Courses open for registration

This course shows how to carry out phylogenetic comparative analyses in the R statistical computing environment.

This course assists you in building and empowering your attitude, behaviour and position in your professional surroundings. No matter how competent a person you are, you will not have sustained and lasting success unless you are able to effectively lead yourself, influence, engage and collaborate with others.

The aim of this course is to provide an introduction to R and R Studio. It introduces the participants to R language syntax, to enable them to write their own R code. They will also learn about R data-types and data-structures, and they will be taught how to explore the data and produce plots. The course will be a combination of lectures and practicals.

Racism, bullying, sexual harassment and other inappropriate behaviours contribute to a toxic culture. The impact they have on people can be hugely detrimental – leading to stress, anxiety and mental health problems. As we are seeing in the news, it can be deeply corrosive and damaging if these behaviours and attitudes are normalised over time. That’s why it’s essential for everyone to know how to speak up when problems arise.

In this workshop, participants will learn the principle of tidy data, how to transform and combine datasets using the tools from the tidyverse and how to generate advanced visualization with the ggplot2 package.

This postgraduate course focuses on root ecology, and how roots grow, function and interact with the surrounding environment. We will discuss differences and similarities between roots in natural ecosystems and roots of agricultural plants.

This course is for participants who want to deepen their knowledge of R programming and be able to use R to deal efficiently with computational problems and programming tasks. Participants will also gain more knowledge on working with R data structures and solving common problems, like working with and manipulating factors, extracting information from statistical models, working with other types of data (textual, time and dates) and handling multiple data files.

Geostatistics is concerned with the analysis and modelling of spatial variability. It also addresses how quantified spatial variability can be used in optimal spatial interpolation and spatial stochastic simulation. Fields of application include hydrology, soil science, ecology, geology, agriculture, and forestry.

This is a refresher course. The level is that of a second course in Statistics. We will refresh basic knowledge of Probability, Statistical Inference (Estimation and Testing), t-tests, simple cases of Regression and ANOVA, Experimental Design, Nonparametric Tests, and Chi-square Tests. Some time is reserved to discuss statistical problems of the participants.

The aim of this course is to provide an understanding of the statistical principles underlying experimentation. A proper set-up of an experiment is of utmost importance to be able to draw statistically sound conclusions.

This course will make you familiar with a modern toolbox of analysis techniques at the interface of statistics and machine learning. You will develop the skills to build and evaluate modeling strategies for complex (big, high-dimensional, hierarchically-structured) data as occurring in the areas relevant to WIAS and PE&RC. Moreover, the course will give you insights in the connections between modern modeling strategies and will teach you to ask the right questions in order to choose the best method for the data at hand. Illustrations will come from relevant case studies.