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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.

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Factor Analysis

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.

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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.

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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.

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Advanced Multivariate Analysis

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.

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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.

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