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.
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.
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.
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.