The top goal for me as a teacher is to establish a classroom atmosphere in which students feel free to expose their thinking process without fear of being judged
Overview of highly computational modern statistical learning methods; applications of logistic regression, ridge regression, LASSO, decision trees, random forests, KNN, k-means and hierarchical clustering, bootstrapping, cross validation, etc., to finance and marketing data. Extensive computer applications using R.
Leveraging large corporate datasets; slice and dice data; dash boards; data mining and statistical tools; multiple and logistic regression; decision tree; gain inference and decision making; k-means and hierarchical clustering. Extensive computer applications using Excel and JMP.
BUAD 310: Applied Business Statistics
Statistical methods for business analysis; data exploration and description; sampling distributions; estimation; hypothesis testing, simple and multiple regression; model building.