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

Structuring data for Tableau

We've already seen that Tableau can connect to nearly any data source. Whether it's a built-in direct connection, ODBC, or using the Tableau data extract API to generate an extract, no data is off limits. However, there are certain structures that make data easier to work with in Tableau.

There are two keys to ensuring a good data structure that works well with Tableau:

  • Every record of a source data connection should be at a meaningful level of detail
  • Every measure contained in the source should match the level of detail or possibly be at a higher level of detail, but should never be at a lower level of detail

For example, let's say you have a table of test scores with one record per classroom in a school. Within the record, you may have three measures: the average GPA for the classroom, the number of students in the class, and the...