MGMT 362 - Business Data Analytics

2018-2019 Catalog Year

Credit(s): 3
Lecture: 3
Non-Lecture: 0
This course aims to provide an applied overview of business data analytics methods such as Generalized Additive Models, Decision Trees, Boosting, Bagging, Neural Networks and Support Vector Machines as well as more classical linear approaches such as Logistic Regression, and Nearest Neighbors. Computing is done in R, and students will focus on how the techniques covered can be applied to solving business problems.

Prerequisite(s): ECON 305/MGMT 305 and junior standing.
Offered: Every spring
Restricted to: School of Business majors