 Another important contrast you can make in trying to understand data science is to compare it with coding or computer programming. Now this is where you're trying to work with a machine and you're trying to talk to that machine to get it to do things. In one sense, you can think of coding as just giving task instructions how to do something. And it's a lot like a recipe when you're cooking. You get some sort of user input or other input. And then maybe you have if then logic and you get output from it to take an extremely simple example. If you're programming in Python version two, you write print and then quotes hello world and that will put the words hello world on the screen. So you gave it some instructions and it gave you some output. Very simple programming. Now, coding and data gets a little more complicated. So for instance, there's word counts where you take a book or a whole collection of books, you take the words and you count how many there are in there. Now this is this is a conceptually simple task. And domain expertise and really math and statistics are not vital. But to make valid inferences and generalizations in the face of variability and uncertainty in the data, you need statistics. And by extension, you need data science. It might help to compare the two by looking at the tools of the respective trades. So for instance, there are tools for coding or generic computer programming. And there are tools that are specific to data science. So what I have right here is a list from the IEEE of the top 10 programming languages in 2015. And it starts at Java and C and goes down to Shell. And some of these are also used for data science. So for instance, Python, and R, and SQL are used for data science. But the other ones aren't major ones in data science. So let's in fact take a look at a different list of most popular tools for data science. And you see that things move around a little bit. Now ours at the top, SQL's there, Python's there. But for me, what's the most interesting on this list is that Excel is number five, which would never be considered programming per se, but is in fact a very important tool for data science. And that's one of the ways that we can compare and contrast computer programming with data science. In sum, we can say this, data science is not equal to coding. They're different things. On the other hand, they share some of the tools, and they share some practices specifically when coding for data. On the other hand, there is one very big difference in that statistics. Statistical ability is one of the major separators between general purpose programming and data science programming.