Non-Mathematical Feature Engineering techniques for Data Science

"Apply Machine Learning like the great engineer you are, not like the great Machine Learning expert you aren't." This is the first sentence in a Google-internal document I read about how to apply ML. In my limited experience working as a server/analytics guy, data (and how to store/process it) has always been the source of most consideration and impact on the overall pipeline. Ask any Kaggle winner, and they will always say that the biggest gains usually come from being smart about representing data, rather than using some sort of complex algorithm. Even the CRISP data mining process has not one, but two stages dedicated solely to data understanding and preparation.