Machine learning plays an increasingly important role in many scientific areas, including geo-information science and remote sensing, ecology, biosystems engineering, and bioinformatics. 

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.

In this module we study how to analyse data that are not normally distributed. We look at fractions (logistic regression), counts (Poisson regression, log-linear models), ordinal data (threshold models), and overdispersion. We discuss (quasi-) maximum likelihood estimation and the deviance.

Researchers trying to summarize the constantly growing body of published research are increasingly using meta-analysis. The focus of this 2-day course will be on concepts of linear models and mixed linear models in meta-analysis. The statistical software R will be used.

This course will empower attendees to maximize and defend their scientific interpretations based on their expert knowledge as informed by quantitative results. A new procedure, “causal knowledge analysis” will be introduced as a way to document expert knowledge and set the stage for SEM analyses. Once that is accomplished, structural equation modeling techniques will be presented as a methodology for representing and evaluating hypotheses involving networks of relationships.

Extend participants' basic knowledge of R by teaching them more advanced programming concepts and the use of R for more complex problem solving, going beyond just statistics.

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.

The course Multivariate Analysis offers a thorough introduction to multivariate statistical methods, tailored for researchers working with complex datasets where multiple variables are measured simultaneously.

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.

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. 

Programming can serve multiple purposes. Purposes like developing applications and working with data are also very useful for research. For dealing with these issues, Python offers many libraries. Getting the skills of working with some of these libraries will enable future learning. This can be for more advanced programming applications, but also for self-learning to apply different libraries.

This is a brief introductory workshop especially aimed at those who have little or no experience working with LaTeX. There will be a theoretical and a hands-on practical part. The theoretical part covers the basics of what TeX and LaTeX are, how they compare with Office text processors, and how to get started with writing documents. The practical is a hands-on demonstration of some of the more useful features of LaTeX, such as making Gantt charts, bibliography management and automatic acronym expansion.

In this module we discuss how to analyse data for which the assumption of independence is violated. In this course, you will learn all about it!

This is a refresher course aimed at PhD candidates. The level is that of a second course in Statistics. 

Classical statistics offers a powerful toolbox for data analysis. This toolbox, however, may not always be sufficiently flexible for modern data situations. 

This Geo-Information Science course is being redeveloped. Expect further information in December 2025.

The course will focus on Concepts, such as: wicked problems, socio-ecological systems, complex systems, participatory modelling, simulation strategies.

This course is primarily aimed at participants who are in the start-up of a modelling project. We purposefully aim to involve people from different scientific backgrounds. The emphasis is not on the mathematical, computational, and statistical aspects of modelling per se, but on the elements of the modelling process before that. You will learn about the scoping of a model, and to think critically about the choices in modelling.

This course offers the basic theories in the field of remote sensing, starting from the information needs of various land applications. 

This course will provide the participants with an overview of a range of methods related to the five soil functions and will provide detailed practical training in a subset of measures. The training will be a combination of lectures, laboratory and field sessions (interactive lectures and practical sessions each day). Assessing a range of measurement types, from simple visual assessments in the field, to training in microscope identification techniques for nematodes and earthworms, and functional measures in the lab such as MicroResp. All methods described in the course will be made available to participants as well as advice on how to analyse the data.

FAIR stands for “Findable, Accessible, Interoperable, Reusable” and is becoming increasingly important for sharing data, especially in research. We discuss the incentives and best practices of FAIR data management. 

Due to various causes errors can propagate through environmental models. Although users may be aware ofi t,they rarely pay attention to this problem.

This two-day workshop is aimed at providing a basic understanding of creating and evaluating de novo assembly using long read technologies. 

The aim of the course is to help experienced R users to tackle the problems they face when analysing big data sets. The course will consist of a mixture of lectures and computer labs (in a ratio of approximately 60/40), so there is plenty of time for hands-on exercises.

The aim of this course is to introduce participants to the Shiny ecosystem and show them how to approach data exploration, storytelling and communication by going beyond static reports and graphs, and create engaging and interactive data stories.

This course provides an introduction to modelling. Modelling concepts will be dealt with in detail, going through the basic steps to be taken. 

This online course consists of 5 modules representing a total of approximately 40 hours of work. Each module consists of video files with short theory presentations, followed by exercises using real data sets, and video files discussing the solutions. 

The purpose of this course is to make the participants familiar with general statistical concepts describing uncertainty, and methods to compute prediction uncertainty and sensitivity coming from uncertain parameter values. 

This course presents a conceptual framework for ecological modelling: covering elementary growth models and probability distributions needed to mathematically model processes. The models are confronted with the data, using state of the art statistical methods. 

This course aims to provide PhD candidates with a solid background in standard and more advanced geostatistical methods, such that they can apply these in their own research. The course is a mix of theory and practice, with case studies that are analysed using R and contributed geostatistical packages.