Book Image

Learning Tableau 2020 - Fourth Edition

By : Joshua N. Milligan
Book Image

Learning Tableau 2020 - Fourth Edition

By: Joshua N. Milligan

Overview of this book

Learning Tableau strengthens your command on Tableau fundamentals and builds on advanced topics. The book starts by taking you through foundational principles of Tableau. We then demonstrate various types of connections and how to work with metadata. We teach you to use a wide variety of visualizations to analyze and communicate the data, and introduce you to calculations and parameters. We then take an in-depth look at level of detail (LOD) expressions and use them to solve complex data challenges. Up next, we show table calculations, how to extend and alter default visualizations, build an interactive dashboard, and master the art of telling stories with data. This Tableau book will introduce you to visual statistical analytics capabilities, create different types of visualizations and dynamic dashboards for rich user experiences. We then move on to maps and geospatial visualization, and the new Data Model capabilities introduced in Tableau 2020.2. You will further use Tableau Prep’s ability to clean and structure data and share the stories contained in your data. By the end of this book, you will be proficient in implementing the powerful features of Tableau 2020 for decision-making.
Table of Contents (19 chapters)
9
Visual Analytics – Trends, Clustering, Distributions, and Forecasting
17
Other Books You May Enjoy
18
Index

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, Open Database Connectivity (ODBC), or the use of 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 ensure 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 of the data source or possibly be at a higher level of detail, but it 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 average GPA of...