 Let's wrap up our discussion of mathematics and data science and the data principles and talk about some of the next steps, things that you can do afterwards. Probably the most important thing is you may learn about math a long time ago, but now it's a good time to dig out some of those books and to go over some of the principles that you've used before. The idea here is that a little math can go a long way in data science. So things like algebra and things like calculus and things like big O and probability. All of those are important in data science, and it's helpful to have at least a working understanding of each. You don't have to know everything, but you do need to understand the principles of your procedures that you select when you're doing your projects. There are two reasons for that very generally speaking. First, you need to know if a procedure will actually answer your question. Does it give you the outcome that you need? Will it give you the insight that you need? Really critical. You need to know what to do when things go wrong. Things don't always work out. Numbers don't add up. You get impossible results or things just aren't responding. You need to know enough about the procedure and enough about the mathematics behind it that you can diagnose the problem and respond appropriately. And to repeat myself once again, no matter what you're working on in data science, no matter what tool you're using, what procedure you're doing, focus on your goal. And in case you can't remember that, your goal is meaning. Your goal is always meaning.