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 2017 - 2018 .

Course responsible: Richard Bischof
Teachers: Hans Ole Ørka, Leif Egil Loe, Thrond Oddvar Haugen
ECTS credits: 5
Faculty: Faculty of Environmental Sciences and Natural Resource Management
Teaching language: EN
(NO=norsk, EN=Engelsk)
Limits of class size:
25
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-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. Examples used during the course to illustrate concepts and techniques will be drawn from the breadth of research at the department. 
Learning outcome:

Knowledge After successfully completing the course, students will have acquired the conceptual and technical skills necessary to work with their own data during thesis preparation. The course will also give students the pre-requisites for adding more advanced analytical tools and specialized methods to their repertoire during future studies.

Skills: After completed course, the students shall be able to do the following:

  • Basic programming in R
  • Data setup (import, quality control, formatting, etc.)
  • Exploring data (summaries, tabulation, etc.)
  • Graphics
  • Statistical distributions, tests, and models
  • Presenting results (predictions, figures, etc.)

Competence: Students will gain the necessary competence to explore and analyze data with R, today's primary statistical software.

Learning activities:
Lectures and statistical exercises
Teaching support:
The teachers are present or available for individual questions during teaching session and normal office hours.
Syllabus:
Will be published in Canvas.
Prerequisites:
Basic statistics (STAT100 or higher)
Recommended prerequisites:
Mandatory activity:
Students are required to attend a minimum of 75% of teaching sessions. 
Assessment:
One individual semester assignment accounts for 30% of the total grade, and the final written exam (3 hrs) accounts for 70% of the grade. All evaluated elements in the course must be passed to pass the course.
Nominal workload:
150 hours
Entrance requirements:
Special requirements in Science
Type of course:
36 houres.
Note:
It is assumed that the students have chosen a topic for their master's thesis. Students may bring their own data to work on.
Examiner:
An external examiner evaluates the final written exam and the semester assignments.
Examination details: Continuous exam: A - E / Ikke bestått