We offer a variety of training modules that can be assembled and customised to develop your own tailored program.

We invite you to browse our catalog. You can have a look at sessions specifically designed for a field of application by clicking on the appropriate field below.


Learn how to address issues commonly encountered in sensory evaluation. Study design & analysis of descriptive, discriminative & consumer test data are covered.
Learn about Design of Experiments to control variation inherent to every experiment, as well as the analysis of designed experiments. Courses are aimed at scientists, laboratory staff & more broadly people involved in discovery processes.
Harness the power of R, a free software environment for statistical computing and graphics. Learn how to use R for data visualisation, analysis & modelling. Learn how to master the basics of this open source language.
Learn good practices for successful predictive model-building. Whether you work in R&D, finance or marketing, you will find this module valuable. Real-life applications are used to illustrate the process.
Learn about multivariate methods and their insightful graphical data summaries. Discover popular and others less known, some classical as well as more recent ones with focus on real-life applications.
Learn about classic and recent modelling technique to relate different types of variables in an explanatory or predictive perspective and model performance measures.
Discover statistical tools for making good strategic decisions in the presence of uncertainty. Real-life business & marketing applications, such as segmentation and mapping, are covered.
Designed for scientists & MSLs,  our training sessions are designed to debunk key biostatistical concepts and increase critical reading skills in the literature. Distance learning available.
Designed for engineers, learn how to control variation present in every engineering situation. Learn about design of experiments (DOE) and statistical data analysis tools.
Uncover the potential of DOE techniques to control variation inherent to every experiment. Learn about factorial, screening, optimisation designs as well as statistical tools for their analysis.
Discover the potential of multivariate & data mining tools for data mapping and visualisation.