Syllabus
Semester structure:
Week #1: Course information + Introduction to Data Science
Week #2-#4: R basics (ZM 1, G 3-5)
Week #5-6: Loading data, cleaning data, sampling (ZM 2-4)
Week #7: Model evaluation (ZM 5)
Week #8-#9: Memorization methods (ZM 6)
Week #10-#12: Advanced regression methods (linear, logistic, GAMs, LASSO, ridge) (ZM 7, T4-5)
Annotation
Introductory course to Data Science with applications in the R programming environment. Special focus is put on understanding of basic practical programming in R, covering model evaluation, memorization methods, advanced regression techniques, and training variance reduction.
The Data Science with R I course will be followed by Data Science with R II covering clustering, text mining, support vector machines, neural networks, and networks.