Online live sessions from Monday to Friday, from 15:00 to 19:00 (GMT+2, Madrid time zone).
The course is given in four modules covering exploratory data analysis, univariate and multivariate statistical techniques and a final discussion session where students will work and discuss their projects:
- Module I. Programming in R and Rstudio
- Module II. Exploratory Data Analysis (EDA)
- Module III. Univariate Statistical Analysis (UniStat)
- Module IV. Multivariate Statistical Analysis (MultiStat)
This course will cover some advanced issues in most statistical computing workflow for Life Sciences. Together with the statistical analysis, we will cover several aspects of data structure and workspace management, and visualization techniques using R and Rstudio.
This course is of intermediate level. Some basic knowledge of R and statistics is highly recommended.
Basic knowledge of Statistics and R.
All participants must have a computer (Windows, Macintosh, Linux). Webcam and headphones are strongly recommended, as well as a good internet connection.
Click here to see the full Program
Module I. Programming in R and Rstudio
- Along with this model, we will review the most commonly used files in any statistical programming workflow like the scripts (.R) the allocated memory (.RData) and the novel Rstudio projects (.Rproj) for establishing dedicated working directories, workspace, history, and source documents.
Module II. Exploratory Data Analysis (EDA)
- During any statistical programming workflow, almost half of the time must be given to data exploration. However, not every exploration is a valid exercise. Here we will review the most common assumption in a classical statistical analysis like normality, heterogeneity and independence in the data.
Module III. Univariate Statistical Analysis (UniStat)
- In module III we will review the classic univariate approach for statistics like te linear models and their extensions. The most common analysis like ANOVA, ANCOVA or Regression analysis will be covered. For those common cases in ecology, where the linear models fail (e.g. non-negative data in count/abundance data) we will present some extensions covering the Generalized Linear Models (GLM) for count and presence/absence data and we will review some insights in Generalized Additive Models (GAM).
Module IV. Multivariate Statistical Analysis (MultiStat)
- Along with this fourth module, we will cover the analysis of multivariate data. We will review two different approaches to understand community (multivariate) data based on different ordination techniques. Thus, two approaches from unconstrained ordination, like Principal Component Analysis (PCA) and non-Metric Multidimensional Scaling (nMDS) will help us to reveal patterns along with our community data. Finally, we will use two approaches from constrained ordination like Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA) to understand the role of some environmental (explanatory) variables over the community structure in our community Data.
- Course Fee
- Early bird (until June 30th, 2021):
- 476 €
(380.8 € for Ambassador Institutions)
- Regular (after June 30th, 2021):
- 570 €
(456 € for Ambassador Institutions)
- Price is VAT included.
After registration you will receive confirmation of your acceptance in the course. Payment is not required during registration.