⚠️ A valid COVID certificate must be presented on site to enter the event. ⚠️

Predicting customer churn, planning for product returns or answering other time-to-event questions with one generalized framework: an introduction to survival analysis in Python

For how long is a customer going to use a company’s service? How many product returns should the quality team expect next year? For how many days are hospital beds occupied? These are all time-to-event problems with great economical and efficiency impact for their respective sectors. This workshop will introduce participants to survival analysis, a technique originally used in medicine and now employed for analyzing the expected duration of time until any kind of event happens. 
During the workshop, participants will be introduced to the basic concepts behind survival analysis. They will then learn how to use them hands-on by applying different algorithms on data relevant to practical use case, then evaluating them and extracting meaningful insights using Python.

By the end of the workshop, participants will learn:

• What kind of problems are suitable for survival analysis and its differences with respect to classical regression models
• The basic concepts behind it, such as the survival and hazard functions, censoring and the concordance index,
• How to apply this on real data with python packages, 
• What are the appropriate models for different cases and how to interpret them
• How to make relevant predictions.

Basic programming knowledge of Python is recommended, as well as some familiarity with machine learning concepts. Participants need to have a laptop with wireless internet access. Details on how to set up the project environment will be provided to the participants before the workshop.