Data Science for Everyone
Analysts, leaders, and other business professionals often see statistical modeling and advanced analytics as confusing and complex. In fact, many will not even attempt to understand them. They see data science as something beyond their grasp — a world for Ph.D.s or people with rarified abilities. This should not be the case.
I often remind people that you don’t have to be a mechanic or an engineer to drive a car. In the same way, you don’t have to be a statistician or a data scientist to use statistical modeling — or at least to understand what a model is telling you about your data. Just as you don’t have to know the torque ratio of your transmission gears to drive your car to the grocery store, you don’t have to know the complex math and theories underlying every statistical algorithm.
How to Understand an Algorithm
There are three keys to understanding statistical modeling and machine learning in a practical manner:
- Know what a particular model (or test) tells you (and what it does not)
- Know when to use that particular test
- Find a practical example of how the technique can be used in real life
Let’s take a popular statistical machine learning algorithm with a scary name — binomial logistic regression — and break it down so any business professional can understand what it does and how to apply it in the real world.
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