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.…
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
Skill Level Intermediate
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1. Software Installation
2. Data, Exploratory Analysis, and Performance Analysis
3. Inference and Regression Analysis
5. Cluster Analysis
6. Conjoint Analysis
7. Best Practices
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