 I want to thank you for joining me in coding and data science. And we'll wrap up this course by talking about some of these specific next steps that you can take for working in data science. The idea here is that you want to get some tools and you want to start working with those tools. Now, please keep in mind something that I've said at another time. Data tools and data science are related. They're important. But don't make the mistake of thinking that if you know the tools that you have done the same thing has actually conducted data science. That's not true. People sometimes get a little enthusiastic and they get a little carried away. What you need to remember is the relationship really is this data tools are an important part of data science, but data science itself is much bigger than just the tools. Now, speaking of tools, remember, there's a few kinds that you can use and that you might want to get some experience with these. Number one, in terms of apps or just specific built applications, Excel and Tableau are really fundamental for both getting the data from clients or doing some basic data browsing. And Tableau is really wonderful for interactive data visualization. I strongly recommend that you get very comfortable with both of those. In terms of code, it's a good idea to learn either R or Python or ideally to learn both because you can use them hand in hand. In terms of utilities, it's a great idea to learn how to work with bash, the command line utility, and to use regular expressions or reg X, you can actually use those in lots and lots of programs, regular expressions. And so they can have a very wide application. And then finally, data science requires some kind of domain expertise, you're going to need some sort of field experience or intimate understanding of a particular domain and the challenges that come up and what constitutes workable answers and the kind of data that's available. Now, as you go through all of this, you don't need to build this monstrous list of things. Remember, you don't need everything, you don't need every tool, you don't need every function, you don't need every approach. Instead, remember, get what's best for your needs, and for your style. But no matter what you do, remember tools or tools, there are means to an end. Instead, you want to focus on the goal of your data science project, whatever it is. And I can tell you really, the goal is meaning extracting meaning out of your data to make informed choices. In fact, I'll say a little more. The goal is always meaning. And so with that, I strongly encourage you get some tools, get started in data science and start finding meaning in the data that's around you.