An applied set of modules with focus on the most widely used multivariate methods and their applications in several fields of application. Learn about the principle of the methods, the data needed, and the information they provide.
An applied set of modules with focus on the most widely used multivariate methods and their applications in several fields of application. Learn about the principle of the methods, the data needed, and the information they provide.
Several clustering methods. Learn about their principle, conditions of use, data preparation phases, common pitfalls as well as good practices. Several real life applications are presented.
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.
Learn about indicators designed to capture the desirable attributes of descriptive data collected by a trained sensory panel (agreement, repeatability and discrimination power). A suite of statistical tools are used to depict and quantify the performance of the panel.
Research question often require the use of a combination of multivariate data analysis techniques. This course covers advanced multivariate analysis applications for mapping purposes, the selection of representative items in groups, segmentation and prediction, and many more.
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.
Discover a powerful multivariate technique for mapping the consensus among assessors rating series of products/concepts, and for quantifying and mapping the redundancy in sensory descriptors.
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.
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.
One of the oldest multivariate techniques, factor analysis is closely related to PCA and even confused by many for PCA. However, it serves a totally different purpose. Uncover hidden dimensions in your data.
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.
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.