Survival Data Mining: A Programming Approach
Business Knowledge Series course
Presented by Chip Wells, Ph.D., Manager, Analytical Education, Education Division, SAS; or Bob Lucas, Ph.D., Analytical Training Consultant, Education Division, SAS; or Mike Patetta, Analytical Training Consultant, Education Division, SAS
This advanced course covers predictive hazard modeling for customer history data. Designed for data analysts, the course uses SAS/STAT software to illustrate various survival data mining methods and their practical implementation.
Note: Formerly titled Survival Data Mining: Predictive Hazard Modeling for Customer History Data, this course now includes hands-on exercises so that you can practice the techniques that you learn. Other additions include a chapter on recurrent events, new features in SAS/STAT software, and an expanded section that compares discrete time approach versus the continuous time models such as Cox Proportional Hazards models and fully parametric models such as Weibull.Learn how to
Who should attendPredictive modelers, data analysts, statisticians, econometricians, model validators, and data scientists
Before attending this course, you should
Many of the SAS examples use DATA step, macro, and SQL programming. The Predictive Modeling Using Logistic Regression and Survival Analysis Using the Proportional Hazards Model courses provide relevant background information. Prior attendance in these courses is advantageous but not required.
This course addresses SAS/STAT software.
Survival Data Mining