 In order to get a better understanding of data science, it can be helpful to look at contrasts between data science and other fields. Probably the most informative is with big data, because these two terms are actually often confused. It makes me think of situations where you have two things that are very similar, but not the same like we have here in the piazza San Carlo in turn Italy. Part of the problem stems from the fact that data science and big data both have Venn diagrams associated with them. So for instance, Venn number one for data science is something we've seen already. We have three circles, and we have coding, and we have math, and we have some domain expertise that put together get data science. On the other hand, Venn diagram number two is for big data, it also has three circles, and we have the high volume of data, the rapid velocity of data, and the extreme variety of data. Take those three V together, you get big data. Now, we can also combine these two if we want in a third Venn diagram, we call it big data and data science. This time it's just two circles with big data on the left and data science on the right. And the intersection there in the middle is big data science, which actually is a real term. But if you want to do a compare and contrast, it kind of helps to look at how you can have one without the other. So let's start by looking at big data without data science. So these are situations where you may have the volume or velocity of variety data, but don't need all the tools of data science. So we're just looking at the left side of the equation right now. Now, truthfully, this only works if you have big data without all three V some say you have to have the volume velocity and variety for to count as big data, I basically say anything that doesn't fit into a standard machine is probably big data. I can think of a couple of examples here of things that might count as big data, but maybe don't count as data science. Machine learning, where you can have very large data sets and probably very complex, doesn't require much domain expertise. So that may not be data science word counts, where you have an enormous amount of data. And it's actually a pretty simple analysis. Again, doesn't require much sophistication in terms of quantitative skills or even domain expertise. So maybe maybe not data science. On the other hand, to do any of these, you're going to need to have at least two skills, you're going to need to have the coding, and you will probably have to have some sort of quantitative skills as well. So how about data science without big data? That's the right side of this diagram. Well, to make that happen, you're probably talking about data with just one of the three v's from big data. So either volume, or velocity, or variety, but singly. So for instance, genetics data, you have a huge amount of data. And it comes in a very set structure, and it tends to come in at once. So you got a lot of volume. And it's a very challenging thing to work with, you have to use data science. But it may or may not count as big data. Similarly, streaming sensor data, where you have data coming in very quickly, but you're not necessarily saving it, you're just looking at these windows in it. That's a lot of velocity. And it's difficult to deal with it takes data science, the full skill set, but it may not require big data per se, or facial recognition where you have enormous variety in the data, because you're getting photos or videos that are coming in. Again, very difficult to deal with requires a lot of ingenuity and creativity, may or may not count as big data, depending on how much of a stickler you are about definitions. Now, if you want to combine the two, we can talk about big data science. And in that case, we're looking right here at the middle. This is a situation where you have volume and velocity and variety in your data. And truthfully, if you have the three of those, you are going to need the full data science skill set, you're going to need coding and statistics and math and you're going to have to have domain expertise, primarily because of the variety you're dealing with. But taken all together, you do have to have all of it. So in sum, here's what we get. Big data is not equal to is not identical to data science. Now there's common ground. And a lot of people who are good at big data are good at data science and vice versa. But they are conceptually distinct. On the other hand, there is the shared middle ground of big data science that unifies the two separate fields.