mathbabe

Yesterday Flowing Data posted an article about the history of data science (h/t Chris Wiggins). Turns out the field and the name were around at least as early as 2001, and statistician William Cleveland was all about planning it. He broke the field down into parts thus:

• Multidisciplinary Investigation (25%) — collaboration with subject areas
• Models and Methods for Data (20%) — more traditional applied statistics
• Computing with Data (15%) — hardware, software, and algorithms
• Pedagogy (15%) — how to teach the subject
• Tool Evaluation (5%) — keeping track of new tech
• Theory (20%) — the math behind the data

First of all this is a great list, and super prescient for the time. In fact it’s an even better description of data science than what’s actually happening.

The post mentions that we probably don’t see that much theory, but I’ve certainly seen my share of theory when I go to Meetups and such. Most of the time the theory is launched into straight away and I’m on my phone googling terms for half of the talk.

The post also mentions we don’t see much pedagogy, and here I strongly concur. By “pedagogy” I’m not talking about just teaching other people what you did or how you came up with a model, but rather how you thought about modeling and why you made the decisions you did, what the context was for those decisions and what the other options were (that you thought of). It’s more of a philosophy of modeling.

It’s not hard to pinpoint why we don’t get much in the way of philosophy. The field is teeming with super nerds who are focused on the very cool model they wrote and the very nerdy open source package they used, combined with some weird insight they gained as a physics Ph.D. student somewhere. It’s hard enough to sort out their terminology, never mind expecting a coherent explanation with broad context, explained vocabulary, and confessed pitfalls. The good news is that some of them are super smart and they share specific ideas and sometimes even code (yum).

In other words, most data scientists (who make cool models) think and talk at the level of 0.02 feet, whereas pedagogy is something you actually need to step back to see. I’m not saying that no attempt is ever made at this, but my experiences have been pretty bad. Even a simple, thoughtful comparison of how different fields (bayesian statisticians, machine learners, or finance quants) go about doing the same thing (like cleaning data, or removing outliers, or choosing a bayesian prior strength) would be useful, and would lead to insights like, why do these field do it this way whereas those fields do it that way? Is it because of the nature of the problems they are trying to solve?

A good pedagogical foundation for data science will allow us to not go down the same dead end roads as each other, not introduce the same biases in multiple models, and will make the entire field more efficient and better at communicating. If you know of a good reference for something like this, please tell me.