and will peer review each other’s materials. There is a large degree of flexibility regarding course content and methods that can be tailored to the individu More information will be given in Canvas at the start of the course.al candidate’s needs. The course will be given as an on-campus/digital hybrid.

The estimated student workload in this course is 135 hours.

Examination requirements

At least 90 % attendance on compulsory learning activities including student-prepared teaching materials and peer review.

Details will be given in Canvas at the start of the course.

Examinations

Individually written report and seminar based on analyses of own data. The report must be approved to be able to present it at the seminar. Both partial exams must be passed.

Graded pass/fail.

More information will be given in Canvas at the start of the course.

Student evaluation

The course will be evaluated in accordance with the quality system for education, chapter 4.1.

Last updated from FS (Felles studentsystem) Oct. 15, 2026 7:21:19 AM