In the first part of this series, I discussed how important it is as a data scientist to ask the right questions when solving a new problem. In this post I will be digging into a specific example relating to the situation when a data scientist is presented with a prediction goal, but no data to support that goal.
Specifically, we will be looking at trying to predict the outcome of the NCAA Men’s National Basketball Tournament known as March Madness. But first, let’s review the key questions that should be answered when beginning any problem like this one.
Author: Scott W. Strong