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Data Handling, Visualization and Statistics

Idioma

Inglés

Formato del curso On-site
Fecha 2020-09-02 - 2020-11-26

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 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

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

Prerrequisitos

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

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.