ADMN 898 (2ON) - Top/Predictive Analytics

Durham   Paul College of Business&Econ :: Administration
Credits: 3.0
Term: Fall 2018 - E-term II (10/15/2018 - 12/11/2018)
Grade Mode: Letter Grading
Class Size:   40  
CRN: 16654
Special topics; may be repeated. Prereq: consent of adviser and instructor.
Section Comments: Full Title: Topics/Integrated Marketing Communication.
Only listed majors in section: ANALYT DS CERT, ANALYTICS, ANALYTICS CERT, ANALYTICS PP, BUSINESS ADM M, BUSINESS ADM OL, BUSINESS ADM PT, BUSINESS ADMIN
Attributes: Online (no campus visits), EUNH
Instructors: STAFF

Times & Locations

Start Date End Date Days Time Location
10/15/2018 12/11/2018 Hours Arranged ONLINE
Additional Course Details: 

This course will introduce modern predictive and learning analytics techniques. The main emphasis will be on the applied aspects of these techniques with programming in the R language (an open source software that has gained tremendous popularity recently). Each lecture is designed to introduce new methods followed by real data applications from various applied fields (marketing, operations, finance, economics, and sports analytics). In introducing these predictive analytics tools, the course will feature discussions on four broadly defined areas of focus: 1) finding the most appropriate model that best represents the data, 2) selecting the optimal set of predictors, 3) reducing the dimension of data and dealing with correlated predictors, 4) improving prediction performance. A summary of topics that will be covered in the course is as follows: linear and non-linear regression analysis (ridge, Lasso, K-nearest neighbor, non-linear splines, neural networks), classification methods (logistic regression, linear discriminant analysis, support vector machines), tree based methods (regression/classification trees, bagging, boosting, random forests), unsupervised learning methods (principle components analysis, k-means clustering, hierarchical clustering).