 I want to finish our extremely brief discussion of coding and data sciences and the languages that can be used by mentioning one other that's called bash. And bash really is a great example of old tools that have survived and are still being used actively and productively with new data. You can think of it this way, it's almost like typing on your typewriter, you're working at the command line, you're typing out code through a command line interface or CLI. Now, this method of interacting with computers practically goes back to the typewriter phase because it predates monitors. So before you even had a monitor, you would type in the code and it would print it out on a piece of paper. And the important thing to know about the command line is it's simply a method of interacting. It's not a language because lots of different languages can run at the command line. So for instance, it's important to talk about the concept of a shell. Now in computer science, a shell is a language or something that wraps around the computer to shell around the language that is the interaction level for the user to get things done at the lower levels that aren't really human friendly. On Mac computers and Linux, the most common is bash, which is short for born again shell. On Windows computers, the most common version is PowerShell. But whatever you do, there actually are a lot of choices. There's the born shell, there's the C shell, which is why I have a C shell right here, the Z shell, there's fish for friendly interactive shell and a whole bunch of other choices. But bash is the most common on Mac and Linux and PowerShell is the most common on Windows as a method of interacting with the computer at the command line level. Now there's a few things you need to know about this. First, you have a prompt of some kind in bash, it's a dollar sign. And that just means type your command here. Then the other thing is you type one line at a time. It's actually amazing how much you can get done with what's called a one liner program by sort of piping things together. So one feeds into the other, you can run more complex commands, if you use a script. And so you call a text document that has a bunch of things in it. And you can get much more elaborate analysis done. Now we have our tools here. In bash, we talk about utilities. And what these are are specific programs that accomplish specific tools. bash really thrives on do one thing and do it very well. There are two general categories of utilities for bash. Number one is the built ins. These are the ones that come installed with it. And so you're able to use them at any time by simply calling in their name. Some of the most common ones are cat, which is for catnate. And that's to put information together. There's awk, which is its own interpreted programming language. But it's often used for text processing from the command line. By the way, the name awk is comes from the initials of the people who created it. Then there's grep, which is for global search with a regular expression and print. It's a way of searching for information. And then there's said, which stands for stream editor. And its main use is to transform text, you can do an enormous amount with just these four utilities. A few more are head and tail that display the first or last 10 lines of a document, sort and unique, which sort and count the number of unique answers in a document, wc, which is for word count, and print f, which formats the output that you get in your console. And while you can get a huge amount of work done with just this small number of built in utilities, there are also a wide range of installables or other command line utilities that you can add to bash or to whatever program you're using. So for instance, some really good ones that have been recently developed are jq, which is for pulling in JSON or JavaScript object notation data from the web. And then there's JSON to CSV, which is a way of converting JSON to CSV format, which is what a lot of statistical programs are going to be happier with. There's real, which allows you to run a wide range of commands from the statistical programming language are in the command line as part of bash. And then there's big MLR. And this is a command line tool that allows you to access big ML's machine learning servers through the command line. Normally, you do it through a web browser and it accesses their servers remote. It's an amazingly useful program. But to be able to just pull it up when you're in the command line is an enormous benefit. What's interesting is that even though you have all these opportunities, all these different utilities, you can do amazing things and that there still is active development of utilities for the command line. So let's say this in some, despite being in one sense as old as the dinosaurs, the command line survives because it is extremely well evolved and well suited to its purpose of working with data, the utilities, both the built in and the installables are fast and they are easy. And generally, they do one thing and they do it very, very well. And then surprisingly, there is an enormous amount of very active development of command line utilities for these purposes, especially with data science.