Multivariate Statistics 1
Abstract:
In this course you will learn when and how to apply unsupervised learning methods such as PCA for dimension reduction and k-means, hierarchical clustering or some hybrid approaches for clustering. It also covers some rotation techniques after dimensionality reduction as well as well as mixture models and heatmaps (clustering).
The course will help to understand the basis of the theory when doing a multivariate analysis. All topics are accompanied with hands-on exercises using the statistical software R. The participants are invited to ask as many questions as they want about the analyses on their own dataset.
Prerequisites: Programming skills with R
Find more information here!
Course Days and Times
- March 16, 2023, 9 am - 5 pm
- March 17, 2023, 9 am - 12:30 pm
- March 23, 2023, 9 am - 5 pm
- March 24, 2023, 9 am - 12:30 pm
In cooperation with Core Facility Statistical Consulting at Helmholtz Munich.
