Book Image

Learning Tableau 2019 - Third Edition

By : Joshua N. Milligan
Book Image

Learning Tableau 2019 - Third Edition

By: Joshua N. Milligan

Overview of this book

Tableau is the gold standard of business intelligence and visual analytics tools in every industry. It enables rapid data visualization and interpretation with charts, graphs, dashboards, and much more. Updated with the latest features of Tableau, this book takes you from the foundations of the Tableau 2019 paradigm through to advanced topics. This third edition of the bestselling guide by Tableau Zen Master, Joshua Milligan, will help you come to grips with updated features, such as set actions and transparent views. Beginning with installation, you'll create your first visualizations with Tableau and then explore practical examples and advanced techniques. You'll create bar charts, tree maps, scatterplots, time series, and a variety of other visualizations. Next, you'll discover techniques to overcome challenges presented by data structure and quality and engage in effective data storytelling and decision making with business critical information. Finally, you'll be introduced to Tableau Prep, and learn how to use it to integrate and shape data for analysis. By the end of this book, you will be equipped to leverage the powerful features of Tableau 2019 for decision making.
Table of Contents (18 chapters)
Free Chapter
1
Section 1: Tableau Foundations
5
Section 2: Leveraging the Full Power of Tableau
10
Digging Deeper - Trends, Clustering, Distributions, and Forecasting
11
Section 3: Data Prep and Structuring
14
Section 4: Advanced Techniques and Sharing with Others

Summary

Up until this chapter, we'd looked at data which was, for the most part, well-structured and easy to use. In this chapter, we considered what constitutes good structure and ways to deal with poor data structure. 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.

Now, you've got some 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 take a brief pause from looking at Tableau Desktop to consider another alternative to tackling challenging...