  Data scientists and statisticians are often at odds when determining the best
approach, machine learning or statistical modeling, to solve an analytics
challenge. However, machine learning and statistical modeling are more cousins
than adversaries on different sides of an analysis battleground. Choosing
between the two approaches or in some cases using both is based on the problem
to be solved and outcomes required as well as the data available for use and
circumstances of the analysis. Machine learning and statistical modeling are
complementary, based on similar mathematical principles, but simply using
different tools in an overall analytics knowledge base. Determining the
predominant approach should be based on the problem to be solved as well as
empirical evidence, such as size and completeness of the data, number of
variables, assumptions or lack thereof, and expected outcomes such as
predictions or causality. Good analysts and data scientists should be well
versed in both techniques and their proper application, thereby using the right
tool for the right project to achieve the desired results.
