Survival Data Mining: A Programming Approach
This advanced course discusses 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.Lernen Sie, wie Sie / Learn how to
Zielgruppe / 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 Überlebenszeitanalyse mit dem proportionalen Hazard-Modell courses provide relevant background information. Prior attendance in these courses is advantageous but not required.
In diesem Kurs wird mit folgenden Software Modulen gearbeitet: SAS/STAT Software
Survival Data Mining