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<p>About the Author vii</p> <p>About the Technical Editors ix</p> <p>Credits xi</p> <p>Acknowledgments xiii</p> <p>Foreword by <i>Nate Vogel</i> xxi</p> <p>Foreword by <i>Sophie Sparkes</i> xxiii</p> <p>Introduction xxv</p> <p><b>Chapter 1: Introduction and Getting Started with Tableau 1</b></p> <p>The Advantages of a Modern Analytics Platform 2</p> <p>My Personal Tableau Story 3</p> <p>The Tableau Application Suite 4</p> <p>Installing Tableau Desktop 5</p> <p>System Requirements for Tableau Desktop 5</p> <p>Downloading and Installing Tableau Desktop 6</p> <p>Registering and Activating Tableau Desktop 6</p> <p>Data Preparation 6</p> <p>Crosstab Reports with Wide Tables 7</p> <p>Preparing Your Data for Analysis 8</p> <p>Long Tables Suitable for Analysis 8</p> <p>The Sample Dataset 9</p> <p>Finding the Dataset 9</p> <p>Understanding the Data 10</p> <p>Opening the Excel File Containing the Sample Dataset 11</p> <p>The Tableau Workspace 13</p> <p>The Menu Bar 15</p> <p>The Data Pane 16</p> <p>Working with Measures and Dimensions 17</p> <p>Visualizing a First Measure 17</p> <p>Breaking Down a Measure Based on a Dimension 17</p> <p>Working with Marks 19</p> <p>Working with Color 20</p> <p>Adding More Information to Tooltips 21</p> <p>Saving, Opening, and Sharing Your Workbooks 21</p> <p>Saving Workbooks 21</p> <p>Opening Workbooks 23</p> <p>Sharing Workbooks with Tableau Reader 23</p> <p><b>Chapter 2: Adding Data Sources in Tableau 25</b></p> <p>Setting Up a Data Connector 26</p> <p>Connecting to a File 27</p> <p>Connecting to a Server 28</p> <p>Connecting to a Cloud Service 30</p> <p>Selecting Data Tables 31</p> <p>Adding a Table to a Data Model 31</p> <p>Joins 31</p> <p>Unions 34</p> <p>Specific Unions (Manual) 34</p> <p>Wildcard Unions (Automatic) 35</p> <p>Data Extracts and Live Connections 36</p> <p>Live Connections 36</p> <p>Untethered with a Data Extract 37</p> <p>Data Protection and Data Governance 38</p> <p>Editing the Model’s Metadata 38</p> <p>Data Types 40</p> <p>Changing a Field’s Data Type 41</p> <p>Adding Hierarchies, Calculated Fields, and Table Calculations 41</p> <p>Data Collection 42</p> <p>Data Collection with IFTTT and Google Sheets 42</p> <p>Website Analysis with Google Analytics 43</p> <p>Checklist for Increasing Performance 46</p> <p>General Advice for Performance Optimization 46</p> <p>Performance Optimization with Files and Cloud Services 47</p> <p>Performance Optimization with Database Servers 48</p> <p><b>Chapter 3: Creating Data Visualizations 49</b></p> <p>Chart Types 50</p> <p>Ready, Set, Show Me 52</p> <p>How Show Me Works 52</p> <p>Scatter Plots 52</p> <p>Bar Charts, Legends, Filters, and Hierarchies 53</p> <p>Bar Charts 54</p> <p>Hierarchies 54</p> <p>Filters 55</p> <p>Line Charts 57</p> <p>Straight Lines 57</p> <p>Adjusting the Time Dimension 58</p> <p>Step Charts 59</p> <p>Jump Lines 59</p> <p>Continuous Date Fields 61</p> <p>Highlight Tables 64</p> <p>Step 1: Cross Tables 64</p> <p>Step 2: Add Color 64</p> <p>Step 3: Change the Mark Type 66</p> <p>Heatmaps 67</p> <p>Step 1: Build the Table 67</p> <p>Step 2: Choose an Interesting Color Palette 68</p> <p>Step 3: Change the Size of Marks 69</p> <p>Bullet Charts 69</p> <p>Step 1: Side-By-Side Bars 71</p> <p>Step 2: Overlay the Measures 71</p> <p>Cumulative Sums with Waterfall Charts 73</p> <p>Step 1: Sorted Bar Chart 73</p> <p>Step 2: Cumulative Sum and Gantt bars 74</p> <p>Step 3: Calculate the Step Size 75</p> <p>Reflection: The Anatomy of a Tableau Visualization 77</p> <p><b>Chapter 4: Aggregate Functions, Calculated Fields, and Parameters 81</b></p> <p>Aggregate Functions 82</p> <p>Calculated Fields 84</p> <p>Aggregations in Calculated Fields 86</p> <p>Text Operators 88</p> <p>Splits 88</p> <p>Shortening Character Strings 89</p> <p>Converting Between