About this course

This course will give masters students a hands-on introduction to setting up, exploring, summarizing, and analyzing scientific data. Students will be taught a tool for doing this: the R statistical programming environment, which today is the most widely-used and flexible statistical software. This course is tailored towards students enrolled in master's programs at the Faculty of MINA and will prepare them for their own research activity during their thesis work.

Learning outcome

Knowledge: After completing the course, students will possess the conceptual and practical knowledge necessary to work with their own data during thesis preparation.

Skills: After completing the course, students shall have the skills to perform the following:

  • Basic programming in R
  • Data preparation (import, quality control, formatting)
  • Data visualization and exploration (graphs, summaries, tabulation
  • Statistical tests and models
  • Interpreting and presenting results (inferences, predictions, graphs)

Competence: After completing the course, students shall possess the competence to explore and analyze data with R, today's primary statistical software. This competence can serve as a basis for adding more advanced analytical tools and specialized methods to the student’s repertoire during future studies.

  • Learning activities

    Lectures and hands-on exercises. Group work forms an important part of the course.

  • Teaching support

    The teachers are present or available for individual questions during teaching session and normal office hours. In addition, 1-hour long weekly tutorial sessions are offered in parallel with the course.

  • Syllabus

    Will be published in Canvas.

  • Prerequisites

    A completed bachelor degree and at least one course in statistics.

  • Assessment method

    Portfolio assessment.

    The final grade is determined based on performance during 8 assignments which students are to complete (mostly R scripts) and deliver weekly. Assignments involve a combination of individual and group work. The collection of all weekly assignments to be delivered by a student constitutes one portfolio.



    Portfolio assessment Karakterregel: Letter grades

  • About use of AI

    Assignements: K3 - Full use of AI.

    The use of AI is permitted, but it must comply with the Guidelines for Use of Artificial Intelligens (AI) at NMBU.

    Descriptions of AI-category codes.

  • Examiner scheme

    An external censor evaluates the mandatory assignment setup which is then used to score student performance.

  • Mandatory activity

    First lecture is compulsory. In addition students are required to attend a minimum of 75% of teaching sessions.

  • Notes

    Registration deadline is August 31.

    Students should have chosen a topic for their master's thesis and have access to data to work with during the course (either their own or their supervisor’s).

  • Teaching hours

    36 houres.

  • Preferential right

    M-ECOL, M-NF, M-SF, M-MILJØ

  • Admission requirements

    Special requirements in Science