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Overview
Context
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 distributions.
This Survival Analysis in Python course offered by Data Camp 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.
Programme Structure
Chapters
- Survival Curve Estimation
- The Weibull Model
- The Cox PH Model
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Campus Location
- New York City, United States
Disciplines
Statistics View 109 other Short Courses in Statistics in United StatesWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
PREREQUISITES
- Introduction to Regression with statsmodels in Python
- Hypothesis Testing in Python
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
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International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
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Domestic
Applies to youIn-StateFree
Additional Details
This course can be accessed for free with the Data Camp Premium or Teams subscriptions
Funding
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