In week two of course, we're going to be covering a bunch of software that you're going to install that will constitute the data scientist's toolbox, as we described it for this course's track. So the first question you might ask yourself is, what software do you need? Well to know what, software you need, you have to know what exactly a data scientist is going to do. So, in this course sequence we're going to talk about all the different components of being a data scientist. So we're going to start with defining a question of interest, and then identifying the ideal data set try to ident, to answer that question. Determining if that data is even accessible, a lot of times the ideal dataset isn't even available. Then ways that you can go out and actually obtain the data whether it's from a database, or from a website, cleaning the data up so that it can be processed and analyzed. Performing some sort of exploratory analysis, including making plots and clusterings so you can identify patterns that you didn't know about before hand in the data set. Performing statistical prediction or modeling to try to, build a sort of an intuition about what's going to happen in the next sample you might take. Interpreting your results, challenging them. Then synthesizing them and writing them up in reproducible ways that can be shared with other people. Finally, we're going to talk about distributing results through things like interacting graphics, also through right ups and presentations, and finally through interactive apps built on top of R. So the main workhorse of data science in terms of this data science track is the R programming language. There are other alternative languages that are also really great for data science, but we're going to be focusing on R, since it's one of the most widely used languages. And it's widely supported by a large group of developers. Who can contribute new packages all the time that can improve and extend the functionality of R. We'll be installing this in the second week of the class. We'll do most of our coding in RStudio. RStudio is an Integrated Development Environment, an IDE for R. It's actually one of the best IDE's I think for many other languages as well in terms of data science. The R IDE is free as well just like the language R, and so we will be downloading this IDE and setting it up again the second week of class. The interface looks something like this. And we'll talk a lot more about this in the second week and later on in the rest of the class. But you can see here in the top left-hand corner I've got a file. So this is a new .R file that's going to contain some code that we're going to be writing in. So we can write that code, here in the file at the prompt and then down here, you see a console. So we'll be entering sort of a commands at the command line down here in this console. And then over here you can see other information you might be interested in looking at. See plots you recently made, the packages that you have loaded, or help files for specific functions that you might be interested in. There are a lot of other really nice functions that come with Rstudio, and we'll be talking about those more throughout the class. The primary type of file that we'll be interacting with, for the most part in this class, is an R script. So, an R script is a file with the extension .R, and so it's just a, actually a text file. Except the text file contains bits of R code, so here it's you can see a comment. So this isn't actually executed but R you could include that so that people can understand what's happening in the code. And then there're things like functions and so forth which we'll be talking about a lot more when we're coding. If this seems intimidating to look at this function right now you should worry about it when you get through R programming. You'll be a wizard and be able to do things much more complicated than this. The other thing that we'll be using is R markdown documents. So, reproducible research involves creating documents that can be reproduced. In other words, they can be rerun and produce the exact same numbers that you got when you did your analysis. And the primary vehicle for doing that is through markdown and R markdown. So this is a file with an extension ,RMD and this .RMD file has a very structured format of text file. And so we'll talk a lot more about what that format is later but you could take this structured file and you can knit it to html with this button here. And you actually create an html file that will actually be formatted very nicely. So for example, what you type in text looks like this, and it turns into a nice bulleted list in HTML, once you knit HTML. And we'll talk a lot more about how that file works later in the class. We're going to talk about how we are going to do distributed version control with Github and Git. So, part of this class will be setting up your Github account and creating a portfolio, of all the different things that you do throughout the course track, that then you can share with employers. Or you can share and contribute to other projects, so that you can get your name out there in the data science community. We're going to running most of the commands from the shell or from the command line interface. So this is a command line interface, it doesn't look like much right here. You can see that there's a prompt up her in the top left. And we're going to be entering commands as text prompts. And those commands will then execute, allowing the programs that we're going to be talking about. So, there's a brief tour of all the tools that we're going to be using in this class.
