[MUSIC] The software that we will be using for this course is RStudio, which sits on something called base R. R and RStudio are very popular, they've been around for a while. And Java would be needed separately in some sense to get things to work for where we are going. So that's what RStudio looks like. So you can see a fairly appealing user friendly interface. One of the questions that would come up at this point is, what is R, why R? There were other options available, probably. Why not them? R is both a language and an environment. It's also a platform, basically. It was born in a university. Its DNA, in some sense, is academic. It was born in the stats department of universities. So it's basically coming from where statisticians were programming it. Very powerful, very flexible, completely open source. Which basically means you can develop something, contribute to the community. You move firms, you take what is yours with you. Perfectly fine. No licenses, no proprietary restrictions and so on would apply. That's the logo of R, though I would prefer it to look something like this. Quite powerful, let me assure you. Assignments in this course in some sense will require the use of R. I am going to provide the code in some sense, R apps, which you can open and run without really having to interact with backend code. So that should take care of a lot of things. The example problems that we will do in the next module for instance. I'm going to put up instructions, download and install R and RStudio, also have Java on your machine updated. Instructions for how to do so will be made available. The plan is to get you to perform analyses using our apps, and the package called Shiny. All right, so let me basically take you to a summary of what we just saw in course software. I'm going to expect that everybody's going to download and install base R, RStudio, and Java. Instructions for how to do so will be made available. The plan is basically to tackle simple analytics problems, and I stress on the word simple, using desktop R apps. These R apps can be run off your local machine. Do not require user interacting with back-end code. And we will see these kind of R apps and associated problem solving in what will come up in the next session, Customer Analytics. [MUSIC]