Churn Analysis

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prerequisites
basic Python • basic PostgreSQL
skills learned
create a data set from raw data stored in a database • visualize how different customer metrics relate to the outcome of churn • analyze different customer behaviors
Carl Gold
2 weeks · 10-15 hours per week · INTERMEDIATE
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catalog / Data Science
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Look inside
In this liveProject, you’ll analyze a data set of customers metrics to uncover patterns and behaviors that indicate customer churn. You’ll determine key details like rate of retention, and analyze your data to determine which metrics are associated with customers leaving and customers staying loyal. You’ll analyze how customer behaviors are related to one another, and see if you can expand on your datasets functionality to find new metrics that indicate churn.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Carl Gold
Carl Gold is the senior data science manager for financial startup Migo.money. He has previously worked as chief data scientist for Zuora, the industry-leading subscription management platform. He has a PhD from the California Institute of Technology.

prerequisites

This liveProject is suitable for anyone with a basic background in programming and data analysis, who wants to really make a difference to a business's churn. To begin this liveProject you will need to be familiar with:

TOOLS
  • Basic Python
  • Basic PostgreSQL

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