On Moving from Statistics to Machine Learning, the Final Stage of Grief

I've spent the last few months preparing for and applying for data science jobs. It's possible the data science world may reject me and my lack of both experience and a credential above a bachelors degree, in which case I'll do something else. Regardless of what lies in store for my future, I think I've gotten a good grasp of the mindset underlying machine learning and how it differs from traditional statistics, so I thought I'd write about it for those who have a similar background to me considering a similar move.1 This post is geared toward people who are excellent at statistics but don't really "get" machine learning and want to understand the gist of it in about 15 minutes of reading. If you have a traditional academic stats backgrounds (be it econometrics, biostatistics, psychometrics, etc.), there are two good reasons to learn more about data science: The world of data science is, in many ways, hiding in plain sight from the more academically-minded quantitative disciplines.