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)
https://www.exitcertified.com/it-training/sas/advanced-analytics/sas-data-mining/-51215-detail.htmlBMCE42Survival Data Mining: A Programming Approachhttps://assets.exitcertified.com/assets/CourseImages/532cb503ef/AdobeStock_189991385__FitMaxWzEwMDAsMTAwMF0.jpg1650.00USDInStock/Training/SAS/Advanced Analytics/Data Mining This advanced course discusses predictive hazard modeling for customer history data. Designed for data analysts, the course uses...1650.00SASClassroom2017-02-23T06:36:08+00:00USD