ECTS
4 credits
Component
Faculty of Science
Description
Given the general context of an increase in the amount of naturalist and scientific data collected/available and the growing need to collect, process, analyze, and archive these data, this module offers learners a training course that will give them the means to approach these issues serenely.
The specific objectives will be to know and use the methods and tools necessary for the capture, verification, management and analysis of scientific and naturalist data. Acquire the vocabulary of statistics and master descriptive statistics. Acquire the knowledge and the necessary approach to take into account the statistical aspects in the implementation of sampling protocols. This will be divided into 3 sequences as follows:
Sequence 1 : Concepts in data processing and descriptive statistics
Theoretical and practical knowledge of data entry, formatting, manipulation, control and visualization (in spreadsheet/R). Descriptive statistics vocabulary (e.g. mean, median, variance), mastery of descriptive statistics tools
Lesson 2 : Inferential statistics concepts
Inferential statistics (most commonly used hypothesis tests), power analysis (case studies).
Sequence 3: Tools and indices for describing diversity
Biodiversity indices (e.g. Shannon's diversity index, Simpson's diversity index, species richness), tools used to study biodiversity (accumulation curves, clustering analyses [NJ, UPGMA]) - Case study with R software.
Knowledge control
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test |
coefficient |
Nb of hours |
Nb Sessions |
Organization (SDS or local) |
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Written |
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Continuous control |
100% |
2 |
2 |
local |
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TP |
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Oral |
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Additional information
Open to continuing education
Targeted competencies
Competencies covered by the EU:
- E-1 Know descriptive statistics (trend, dispersion, distribution)
- E-2 Know the classical tests, conditions and limits of use
- E-4 Know how to use statistical knowledge to set up a sampling and/or experimentation plan
- E-5 Numerical description and graphical representation of data
- E-6 Know how to select and perform a hypothesis test appropriate to the biological question and the type of data (t-test, Fisher exact test, Chi-square, comparison of 2 proportions, correlation)
- E-7 Know how to use a spreadsheet and R software for simple statistical analysis, calculations, and data manipulation and representation
- I-17 Be able to use computer tools for data entry, analysis and storage (spreadsheet, R)