Syllabus
Week #1-#2: Course information + R basics (ZM 1, G 3-5)
Week #3: Loading data, cleaning data, sampling (ZM 2-4)
Week #4: Model evaluation (ZM 5)
Week #4-5: Memorization methods (ZM 6)
Week #6: Correlations, linear and logistic regressions and beyond (ZM 7, T4-5)
Week #7: Clustering (T1, ZM 8)
Week #8-#9: Data and text mining sequences (T 2-3)
Week #10: Reducing training variance & Generalized additive models (ZM 9)
Week #11: Machine learning techniques (ZM 9, T 10-12)
Week #12: aLook Analytics presentation
Annotation
Introductory course to Data Science with applications in the R programming environment. Special focus is put on data visualization, data & text mining, and machine learning methods.