autumn 2020
FSK-3006 Model theory and data processing methods - 10 ECTS

Last changed 22.10.2020

Application deadline

Applicants from Nordic countries: 1 June for the autumn semester. Exchange students and Fulbright students: 15 April for the autumn semester.

Type of course

This subject is obligatory in the Master's programme in International Fisheries Management (IFM). The course can also be taken as a singular course.

Admission requirements

Application code: 9371

Recommended entrance requirements are BIO-3556 Fishery Biology and Harvest Technology or similar background, and basic familiarity with quantitative methods.


Course content

The course covers basic statistical theory and the quantitative and qualitative analysis of structured and unstructured information related to fisheries management.

Students are drilled in data retrieval, treatment, analysis, and presentation, using simple and widely available software.

Relevance for study program: The course provides a practical background for scientific analysis and reporting in a multidisciplinary environment.


Recommended prerequisites

BIO-3556 Fishery Biology and Harvest Technology

Objectives of the course

Intended learning outcomes:

Knowledge:

  • apply basic quantitative methods to process, analyze, test hypotheses, and visualize structured and unstructured data (e.g. generalized linear models)
  • apply basic qualitative methods to process, analyze, test hypotheses, and visualize structured and unstructured data (e.g. content analysis, conceptual frameworks)
  • explain results of the data processing and data analysis methods that were used
  • use simple models to represent systems and make forecasts
  • familiarize with both academic and applied/business-context research and development environments

Skills:

  • summarize and present qualitative and quantitative data
  • perform statistical analyses using different software
  • practice different oral and written communication skills
  • practice time-management skills

General competences:

  • communicate findings of analyses to support decision-making


Language of instruction

The language of instruction and all syllabus material is English.

Teaching methods

Lectures, seminars, short visits, workshops, computer labs, peer-teaching, simulations, flipped-classroom, formative assessment.

The learning outcomes are effectively achieved through active student participation. Students are expected to prepare before every session.


Assessment

The exam consists of portfolio assessment. The grading scale is A-F, where A-E is passed, and F is failed.

Submission of reports and essay: electronically.

Portfolio

  • 20% of the final grade: Three written reports on quantitative methods assignment (each report: max. 7 pages, without references and annexes). Each report will be graded individually and an average will be made.
  • 15% of the final grade: One written report on qualitative methods assignment (max. 3 pages, without references and annexes).
  • 15% of the final grade: One written or video report on modelling assignment (if written report, max. 3 pages, without references and annexes).
  • 50% of the final grade: Final essay (home exam) on given topic (37.5 effort hours) (max. 10 pages, without references and annexes).

Work requirements

The following work requirements are mandatory. Each item will be evaluated with pass/fail. All requirements have to be passed in order for the student to be allowed to take the exam.

  • Oral presentation on a given article (max. 20 minutes).
  • Oral presentation on a given topic (quantitative method, qualitative method or modelling) (duration varies depending on the topic).
  • Oral presentation on use of data analysis methods in own Master’s thesis (max. 20 minutes).
  • Oral presentation of the final essay (on given topic) (group presentation, max. 20 min).
  • Filling in an on-line time-sheet for all activities in the course.

A re-sit exam will be arranged in the next semester. A student may re-sit only the part of the portfolio that he/she has failed.


Schedule

Course overlap

BED-3105 Research Seminar 1 stp
BIO-3012 Studydesign and dataanalysis in Biology II 3 stp
BIO-3524 Analysis of multidimensional data in ecology and environmental 3 stp
HEL-3070 Biostatistics 3 stp
STA-3300 Applied Statistics 2 3 stp
FSK-2020 Sustainable fisheries 10 stp
  • About the course
  • Campus: Tromsø |
  • ECTS: 10
  • Course code: FSK-3006