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The Tableau Workshop

You're reading from   The Tableau Workshop A practical guide to the art of data visualization with Tableau

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781800207653
Length 822 pages
Edition 1st Edition
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Authors (5):
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Shweta Savale Shweta Savale
Author Profile Icon Shweta Savale
Shweta Savale
Kenneth Michael Cherven Kenneth Michael Cherven
Author Profile Icon Kenneth Michael Cherven
Kenneth Michael Cherven
Sumit Gupta Sumit Gupta
Author Profile Icon Sumit Gupta
Sumit Gupta
Sylvester Pinto Sylvester Pinto
Author Profile Icon Sylvester Pinto
Sylvester Pinto
JC Gillet JC Gillet
Author Profile Icon JC Gillet
JC Gillet
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Toc

Table of Contents (12) Chapters Close

Preface
1. Introduction: Visual Analytics with Tableau 2. Data Preparation: Using Tableau Desktop FREE CHAPTER 3. Data Preparation: Using Tableau Prep 4. Data Exploration: Comparison and Composition 5. Data Exploration: Distributions and Relationships 6. Data Exploration: Exploring Geographical Data 7. Data Analysis: Creating and Using Calculations 8. Data Analysis: Creating and Using Table Calculations 9. Data Analysis: Creating and Using Level of Details (LOD) Calculations 10. Dashboards and Storyboards 11. Tableau Interactivity: Part 1

Introduction

At a very broad level, the whole data analytics process can be broken down into the following steps: data preparation, data exploration, data analysis, and distribution. This process typically starts with a question or a goal, which is followed by finding and getting the relevant data. Once the relevant data is available, you then need to prepare this data for your exploration and analysis stage. You might have to clean and restructure the data to get it in the right form, maybe combine it with some additional datasets, or enhance the data by creating some calculations. This stage is referred to as the data preparation stage. After this comes the data exploration stage. It is at this stage that you try to see the composition and distribution of your data, compare data, and identify relationships if any exist. This step gives an idea of what kind of analysis can be done with the given dataset.

Typically, people like to explore the data by looking at it in its raw form...

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