 When we talk about data science, and we're contrasting it with some fields, another field that a lot of people get confused and think they're the same thing as data science and statistics. Now, I'll tell you, there's a lot in common, but we can talk a little bit about the different focuses of each. And we also get into the issue of sort of definitionalism that data science is different because we define it differently, even when there's an awful lot in common between the two. It helps to take a look at some of the things that go on in each field. So let's start here about statistics, put little circle here and we'll put data science. And to borrow a term from Steven J. Gould, we can call these non overlapping magisteria noma. So you think of them as separate fields that are sovereign unto themselves with nothing to do with each other. But you know, that doesn't seem right. And part of that is if we go back to the data science Venn diagram, you know, statistics is one part of it, there it is in the top corner. So now what do we do? What's the relationship? So it doesn't make sense to say these are totally separate areas. Maybe data science and statistics because they share procedures. Maybe data science is a subset or a specialty of statistics more like this. But if data science were just a subset or specialty within statistics, then it would follow that all data scientists would first be statisticians. And interestingly, that's just not so. Say for instance, we take a look at the data science stars, the superstars in the field, we go to a rather intimidating article, it's called the world's seven most powerful data scientists from Forbes.com. And you can see the article if you go to this URL. There's actually more than seven people on the list, because sometimes he brings them up in pairs. But let's check their degrees, see what their academic training is in. If we take all the people on this list, we have five degrees in computer science, three in math, two in engineering, and one each in biology, economics, law, speech pathology, and one in statistics. And so that tells us, of course, that these major people in data science are not trained as statisticians, only one of them has formal training in that. So that gets us to the next question, where do these two fields, statistics and data science diverge? Because they seem like they should have a lot in common, but they don't have a lot in training. And specifically, we can look at the training, most data scientists are not trained formally as statisticians. Also, in practice, things like machine learning and big data, which are central to data science, are not shared generally, with most of statistics. And so they have separate domains there. And then there's the really important issue of context. Data scientists tend to work in different settings than statisticians. Specifically, data scientists very often work in commercial settings where they're trying to get recommendation engines or ways of developing a product that will make them money. So maybe instead of having data science as a subset of statistics, we can think of it more as these two fields have different niches, they both analyze data, but they do different things in different ways. So maybe it's fair to say they share they overlap, they both have analysis in common of data. But otherwise, they are ecologically distinct. So in sum, what we can say here is that data science and statistics both use data, they analyze it. But the people in each tend to come from different backgrounds, and they tend to function with different goals and contexts. And in that way, render them to be conceptually distinct fields, despite the apparent overlap.