 The next step we need to talk about in data science methods is coding. And I'm going to give you a very brief non technical overview of coding and data science. The idea here is that you're going to get in there and you are going to be king of the jungle, master of your domain, and make the data jump when you need it to jump. Now, if you remember when we talked about the data science Venn diagram in the beginning, coding is up here on the top left. And while we often think about sort of people typing lines of code, which is very frequent, it's more important to remember when we talk about coding or just computers in general, what we're really talking about here is any technology that lets you manipulate the data in the ways you need to perform the procedures you need to get the insight that you want out of your data. Now, there are three very general categories that we're going to talk about. What we'll be discussing here on data lab. The first is apps. These are specialized applications or programs for working with data. The second is data or specifically data formats. There are special formats for web data. I'll mention those in a moment. And then code. There are programming languages that give you full control over what the computer does and how you interact with the data. Let's take a look at each one very briefly. In terms of apps, they're spreadsheets like Excel or Google Sheets. These are the fundamental data tools of probably the majority of the world. They're specialized applications like Tableau for data visualization, or SPSS a very common statistical package in the social sciences and in business. And one of my favorite JASP, which is a free open source analog of SPSS, which actually I think is a lot easier to use and replicate research with. And there are tons of other choices. Now, in terms of web data, it's helpful to be familiar with things like HTML and XML and JSON and other formats that are used to encapsulate data on the web. Because those are the things that you're going to have to be programming about to interact with when you get your data. And then their actual coding languages are probably the most common along with Python, general purpose language, but it's been well adapted for data use. There's SQL, the structured query language for databases, and very basic languages like C and C++ and Java, which are used more in the back end of data science. And then there's bash, the most common command line interface, and regular expressions. And we'll talk about all of these in other courses here at Datalab. But remember this tools are just tools. They're only one part of the entire data science process. There a means to the end. And the end the goal is insight. You need to know where you're trying to go, and then simply choose the tools that help you reach that particular goal. That's the most important thing. So in sum, here's a few things. Number one, use your tools wisely. Remember, your questions need to drive the process, not the tools themselves. Also, I'll just mention that a few tools is usually enough, you can do an awful lot with Excel and R. And then the most important thing is focus on your goal and choose your tools and even your data to match the goal. So you can get the most useful insights from your data.