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Survival Data Mining: A Programming Approach

GK# 2821

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Course Overview

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This advanced course covers 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.

Schedule

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Outline

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Classroom Live Outline

1. Survival Data Mining

  • Introduction to survival data mining
  • Elements of survival analysis
  • Time-dependent covariates

2. Survival Models

  • Semi-parametric survival models
  • Parametric survival models
  • Discrete-time survival models

3. Flexible Hazard Modeling

  • Building discrete time hazard models
  • Grouped expanded data

4. Hazard Modeling with Big Data

  • Outcome-dependent sampling
  • Data truncation
  • Piecewise constant hazards

5. Predictive Performance

  • Predictive scoring
  • Empirical validation

6. Recurrent Events

  • Introduction to recurrent events

Labs

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Classroom Live Labs

Exercises or hands-on workshops are included with most SAS courses.

Who Should Attend

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  • Predictive modelers
  • Data analysts
  • Statisticians
  • Econometricians
  • Model validators
  • Data scientists
Course Delivery

This course is available in the following formats:

Classroom Live

Receive face-to-face instruction at one of our training center locations.

Duration: 3 day

Virtual Classroom Live

Experience expert-led online training from the convenience of your home, office or anywhere with an internet connection.

Duration: 6 day

Request this course in a different delivery format.
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