Syllabus
All online videos (lectures and coding) via Loom (respecting the planned scheduled): link will come.
BLOCK I - Supervised learning: Advanced methods (Weeks 1-4)
Bagging and random forests (ZM 9)
Kernel methods and support vector machines (ZM 9, T 10, L 14)
Neural networks (T 10, L 14)
BLOCK II - Unsupervised learning (Weeks 5-9)
Clustering (T1, ZM 8, L 15)
Association rules (ZM 8, L 16)
Principal component analysis and principal regression (L 17-18)
BLOCK III - Text mining (Weeks 10-11)
Text mining (T 2-3, L 19)
BLOCK IV - Network analysis (Weeks 12-13)
Network analysis (L 20)
Annotation
Data Science with applications in R course covering the advanced topics and following the Data Science with R I course. Data Science with R II covers clustering, text mining, support vector machines, neural networks, and networks.
The main aim of the course is to train students to be able to properly analyze specific datasets with methods outside of standard econometric framework using the R programming environment.