Business Knowledge Series course

Presented by Mike Patetta, Analytical Training Consultant, Education Division, SAS; or Chip Wells, Ph.D., Manager, Analytical Education, Education Division, SAS

This advanced course covers predictive hazard modeling for customer history data. Designed for analysts, the course uses SAS Enterprise Miner to illustrate survival data mining methods and their practical implementation.

The self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual lab time to practice.

• Build models for time-dependent outcomes derived from customer event histories.
• Account for competing risks, time-dependent covariates, right censoring, and left truncation.
• Compute the expected value of the remaining time until an event.
• Evaluate the predictive performance of the model.
• Score new customers.

Who should attend

Formats available	Standard duration
Classroom:	2.0 days
Live Web Classroom:	4 half-day session(s)	System Requirements
e-Learning:	14 hours/180 day	System Requirements

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.

SAS programming experience is helpful but not mandatory.

This course addresses SAS Enterprise Miner software.

• Introduction to survival data mining.
• Elements of survival analysis.
• SAS Enterprise Miner: fundamentals and exploratory analysis.
• Empirical hazard plots (self-study).

• Building discrete-time hazard models.
• Time-dependent covariates.
• Data expansion syntax (self-study).

• Outcome-dependent sampling.
• Data truncation.

• Predictive scoring.
• Empirical validation.