Learn how to perform regression analysis using Tableau and how to interpret the results.
- [Instructor] Let's continue to build on…what we've learned so far, now with Tableau.…I've opened up our application and connected to our data.…If you need help on how to connect to your data,…feel free to open up the video…on exploratory analysis in Tableau.…And I've connected specifically to the exercise files…03_04 and the .csv file…that you will find in that directory.…Okay, great.…We will navigate into our workspace…by clicking on the tab that reads Sheet 1.…And again, similar to how we have…seen in our two previous videos, we're going to plot our data.…
So we're going to grab our broadcast measure…and we're going to drop that onto our Column shelf.…Which you can think of the Column shelf…as the same as your X axis.…So Broadcast, Columns, and just dropping that right there.…And now let's grab our Net Sales…and drop that onto our Rows shelf,…which you can think of as the same as our Y axis.…So let's get out Net Sales and bring that over.…Now there's a little trick at this stage of the game…where you're going to want to turn off your aggregate measures.…
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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