Multivariate Statistics

Course description

Requirements: Programming skills with R, e.g. course Introduction to R and basic knowledge of statistics, e.g. course Introduction to Statistics. Eventually some practice in ggplot2, that can be achieved in the course Advanced Graphics with R (not mandatory).

Your profit: The participants will learn when and how to apply unsupervised learning methods such as PCA or k-means with R. The course will also help to understand the basis of the theory when doing a multivariate analysis. All topics are accompanied with hands-on exercises.

Topics: This course on multivariate statistics covers two different topics:

  • Dimension reduction methods: This first chapter focuses more on Principal Component Analysis (PCA), what is “under the hood” and how to visualize and interpret the results. A short overview on other multivariate methods (e.g. for data structured into groups) is also part of the lecture. Finally, this chapter includes an extension part on dimension reduction for omics data (t-SNE, UMAP).
  • Cluster analysis: This second chapter focuses on the two most frequently used clustering methods: k-means and hierarchical clustering (HC). It describes the different measures of dissimilarity and distances that can be used to define clusters. A short part also illustrates how to combine both algorithms (k-means and HC) into hybrid algorithms. Finally, this chapter covers the R commands that permit to produce heatmaps together with the result of a clustering algorithm.

Methods: Each day consists of blocks covering first the theory behind the methods and their application in R, and then hands-on examples with best-practice solutions. 

Duration: 2 Days

Language: English


  • Material for the course can be found here (only for HMGU staff).
  • Please be aware that the materials will be updated shortly before the next course.

Dates and Application: You can check the current dates and whether the courses are already fully booked here.
Please apply via the forms of the HR Development department

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