BB852: Data handling, visualization and statistics
Study Board of Science
Teaching language: English
EKA: N110040102
Assessment: 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: 12-04-2023
Duration: 1 semester
Version: Approved - active
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, the course's explicit focus is 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 develop skills critical to the planning and performance of advanced biological research. These skills include using hypotheses and reasoning to develop and understand research outputs and managing data collection and use. Specifically, students will:
- Develop data science skills in data exploration, visualisation and interpretation.
- Develop the competence to undertake appropriate quantitative analysis of their own data (e.g. masters project).
- Develop skills to critically evaluate statistical analyses (e.g. in scientific papers/presentations).
- Develop skills in using 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.
Content
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
Literature
Beckerman, Childs & Petchey (2017) Getting Started With R. 2nd Edition. Oxford University Press.
Other literature uploaded to, or linked to, on itslearning.
See itslearning for syllabus lists and additional literature references.
Other literature uploaded to, or linked to, on itslearning.
See itslearning for syllabus lists and additional literature references.
Examination regulations
Exam element a)
Timing
Autumn
Tests
Portfolio
EKA
N110040102
Assessment
Second examiner: Internal
Grading
7-point grading scale
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course.
ECTS value
5
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.
Indicative number of lessons
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
Timetable
Administrative Unit
Team at Educational Law & Registration
Offered in
Recommended course of study
Transition rules
Transitional arrangements describe how a course replaces another course when changes are made to the course of study.
If a transitional arrangement has been made for a course, it will be stated in the list.
See transitional arrangements for all courses at the Faculty of Science.