Learn about how to perform a cluster analysis using Tableau and how to interpret the results.
- [Instructor] Tableau 10 introduced…k-means clustering functionality to the platform…and it's a great example of how the platform…is constantly evolving and getting more powerful.…I have Tableau open…and have connected to our data from our exercise files…so let's navigate into our workspace…and we can see our list of measures includes…three different behaviors.…We also have a brand preference measure, CTA…and some demographic information.…Now in this case we're interested in…creating a set of groups from CTA and age.…
In other words, this data tells us…the age for each response to a certain call to action.…I'm going to select our CTA measure…and drop that in the column shelf.…I'm going to select the Demo-Age…and drop that in the row shelf.…Let's go ahead and disaggregate the data…so drop down our Analysis menu…and deselect Aggregate Measures.…Now if you look towards the top left of Tableau…you'll see a tab just to the right of the Data tab…called Analytics.…
See that there?…Let's go ahead and click on that tab.…Under the Model heading is an option called Cluster…
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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