Prerequisite: See below
This course can also be delivered using R.
In this hands-on class, your team will learn the fundamentals of applying logistic regression to business data. Logistic regression is the most common predictive model used to answer business questions like, “how likely is a customer to churn?”
After learning about the types of business problems that might benefit from logistic regression analyses, your team will learn how logistic regression can answer interesting business questions like:
- Given a customer’s behavior, how likely are they to convert to paying?
- What factors are most important in determining if a customer will churn?
- Does the interaction of certain product characteristics affect the likelihood of a warranty claim?
If your team needs to level up their analytics skills to perform logistic regression or needs to understand the logistic regression models produced by data science teams, this is the class for you.
Want to know the best part? No difficult programming or mathematics is required!
Your Team Will Learn
- The type of business problems where logistic regression can be useful
- The basics of probabilities and odds ratios
- Building simple logistic regression models
- Building multiple logistic regression models
- Interpreting logistic regression models in terms of business drivers
- Evaluating the effectiveness of your logistic regression models
- How to communicate your insights effectively
- The “gotchas” of logistic regression
- Additional resources to extend your learning
- Business and data analysts
- BI and analytics developers and managers
- Business users
- Data scientists
- Anyone interested in using logistic regression for analyzing business data
No background in advanced mathematics or statistics is required.
Students must be familiar with Python and Jupyter notebooks or complete the pre-recorded course “Python Quick Start” prior to the class. This pre-recorded course will be made available in advance to any students who need it.
Attendees will need a laptop computer with specific software installed before the session. In advance of the class, attendees will receive detailed software download and installation instructions.