In the world of data science, we have so many (almost too many) options available to us when we are trying to solve a problem.

Much of the time, people tend to get caught up in the “deep” this and the “Bayesian” that and forget about having a foundational process to rely on that is required to solve many problems in data science. In the most general sense, data science problems can be broken into two different camps: 1) A problem that requires the exploration of a dataset, and 2) A problem that has a specific task, but an undefined dataset or starting point.

Source: pindropsecurity.com
Author: Scott W. Strong