## The Multivariate Analysis of Sensory Data

An applied set of modules with focus on the most widely used multivariate methods and their applications in sensory evaluation. Learn about the principle of the methods, the data needed, and the information they provide.

## Fundamentals Statistical Tools for Research

An easy intro to key statistical concepts. Learn about data visualisation & outlier detection. Demystify statistical significance testing – p-values, significance, power, confidence, etc. An excellent introductory module and a solid basis for all other courses.

## Market Research – Mapping Tools

Discover powerful mapping techniques to explore & understand the underlying preference structure of consumers. Learn how to create and interpret efficient and insightful graphical data summaries.

## Linear Regression Modelling Techniques

Building a regression model with stats packages has become straightforward. However, interpreting the software output and building a good are no simple tasks. Learn the essentials of model-building, goodness-of-fit tools & common pitfalls.

## Generalised Procrustes Analysis

Discover a powerful multivariate techniqueÂ for mapping the consensus among assessors rating series of products/concepts, and for quantifying and mapping the redundancy in sensory descriptors.

## Cluster Analysis

Learn how to take data (consumers, genes, …) and organise them into homogeneous groups for use in many applications, such as market analysis and biomedical data analysis, or as a pre-processing step for many data mining tasks. Learn about this very active field of research in statistics and data mining, and discover new techniques.

## Discriminant Analysis

The primary goal of this method is to discover which variables have the best ability of discriminating between two or more known groups in your data. Discriminant analysis may also be used to build predictive analytics models.

## Correspondence Analysis

Conceptually similar to PCA, correspondence analysis a method is designed for discovering associations in categorical rather than continuous data. Discover informative 2D-plots for efficient data mapping.

## Principal Component Analysis

Learn about Principal Component Analysis, a data reduction technique, to identify, quantify & visualise the structure of a set of measurements. PCA provides insightful data visualisation tools. Learn about innovative applications.