sponsored by Helmholtz Information & Data Science Academy (HIDA)
in cooperation with Core Facility Statistical Consulting at Helmholtz Zentrum München - German Research Center for Environmental Health (Helmholtz Munich)
Multivariate Statistics 1
In the “Multivariate Statistics 1” 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. The course also covers some rotation techniques after dimensionality reduction as well as mixture models, heatmaps, and other culstering methods (DBSCan , Louvain). The course will help understand the basis of the theory when doing a multivariate analysis. All topics are accompanied by 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, e.g. course “Introduction to R” and basic knowledge of statistics, e.g. course “Introduction to Statistics”. Some practice in ggplot2 is also welcome (e.g. course “Graphics with R”).
More details here.
Course Days and Time
Apr, 11 - 12, 2024, 9 am - 12.30 pm
Apr, 18 - 19, 2024, 9 am - 12.30 pm
Apr, 25 - 26, 2024, 9 am - 12.30 pm
NOTE: Registration will open March 14th, 2024, 12pm.
Additional Information
The course content is coordinated, so we strongly recommend that you do not miss any part of the course. To receive a certificate we expect full time and active participation.
Your registration for this course is binding. If you need to leave/miss the course for a period of time, please let us know in advance.
There is no waiting list for this course! If someone withdraws from a course, their place is automatically reopened. We therefore advise you to keep an eye on the registration in case the course is full and you would like to attend. Also, this course will be offered again in the future - you can check our HIDA Course Catalog for updates.
This course is free of charge.
