What is data science?

We’re about to talk about the interface between you and your data science team. I thought we’d take a brief pause and just talk about data science and get a working view of what it means before we move on to that. I have this Venn diagram that I’m going to walk you through. It’s created by a guy named Drew Conway who is a thought leader in the space. And the first part of the data science discipline is substantive expertise so this would be kind of your thing as the product manager, your input into the practice of data science. And, then we have this intersection between hacking skills, meaning the ability to program and create software, and substantive expertise.

The idea is that this is a danger zone because you want to make sure that you’re also applying a knowledge of math and statistics just to make sure that the inference is that you’re creating are actually statistically valid and reasonable

then you need to know enough of this to do that. And then a lot of people will ask, well, what’s difference between all this hubbub about data science and the kind of statistics work that I’ve been doing for the last 20 years. And the idea is math and statistics and substantive expertise, that’s kind of what we mean by traditional research. Data science is at the intersection of these three things.

So Machine Learning which is where we allow actual software to have this sort of artificial intelligence, where it’s able to apply statistical models to data and come up with its own inferences that people can look at and these, these two other things and here we have this new discipline of data science. This is a simple view of what’s meant by data science and it’s a really big deal for the product manager. If you use either Google Photos or Google Analytics, you may have noticed this assistant feature.

In Google photos, it will go and do things like suggest that you look back on this day last year or pick similar photos and create a mosaic for you to look at or even put those together to music and create a video. And in Google Analytics, which is, which is what you’re seeing here, this will tell you key statistics about things that are happening with your website that you may want to look into and act on.

Now, these are things that, not being an expert on data science, I am told that they’re relatively trivial from a machine intelligence and a data science standpoint but these are a huge deal from a product design standpoint because these are now interfaces that are machine generated and what the user is seeing is not something that a product manager exactly and a bunch of developers deterministically set. “Okay. We’re going to create this experience. Everybody is going to have it.” But rather there is a machine intelligence, it’s saying, “all right, let’s try this,” with the user. “Let’s try that. Let’s work to make that even better.”

So, it’s the first kind of, it is an example of the sort of early interaction that the average end user is having with data science and machine intelligence. So, we talked a little bit about what do we mean what is data science. Now we’re going to talk about your interface with the data science team, the data science resources that you may have available to you.