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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.

Predictive analytics (PA) is on everyone's lips. But what is it really all about? Discover its principle, implementation, typical pitfalls and good practices. Learn about data wrangling and munging, a crucial step in predictive analytics. An overview of the most commonly used models is also presented.

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. Discrimimant analysis may also be used to build predictive analytics models.

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 and common pitfalls.

This module offers an overview of tree-based modeling techniques. Learn how they work, when to use them, their strengths and weaknesses, and their implementation including validation. Several applications are presented.

Classical linear regression is inappropriate when the predictors are correlated (multicollinearity). Learn the principe of PCR and PLS regression designed to deal with multicollinearity and when it is relevant to use them.

Linear regression is inappropriate to model binary responses such as pass/fail, alive/dead. Learn the principle of logistic regression, its similarities with linear regression and its specific tools. Good practices for model-building are presented.

Once influential factors are identified, the next goal consists of optimising their settings. This module covers the construction of experimental designs for optimisation.  Data modeling is carried out with response surface methodology.