Book Image

Mastering Tableau

By : David Baldwin
Book Image

Mastering Tableau

By: David Baldwin

Overview of this book

Tableau has emerged as one of the most popular Business Intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. This book will empower you to become a master in Tableau by exploiting the many new features introduced in Tableau 10.0. You will embark on this exciting journey by getting to know the valuable methods of utilizing advanced calculations to solve complex problems. These techniques include creative use of different types of calculations such as row-level, aggregate-level, and more. You will discover how almost any data visualization challenge can be met in Tableau by getting a proper understanding of the tool’s inner workings and creatively exploring possibilities. You’ll be armed with an arsenal of advanced chart types and techniques to enable you to efficiently and engagingly present information to a variety of audiences through the use of clear, efficient, and engaging dashboards. Explanations and examples of efficient and inefficient visualization techniques, well-designed and poorly designed dashboards, and compromise options when Tableau consumers will not embrace data visualization will build on your understanding of Tableau and how to use it efficiently. By the end of the book, you will be equipped with all the information you need to create effective dashboards and data visualization solutions using Tableau.
Table of Contents (18 chapters)
Mastering Tableau
Credits
About the Author
www.Packtpub.com
Preface

CRISP-DM


The Cross Industry Standard Process for Data Mining (CRISP-DM) model was created between 1996 and 2000 as a result of a consortium including SPSS, Teradata, Daimler AG, NCR Corporation, and OHRA. It divides the process of data mining into six major phases, as shown in the following CRISP-DM reference model. This model provides a bird's-eye view of a data-mining project lifecycle. Although the sequence of the phases shown in the diagram is typical, it is not rigid; that is, jumping back and forth from phase to phase is allowed and expected. Note that the outer circle communicates the ongoing data-mining lifecycle. Data mining does not cease upon the completion of a particular project. Instead, it exists as long as the business exists and should be constantly revisited to answer new questions as they arise:

We will consider each of the six phases that comprise CRISP-DM and explore how Tableau can be used effectively throughout the lifecycle. We will particularly focus on the Data...