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This module offers an easy introduction to R programming. Learn the basics of R programming and the commonly used plots and statistical tools without pain.

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.

Learn how to take data (consumers, genes, stores, ...) 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. Cluster analysis comprises a collection of powerful techniques. Learn about this very active field of research in statistics and data mining, and discover new techniques.

Learn about key biostatistical concepts and efficient tools for summarising and plotting data as well as outlier detection. Demystify the statistical testing approach used to make decision in the presence of uncertainty: p-values, power, and so on.