The knowledge vs. faith conundrum has been a philosophical debate for eons, yet it usually is geared towards abstract matters, such as life after death. So, how does this apply to a pragmatic field such as data science? Well, contrary to what many people think, most data science practitioners often rely on faith to a great extent, when dealing with data science matters. But why is that?
Unfortunately, most people learning the craft have a strict time table to keep, so they don't have a chance to go in depth on the material covered. This increasingly severe temporal limitation is also coupled with other factors, such as the plethora of "cookbooks" on the topic. Not to be confused with actual cookbooks, comprising of various recipes, oftentimes original tried and tested dishes developed by experienced chefs; these cookbooks are fine and probably have a bigger bang for your buck, compared to the technical cookbooks that are basically a bunch of methods and functions, usually in a popular programming language, organized by someone who oftentimes doesn't even understand them. If you rely mainly on such sources of knowledge, you are basically putting your faith in these people and creating gaps in your understanding of the craft.
So, if you obtain technical knowledge quickly or from a source that doesn't go much in depth, it is unlikely to truly know data science. That's not to say that you shouldn't read books; far from it. Books are useful but no matter how good they are, the best way to learn something remains the empirical approach. Going under the hood of the methods involved, implementing methods from scratch and even experimenting with your own ideas, are all good ways to learn something in more depth and remember it for longer periods of time. Also, through empirical knowledge of the craft, you are more confident about what you know and oftentimes more aware of the boundaries of your knowledge.
There is room for faith in our field, as for example when you trust what your data science lead/director tells you, when you accept advice from a mentor, and when you rely on the know-how of an academic paper written by someone who knows data science in-depth. However, it's good to balance it with empirical knowledge to the extent your time allows. Perhaps in abstract matters, it's hard to obtain empirical knowledge, but on things that you can test yourself, the only limitations are man-made ones. Are you willing to transcend them?
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.