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Survival Data Mining: A Programming Approach

Course Details
Tuition (USD): $1,200.00 • Classroom (1 day)

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.

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.

Skills Gained

  • 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.

Course Details

Survival Data

  • time-dependent outcomes derived from customer event histories
  • features of the event-time distribution such as competing risks, time-dependent covariates, censoring, and truncation
  • basic nonparametric estimation of the hazard and distribution functions

Flexible Parametric Hazard Models

  • multinomial logistic regression for right censored data
  • regression spline and neural network modeling
  • adaptations for large data sets

Modeling Current Status Data

  • simple models for reduced sample data
  • powerful models for cross-sectional data

Predictive Performance

  • predictive scoring
  • estimating the mean residual lifetime
  • empirical validation using concentration curves
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