Friday October 29 3:30 PM – Friday October 29 5:00 PM in Workshop/Tutorial II

Computational Survival Analysis

Allen Downey

Prior knowledge:
Previous knowledge expected
No previous knowledge of survival analysis is required. Participants should know basic Python. Familiarity with Pandas is helpful, but not required.


Survival analysis is a powerful and versatile set of statistical tools that not enough people know about. They are usually presented mathematically, but in this tutorial we take a computational approach, using Pandas objects to represent survival functions and hazard functions, and to implement basic algorithms of survival analysis, like Kaplan-Meier estimation.


In this tutorial, participants learn the fundamental ideas of survival analysis -- including survival functions and hazard functions -- and implement basic algorithms like Kaplan-Meier estimation.

The tutorial consists of three case studies, each presented in a Jupyter notebook where the participants can read short explanations, run worked examples, and then work on exercises to practice what they learned.

The examples come from health, engineering, and social science, but these methods are applicable in other fields, including business analytics.