Participants are expected to have knowledge of basic statistics, e.g. hypothesis testing, correlation and linear regression, and experience using R and RStudio.

The scope of implemented topics is:

• Data pre-treatment (e.g. centering, autoscaling, pareto scaling and range scaling), data exploration using Principal Component Analysis (PCA) and regression using the principal components from PCA in Principal Component Regression, PCR.
• Modern regression techniques (e.g. Partial Least Squares and regularized regression (ridge/lasso)) and model validation.
• Clustering and classification; k-means, hierarchical clustering, LDA and PLS-DA Discussion of cluster analysis: choice of similarity measure, agglomerative methods, divisive methods, k-means & hierarchical clustering.

Each day consists of lectures in the morning and practicals using R in the afternoon.