One of the reactions I’ve gotten to the argument behind my last post is that it’s unrealistic to think we can smooth data science’s learning curve. When you get beyond very simple point and click, you’ve got to immerse yourself in the dirty details of how statistics, machine learning, etc. work. In other words, we can’t really make data science accessible because the body of knowledge you need to go beyond baby steps is just too large.
When I first ran into this argument, I would reply with stories about the skilled practitioners in the field I’ve worked with who’ve forgotten a lot of what they learned in, say, intro stats – couldn’t perform a chi-square test by hand if their life depended on it – but still produce very powerful, highly influential work. These days my answer is a lot simpler.
Let’s have a show of hands of everyone who has relatives or friends who are amazing cooks. Now keep your hand up if most of those amazing cooks know the chemistry and physics behind what they do. Not a whole lot of hands left up.
It’s not that these amazing cooks don’t have any of the knowledge that’s embodied in chemistry and physics. They know a lot about how to work with boiling water, how you know when something they’ve been frying is done, etc. But the model they have in their head – or “in their fingers” – isn’t the one you get in chemistry class.
I think Data Chefs is going to end up demonstrating that’s also true for data science: you don’t need to be a Data Chemist to bake great data cookies. I don’t have any concrete empirical data to back me up. But neither do the people who are saying it can’t be done. All we know for sure is that that’s not how it’s been taught in the past. And if the data-driven revolution has taught us anything, you wouldn’t want to build the foundation of data science training on “but that’s the way it’s always been done.”