Learn how to install R for use with the course materials.
- [Narrator] We will be using three programming languages in this course. The first programming language is known as R, which I think is the leading open source platform for statistical modeling available today. Now in terms of functionality, it's comparable to other popular but proprietary statistical modeling packages such as SAS. For this course, we're going to use the popular RStudio. Now RStudio is an integrated development environment for R and is available for free for Mac, Windows, and Linux platforms. Follow the link to RStudio.com and click on the Desktop version of RStudio.
Now we're going to scroll down to the bottom and choose Download specifically for the RStudio Desktop Personal License. This will provide us with a list of different installers for different operating systems. Just a quick note, if you're using Windows or another operating system, the process is going to be very much the same. You'll simply want to choose the installer that's right for your operating system. I'm going to download Mac OS X.
Once that file has finished downloading, I'm going to open it in Finder, and you can open it in whichever application makes the most sense for your operating system. Now I'm going to double click on the installer and install RStudio. I'm going to drag the RStudio into my Applications folder. Now I'm going to open my Applications folder, I'm going to find RStudio, and I'm going to drag it down into the dock so I have a place to open RStudio from quickly and easily.
I'm now going to open RStudio from that icon from the dock. At this point I'm being asked to install git. I'm going to simply bypass this option. And this is our RStudio environment. We'll go into more detail about this environment in a future video, and for now we can just close out of the application.
In this course, discover how to gain valuable insights from large data sets using specific languages and tools. Follow Chris DallaVilla as he walks through how to use R, Python, and Tableau to perform data modeling and assess performance. As Chris dives into these concepts, he shares specific case studies that come directly from his own work with clients. Plus, he shares three essential—and practical—best practices for data-driven marketing that you can use to bolster your organization's marketing performance.
- Installing R, Python, and Tableau
- Navigating the UI for R, Python, and Tableau
- Using R, Python, and Tableau
- Exploratory analysis
- Performing regression analysis
- Performing a cluster analysis
- Performing a conjoint assessment
- Stakeholder alignment