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Survival Data Mining Using SAS(R) Enterprise Miner(TM)

Course Details
Code: FBSDM
Tuition (USD): $1,200.00 • Classroom (2 days)
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 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, a virtual lab with software for practice, and a half-day Live Web session to discuss questions about the material during the course.

What's included?

  • 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

This course can also be scheduled as a Live Web class. For details, view the

Skills Gained

  • 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 Can Benefit

  • Predictive modelers, data analysts, statisticians, econometricians, model validators, and data scientists

Prerequisites

  • 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.

Course Details

Survival Data Mining

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

Flexible Hazard Modeling

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

Hazard Modeling with Big Data

  • Outcome-dependent sampling.
  • Data truncation.

Predictive Performance

  • Predictive scoring.
  • Empirical validation.
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