Learn how to perform analysis for prediction using Tableau and how to interpret the results.
- [Narrator] So, we have our Tableau environment loaded.…And we've connected to our data source.…Something to point out here is…at this point and time, Tableau does not…offer a predictive analytics function.…But there's definitely some things we can do…to visualize the potential influence…of our predictors, here in Tableau.…Tableau does offer a tree map…which isn't the same as a decision tree…but it can help tell a story.…Now, let's navigate into our workspace…by selecting sheet one, at the bottom…of the Tableau screen.…And let's select weather from our list of measures.…
And let's bring that over to the column shelf.…Now I want to transform this data into a dimension.…So the way I do that is I come up…to the column shelf.…I identify what's called the pill for weather.…And I'm going to drop down this arrow…and select dimension.…So we can see that weather's organized…by these different classifiers.…Now next, let's grab our dependent variable, sales.…And we'll bring that over to our row shelf.…So we can see that Tableau provides a line graph…
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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