Survival Analysis in Python
Use survival analysis to work with time-to-event data and predict survival time.Start Course for Free
4 Hours16 Videos48 Exercises3,068 Learners3850 XP
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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.
Introduction to Survival AnalysisFree
What 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.What is survival analysis?50 xpWhat problems does survival analysis solve?100 xpChoose the right data for survival analysis50 xpWhy use survival analysis?50 xpIdentify the censorship type50 xpPreprocess censored data100 xpFirst look at censored data100 xpYour first survival curve!50 xpDraw a survival curve100 xpLong live democracy!100 xp
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.Fitting a Kaplan-Meier estimator50 xpHow to fit a Kaplan-Meier estimator?100 xpHeart disease patient survival100 xpVisualizing your Kaplan-Meier model50 xpPlotting the survival curve50 xpPatient soreness treatment100 xpApplying survival analysis to groups50 xpSenators' terms in office100 xpComparing patient soreness treatments100 xpThe log-rank test50 xpAppropriate data for log-rank test100 xpPlotting and comparing survival curves100 xpLog-rank test100 xp
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.Fitting the Weibull model50 xpModel prison data with Weibull100 xpCompare Weibull model parameters100 xpWeibull model with covariates50 xpAnalyze heart patients characteristics100 xpExplore gender-LVDD interaction100 xpVisualization and prediction with Weibull model50 xpHow do prior arrests impact re-arrest rate?100 xpPredict re-arrest rate100 xpOther distributions and model selection50 xpHow good is the fit?100 xpChoose a parametric model100 xp
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.Fitting the Cox Proportional Hazards model50 xpModel prison data with Cox PH100 xpCustom Cox PH model100 xpInterpreting the Cox PH model50 xpCox PH model survival time100 xpPlot covariate effects on survival100 xpThe proportional hazards assumption50 xpTest the PH assumption with KM curves100 xpTest the PH assumption automatically100 xpPredicting with the Cox PH model50 xpEmployee churn study100 xpPredict before they leave!100 xpCongratulations!50 xp
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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