Join Chris DallaVilla for an in-depth discussion in this video Exercise files, part of The Data Science of Marketing.
- [Instructor] You can download the exercise files…and save them to your desktop,…which is what I've done right here.…And so, for example, if I open up the exercise files,…you can see that each chapter,…and each video, has a corresponding number.…So, for example, exploratory analysis,…is right here in 02_02.…And you can see we have both an R file,…and a data file as well.…In the chapter just below that,…this is a python file, and then some data for it down below,…so we're going to walk through each of these,…real quickly, just so that we know how…to access the exercise files with our different platforms.…
So the first is R, so I'm going to open up our studio.…I'm going to select Not Now,…in case you're getting this prompt as well.…And, what I can see is I have the ability…to input a command right here.…And to access that data, and I'll go into more detail…about some of the commands that…we're typing in right now, in a future video.…But for now, just to make sure you can…access the different files I'm going to type myData,…
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
Next steps1m 8s
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