BB852: Data handling, visualization and statistics

Study Board of Science

Teaching language: English
EKA: N110040102
Censorship: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master's level course approved as PhD course

STADS ID (UVA): N110040101
ECTS value: 5

Date of Approval: 17-03-2021

Duration: 1 semester

Version: Approved - active


04013901 + N110015101 (former UVA) is identical with this course description. 

Entry requirements


Academic preconditions

Students taking the course are expected to:

  • Have basic knowledge of statistics and mathematics
  • Have a Bachelor’s degree in a field with some level or focus on quantitative methods

Course introduction

The aim of this course is to provide students with the tools to explore, analyse, and interpret data, and to present the results of biological studies.
The course builds on knowledge acquired in previous courses in mathematics and statistics. 
The course gives an academic basis for work carried out in individual study activities and the masters project.

In relation to the competence profile of the degree it is the explicit focus of the course to develop student skills that help them understand and reflect on theoretical concepts and experimental methods within the field of biology. The course aims to enhance students’ biological thinking and reasoning skills and to develop skills that are critical to the planning and performance of advanced biological research. These skills include the use of hypotheses and reasoning to understand develop and understand research outputs, and the management of data collection and use. Specifically students will:

  • Develop skills in data exploration, visualisation and interpretation.
  • Develop the students’ competence to undertake appropriate quantitative analysis of the student’s own data (e.g. masters project).
  • Develop skills to critically evaluate statistical analyses (e.g. in scientific papers/presentations). 
  • Develop skills to use the R statistical software for analysis and graphing. 
  • Structure personal learning

Expected learning outcome

The learning objectives of the course are that the student demonstrates the ability to:
  • formulate appropriate scientific questions in biological disciplines, e.g. ecology, physiology, neurobiology, and evolutionary biology.
  • design appropriate laboratory or field studies in order to address scientific questions.
  • manipulate and explore visually data from experimental and field studies.
  • select appropriate statistical approaches for a variety of different data types.
  • fit and interpret appropriate regression models (ordinary least squares, generalised linear models), randomisation tests, and “classical tests” (t-tests, chi-squared tests etc.).
  • understand and commonly used model selection approaches. 
  • present quantitative the results from biological studies, including graphically.


The following main topics are contained in the course:
  • Questions and hypotheses in research
  • Designing data collection for biological studies
  • Manipulating data with R (dplyr, tidyr, magrittr)
  • Visualising data with R (ggplot2)
  • Regression models, randomisation tests and “classical” tests
  • Model selection
  • Presenting results of statistical analyses


Beckerman, Childs & Petchey (2017) Getting Started With R. 2nd Edition. Oxford University Press.
Other literature uploaded to, or linked to, on Blackboard.
See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)








Second examiner: Internal


7-point grading scale


Student Identification Card


Normally, the same as teaching language

Examination aids

To be announced during the course.

ECTS value


Additional information

The portfolio exam consists of multiple choice tests at the end of the semester (weighs 30% of the total assessment) and a take-home exam / report (weighs 70% of the total assessment).
Reexam in the same exam period or immediately thereafter.
The mode of exam at the re-examination may differ from the mode of exam at the ordinary exam.

Indicative number of lessons

44 hours per semester

Teaching Method

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.

Intro phase: 22 hours
Skills training phase: 22 hours, hereof exercise class: 22 hours

Activities during the study phase:

  • Computer-based exercises 
  • Research oriented learning (guided exploration of data)
  • Reading relevant literature, or other written material (e.g. blog posts)

Teacher responsible

Name E-mail Department
Owen Jones Biologisk Institut, Biodemography Unit, Interdisciplinary Centre on Population Dynamics – Four Faculties Affiliates


08 - 09
09 - 10
10 - 11
11 - 12
12 - 13
Class f
13 - 14
Class h1e
14 - 15
Class f
15 - 16
Class h1e
Show full time table

Administrative Unit

Biologisk Institut

Team at Registration & Legality


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