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.

720 EUR

871 EUR including VAT

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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

	Starting date	Type	Course duration	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

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

be familiar with SQL (including topics such as sub-queries and left-joining)

have SAS programming proficiency.

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