Business Knowledge Series course
Presented by Bob Lucas, Ph.D., Director, Analytical Education, Education Division, SAS
This advanced course identifies the benefits and pitfalls of using survival analysis for business intelligence. Designed for data analysts, it covers both theoretical justification of various survival data mining methods and their practical implementation using SAS software.
Learn how to
- 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 should attend
Predictive modelers, data analysts, and statisticians
| Classroom:|| 3.0 days |
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.
Many of the SAS examples use DATA step, macro, and SQL programming. Modeling methods are implemented using SAS/STAT procedures and SAS Enterprise Miner. The Predictive Modeling Using Logistic Regression
and Neural Network Modeling
courses provide relevant background information. Prior attendance in these courses is advantageous but not required.
This course addresses SAS Enterprise Miner, SAS/STAT software.