A Gentle Introduction to Survival Models with Applications in Python and R
A Gentle Introduction to Survival Models with Applications in Python and R


Survival/duration models are common ways to model the probability to fail/survive at each period in your data set. Though they are common in certain fields in economics, econometrics and biology, they are less commonly applied in data science, despite them often being the most appropriate approach to a problem.

This workshop will start with a theoretical introduction on basic non-parametric, semi-parametric and parametric models such as Kaplan-Meier, Cox Proportional Hazard (with and without time-varying covariates), and Aalen additive model, and random survival forests. In the second part of the workshop, we will look at how we can apply these models in Python and R.


Violeta has been working as a data scientist in the Data Innovation and Analytics department in ABN AMRO bank located in Amsterdam, the Netherlands.In her daily job, she works on projects with different business lines applying latest machine learning and advanced analytics technologies and algorithms. Before that, she worked for about 1.5 years as a data science consultant in Accenture, the Netherlands. Violeta enjoyed helping clients solve their problems with the use of data and data science but wanted to be able to develop more sophisticated tools, therefore the switch.

Before her position at Accenture, she worked on her PhD, which she obtained from Erasmus University, Rotterdam in the area of Applied Microeconometrics.In her research she used data to investigate the causal effect of negative experiences on human capital, education, problematic behavior and crime commitment.

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