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Learning Tableau 2022

Learning Tableau 2022

By : Joshua N. Milligan
4.8 (29)
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Learning Tableau 2022

Learning Tableau 2022

4.8 (29)
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

Summary

Up until this chapter, we’d looked at data that was, for the most part, well structured and easy to use. In this chapter, we considered what constitutes a good structure and ways to deal with poorly structured data. A good structure consists of data that has a meaningful level of detail and that has measures that match that level of detail. When measures are spread across multiple columns, we get data that is wide instead of tall.

We also spent some time understanding the basic types of transformation: pivots, unions, joins, and aggregations. Understanding these will be fundamental to solving data structure issues.

You also got some practical experience in applying various techniques to deal with data that has the wrong shape or has measures at the wrong level of detail. Tableau gives us the power and flexibility to deal with some of these structural issues, but it is far preferable to fix a data structure at the source.

In the next chapter, we’ll...

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