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. The structure of this courses allows for a personalized learning experience through a combination of instructor-led class time and structured self-study. The course consists of classroom instruction, digital course notes, case studies with solutions, virtual lab with software for practice, and a half-day Live Web session to discuss questions about the material during the course.
- one day in class to cover key topics
- case studies with solutions posted online
- half-day Live Web review session
- access to a SAS instructor for questions
- online forum for support.
- build models for time-dependent outcomes derived from customer event histories
- account for competing risks, time-dependent covariates, censoring, and truncation
- use techniques to model current status data and to 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 experience with predictive modeling, particularly with logistic regression
- be familiar with statistical concepts such as random variables, probability distributions, and parameter estimation
- be comfortable working with summation notation, vectors, matrices, and analytic geometry
- have SAS programming proficiency.