Survival Analysis in Python
Use survival analysis to work with time-to-event data and predict survival time.
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Course Description
How long does it take for flu symptoms to show after exposure? And what if you don't know when people caught the virus? Do salary and work-life balance influence the speed of employee turnover? Lots of real-life challenges require survival analysis to robustly estimate the time until an event to help us draw insights from time-to-event distributions. This course introduces you to the basic concepts of survival analysis. Through hands-on practice, you’ll learn how to compute, visualize, interpret, and compare survival curves using Kaplan-Meier, Weibull, and Cox PH models. By the end of this course, you’ll be able to model survival distributions, build pretty plots of survival curves, and even predict survival durations.
- 1
Introduction to Survival Analysis
FreeWhat problems does survival analysis solve, and what is censorship? You’ll answer these questions as you explore survival analysis data, build survival curves, and make basic estimations of survival time. - 2
Survival Curve Estimation
In this chapter, you’ll learn how the Kaplan-Meier model works and how to fit, visualize, and interpret it. You’ll then apply this model to explore how categorical variables affect survival and learn how to supplement your analysis using hypothesis testing methods like the log-rank test. - 3
The Weibull Model
Discover how to model time-to-event data with parametric models. Learn how to use the Weibull model and the Weibull AFT model and what different purposes they serve. Use survival regression to make inferences about how covariates affect the survival function and learn how to select the best survival model for your data. - 4
The Cox PH Model
Another chapter, another model! In this final chapter, you'll learn about the proportional hazards assumption and the role it plays in fitting and interpreting the Cox Proportional Hazards model. You’ll also learn how to predict new subjects' survival times using the Cox Proportional Hazards model.
Maggie Matsui
Hadrien Lacroix
Shae Wang
Senior Data Scientist at Ripple
Shae Wang is a Senior Data Scientist at Ripple. She works on applied machine learning problems in the payments space, blockchain analytics, and experimentation. Before joining the blockchain industry, Shae was a Data Scientist at Uber and worked on a wide range of problems from OCR modeling and churn modeling to causal inference. Prior to her role at Uber, Shae used Neural Net to predict stock market movements for a startup. Shae earned her Masters in Computer Science with a focus in Machine Learning and Bachelor's degree in Statistics from Northwestern University.
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