History, applications and data mining principles. 15%
Data exploration. 10%
Classification and regression trees. 20%
Logistic regression. 15%
Model comparisons and scoring. 10%
Neural networks. 10%
Random forests and boosted trees. 10%
Comprehensive example. 10%