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. During the workshop, emphasis is put on the principles and the conditions of utilization of the method, the results they provide and their interpretation. Plenty of time is devoted to case studies and interpretation of software output.
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. Learn about innovative applications. During the workshop, emphasis is put on the principles and the conditions of utilization of the method, the results they provide and their interpretation. Plenty of time is devoted to case studies and interpretation of software output.
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.
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.