Survival Data Mining: A Programming Approach

Course code: BMCE42

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.

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.

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 14 hours

Language: en

Price without VAT: 720 EUR


Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR


Type Course
Language Price without VAT
Upon request E-learning 14 hours en 720 EUR Register
Upon request Upon request 14 hours en 1 200 EUR Register
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Target group

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

Course structure

Survival Data Mining

  • introduction to survival data mining
  • elements of survival analysis
  • time-dependent covariates

Survival Models (Self-Study)

  • semi-parametric survival models
  • parametric survival models
  • discrete-time survival models

Flexible Hazard Modeling

  • building discrete time hazard models
  • grouped expanded data

Hazard Modeling with Big Data

  • outcome-dependent sampling
  • data truncation
  • piecewise constant hazards (self-study)

Predictive Performance

  • predictive scoring
  • empirical validation

Recurrent Events

  • introduction to recurrent events


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
  • be familiar with SQL (including topics such as sub-queries and left-joining)
  • have SAS programming proficiency.
  • Do you need advice or a tailor-made course?


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