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.
- build models for time-dependent outcomes derived from customer event histories
- account for competing risks, time-dependent covariates, right censoring, and left truncation
- handle large data sets
- compute the expected value of the remaining time until an event
- evaluate the predictive performance of the model.
Who Can Benefit
- Predictive modelers, data analysts, statisticians, econometricians, model validators, and data scientists
- Before attending this course, you should
- have a basic understanding of survival analysis
- have experience with predictive modeling, particularly with logistic regression
- be familiar with statistical concepts such as random variables, probability distributions, and parameter estimation
- be familiar with SQL (including topics such as sub-queries and left-joining)
- have SAS programming proficiency.