Course code ECOL340

ECOL340 Exploring and Analyzing Data in Ecology and Natural Resource Management

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Showing course contents for the educational year 2022 - 2023 .

Course responsible: Richard Bischof
ECTS credits: 5
Faculty: Faculty of Environmental Sciences and Natural Resource Management
Teaching language: EN
(NO=norsk, EN=Engelsk)
Limits of class size:
30
Teaching exam periods:
This course starts in the autumn parallel. This course has teaching/evaluation in the autumn parallel.
Course frequency: Annually
First time: Study year 2015-2016
Preferential right:
M-ECOL, M-NF, M-SF, M-MINA, M-REIS, M-FORNY
Course contents:
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.
Recommended prerequisites:
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).
Mandatory activity:
First lecture is compulsory. In addition students are required to attend a minimum of 75% of teaching sessions. 
Assessment:
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".
Nominal workload:
125 hours
Entrance requirements:
Special requirements in Science
Type of course:
36 houres.
Note:

Registration deadline is September 1. 

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).

Examiner:
At the end of the semester, an external examiner evaluates the collection of each student’s submissions associated with assignment in order to determine the final grade.
Examination details: Portfolio assessment: Letter grades