ECOL340 Exploring and Analyzing Data in Ecology and Natural Resource Management

Credits (ECTS):5

Course responsible:Richard Bischof

Campus / Online:Taught campus Ås

Teaching language:Engelsk

Limits of class size:30

Course frequency:Annually

Nominal workload:125 hours

Teaching and exam period:This course starts in the autumn parallel. This course has teaching/evaluation in the autumn parallel.

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.

  • Lectures and hands-on exercises. Group work forms an important part of the course.
  • 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.
  • A completed bachelor degree and at least one course in statistics.
  • Formative evaluation throughout the course. The final grade is determined based on performance during 8 short 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 "mappe".

  • An external censor evaluates the mandatory assignment setup which is then used to score student performance.
  • First lecture is compulsory. In addition students are required to attend a minimum of 75% of teaching sessions.
  • 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).

  • 36 houres.
  • Special requirements in Science