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Learning Tableau 2022

Learning Tableau 2022

By : Joshua N. Milligan
4.7 (30)
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Learning Tableau 2022

Learning Tableau 2022

4.7 (30)
By: Joshua N. Milligan

Overview of this book

Learning Tableau 2022 helps you get started with Tableau and data visualization, but it does more than just cover the basic principles. It helps you understand how to analyze and communicate data visually, and articulate data stories using advanced features. This new edition is updated with Tableau’s latest features, such as dashboard extensions, Explain Data, and integration with CRM Analytics (Einstein Analytics), which will help you harness the full potential of artificial intelligence (AI) and predictive modeling in Tableau. After an exploration of the core principles, this book will teach you how to use table and level of detail calculations to extend and alter default visualizations, build interactive dashboards, and master the art of telling stories with data. You’ll learn about visual statistical analytics and create different types of static and animated visualizations and dashboards for rich user experiences. We then move on to interlinking different data sources with Tableau’s Data Model capabilities, along with maps and geospatial visualization. You will further use Tableau Prep Builder’s ability to efficiently clean and structure data. By the end of this book, you will be proficient in implementing the powerful features of Tableau 2022 to improve the business intelligence insights you can extract from your data.
Table of Contents (20 chapters)
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18
Other Books You May Enjoy
19
Index

Examples of include level of detail expressions

Include level of detail calculations can be very useful when you need to perform certain calculations at levels of detail that are lower (more detailed) than the view level of detail. Let’s take a look at an example.

Average loans per member

Some members have a single loan. Some have two or three or possibly more. What if we wanted to see how many loans the average member has on a state-by-state basis? Let’s consider how we might go about that.

We’ll start with a sheet where the view level of detail is State:

Figure 5.10: The starting place for the example—a filled map by state

It would be relatively easy to visualize the average credit score or average balance per state. But what if we want to visualize the average number of loans per member for each state? While there are several possible approaches to solving this kind of problem, here we’ll consider using the following level...

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