Survival Data Mining: A Programming Approach
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. 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.
Virtual Training nebo e-Learning?
Máme dostatečnou flexibilitu, takže vybírat můžete jak prezenční termíny, tak online kurzy.
Zkuste živý kurz virtuálněTarget 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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Jsme vám k dispozici na telefonním čísle +420 222 553 101 vždy od pondělí do pátku: 9:00 - 17:00.
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