Survival Data Mining: A Programming Approach
Presentado por Chip Wells, Ph.D., Manager, Analytical Education, Education Division, SAS
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.Aprenda a
A quién va dirigidoPredictive 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.
Este curso utiliza SAS/STAT software.
Survival Data Mining
|Título||Duración||Periodo de Acceso||Idioma||Precio||Añadir Inscripción|
|Survival Data Mining: A Programming Approach (14.2) (PDF + virtual lab)||14.0 horas||180 días desde la fecha del pedido||English||870 EUR|