sponsored by Helmholtz Information & Data Science Academy (HIDA)
In the “Multivariate Statistics” course you will learn when and how to apply unsupervised learning methods such as PCA, MDS, t-SNE or UMAP for dimension reduction and k-means, hierarchical clustering or some hybrid approaches for clustering. The course also covers two supervised learning methods, namely principal component regression and partial least squares regression. The course will help to understand the basis of the theory when doing a multivariate analysis.
Prerequisites: Programming skills with R, e.g. course “Introduction to R” and basic knowledge of statistics, e.g. course “Introduction to Statistics”. Some practice in ggplot2 is also welcome.
More details here.