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 this data, this module provides learners with the training they need to tackle these issues with confidence.
Specific objectives will be to learn about and use the methods and tools needed to capture, verify, manage and analyze scientific and naturalist data. Acquire the vocabulary of statistics and master descriptive statistics. Acquire the knowledge and approach needed to take statistical aspects into account when implementing sampling protocols. This will be divided into 3 sequences as follows:
Sequence 1: Basics of data processing and descriptive statistics
Theoretical and practical knowledge of data entry, formatting, manipulation, control and visualization (spreadsheet/R). Descriptive statistics vocabulary (e.g. mean, median, variance), proficiency in descriptive statistics tools.
Sequence 2: Basics of inferential statistics
Inferential statistics (most commonly used hypothesis tests), power analysis (case studies).
Sequence 3: Tools and indices for describing diversity
Biodiversity indices (e.g. Shannon and Simpson diversity indices, species richness), tools used to study biodiversity (accumulation curves, clustering analyses [NJ, UPGMA]) - Case study using R software.
Knowledge control
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test |
coefficient |
No. of hours |
Nb Sessions |
Organization (FDS 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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Further information
Open to continuing education
Target skills
Skills targeted by the EU :
- E-1 Descriptive statistics (trend, dispersion, distribution)
- E-2 Know the classic 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 Select and perform a hypothesis test appropriate to the biological question and 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 analyses, calculations, and data manipulation and representation.
- I-17 Be able to use IT tools for data entry, analysis and storage (spreadsheet, R).