Book Image

Tableau 10 Complete Reference

By : Joshua N. Milligan, Tristan Guillevin
Book Image

Tableau 10 Complete Reference

By: Joshua N. Milligan, Tristan Guillevin

Overview of this book

Graphical presentation of data enables us to easily understand complex data sets. Tableau 10 Complete Reference provides easy-to-follow recipes with several use cases and real-world business scenarios to get you up and running with Tableau 10. This Learning Path begins with the history of data visualization and its importance in today's businesses. You'll also be introduced to Tableau - how to connect, clean, and analyze data in this visual analytics software. Then, you'll learn how to apply what you've learned by creating some simple calculations in Tableau and using Table Calculations to help drive greater analysis from your data. Next, you'll explore different advanced chart types in Tableau. These chart types require you to have some understanding of the Tableau interface and understand basic calculations. You’ll study in detail all dashboard techniques and best practices. A number of recipes specifically for geospatial visualization, analytics, and data preparation are also covered. Last but not least, you'll learn about the power of storytelling through the creation of interactive dashboards in Tableau. Through this Learning Path, you will gain confidence and competence to analyze and communicate data and insights more efficiently and effectively by creating compelling interactive charts, dashboards, and stories in Tableau. This Learning Path includes content from the following Packt products: • Learning Tableau 10 - Second Edition by Joshua N. Milligan • Getting Started with Tableau 2018.x by Tristan Guillevin
Table of Contents (20 chapters)
Title Page
About Packt
Deeper Analysis - Trends, Clustering, Distributions, and Forecasting

Data densification

Data densification is a broad term which indicates that missing values or records are filled in. Sometimes, specific terms, such as domain padding (filling in missing dates or bin values) or domain completion (filling in missing intersections of dimensional values), are used to specify the type of densification, but here we'll simply use the term data densification.


Data with missing values (such as data that doesn't have a record for every single date or only contains records for products that have been ordered as opposed to all products in the inventory) is referred to as sparse data.

Understanding when Tableau uses data densification and how you can turn it on or turn it off is important as you move toward mastering Tableau. There will be times when Tableau will engage data densification and you don't want it; you'll need to recognize it and understand the options to turn it off. At other times, you'll want to leverage data densification to solve certain types of...