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

Visualizing distributions

Often, simply understanding totals, sums, and even the breakdown of the part to whole only gives a piece of the overall picture. Many times, you'll want to understand where individual items fall within a distribution of all similar items.

You might find yourself asking questions such as:

  • How long do most of our patients stay in the hospital? Which patients fall outside the normal range?
  • What's the average life expectancy for components in a machine and which components fall above or below that average? Are there any components with extremely long or extremely short lives?
  • How far above or below the average score were most students' test scores?

These questions all have similarities. In each case, you seek an understanding of where individuals (patients, components, students) were in relation to the group. In each case, you most likely have a relatively high number of individuals. In data terms, you have a dimension (patient, components, and student) with high cardinality...