Uppercase and Lowercase 90</p> <p>Replacing Substrings 90</p> <p>Date Fields 90</p> <p>Date Parts 90</p> <p>Traditional Gregorian and ISO 8601 Calendars 91</p> <p>Date Calculations 92</p> <p>Parsing Date Parts 92</p> <p>Date Format Conversions 93</p> <p>Logical Functions in Calculated Fields 94</p> <p>Case Discrimination 94</p> <p>Case Discrimination with IF-THEN-ELSE Logic 95</p> <p>Case Discrimination with the IIF Function 96</p> <p>Working with NULL Values 97</p> <p>Parameters 97</p> <p>Creating a Parameter and Displaying the Control Element 97</p> <p>Parameters in Calculated Fields 98</p> <p>Searching Text Fields 100</p> <p><b>Chapter 5: Table Calculations and Level of Detail Calculations 105</b></p> <p>Different Types of Calculations 106</p> <p>Order of Processing Steps 107</p> <p>Quick Table Calculations 107</p> <p>Setting Up a Quick Table Calculation 107</p> <p>Duplicate as Crosstab 110</p> <p>Editing Table Calculations 110</p> <p>Customized Table Calculations 113</p> <p>Bump Charts 113</p> <p>Dual Axis Charts 116</p> <p>Adjustable Moving Average 118</p> <p>Level of Detail Expressions 123</p> <p>Keywords and Syntax 123</p> <p>Cohort Analysis 124</p> <p>Regional Averages 125</p> <p>Higher-Level Regions 127</p> <p><b>Chapter 6: Maps 131</b></p> <p>Symbol Maps 132</p> <p>Filled Maps 134</p> <p>Density Maps 134</p> <p>Map Layers 136</p> <p>Maps with Pie Charts 137</p> <p>Creating a Pie Chart Map 138</p> <p>Adding a Filter 138</p> <p>Dual Axis Map 139</p> <p>Viz in Tooltip 140</p> <p>Step 1: Create the Second Chart 141</p> <p>Step 2: Embedding the Chart in Tooltips 142</p> <p>Reflection: The Anatomy of a Tableau Map 143</p> <p>Alternative Map Services 144</p> <p>Mapbox Maps 145</p> <p>Mapbox Account and Token 145</p> <p>Mapbox in Tableau 146</p> <p>Using the Background Map 146</p> <p>Spatial Data 147</p> <p>Undersea Communication Cables 148</p> <p>Open Data 152</p> <p><b>Chapter 7: Advanced Analytics: Trends, Forecasts, Clusters, and other Statistical Tools 155</b></p> <p>Overview of the Tableau Analytics Pane 156</p> <p>Constant, Average, and Reference Lines 157</p> <p>Trend Lines 157</p> <p>Adding Trend Lines 158</p> <p>Trend Line Options 160</p> <p>Line and Trend Model Description 161</p> <p>Forecasts 162</p> <p>Adding a Forecast Line to the View 162</p> <p>Forecast Settings 162</p> <p>Model Description 164</p> <p>Cluster Analysis 166</p> <p>Clustering in Tableau 166</p> <p>Saving and Working with Clustering Results 167</p> <p>Python, R, and MATLAB Integration 168</p> <p>Getting Started with Python and TabPy 169</p> <p>Connecting Tableau with TabPy 170</p> <p>Python Scripts in Calculated Fields 172</p> <p>Trellis Chart with Python Script 173</p> <p>R Integration 174</p> <p>Security 175</p> <p>Example: Local Regression with R 175</p> <p>MATLAB Integration 178</p> <p><b>Chapter 8: Interactive Dashboards 181</b></p> <p>Preliminary Considerations 182</p> <p>Creating a New Dashboard 183</p> <p>The Dashboard Pane 184</p> <p>Placing Charts on the Dashboard 185</p> <p>Dashboard Titles 187</p> <p>Navigation Buttons 188</p> <p>Dashboard Actions 191</p> <p>Filter Actions 191</p> <p>Adding and Editing Filter and Highlight Actions 193</p> <p>Adding Web Content via URL Actions 195</p> <p>Email Notifications via URL Actions 198</p> <p>Dashboard Starters: Templates for Cloud Data 199</p> <p>Dashboard Best Practices and Inspiration 201</p> <p>Design Tips for Creating a Dashboard 201</p> <p>Tableau Public: A Gallery of Inspiration 202</p> <p><b>Chapter 9: Sharing Insights with Colleagues and the World 205</b></p> <p>Preliminary Considerations 206</p> <p>Tableau Online and Tableau Server 207</p> <p>Publishing 207</p> <p>Ask Data 211</p> <p>Tableau Mobile 212</p> <p>Tableau Public 213</p> <p>Publishing to Tableau Public 214</p> <p>Your Tableau Public Profile 216</p> <p>Web Embedding 216</p> <p><b>Chapter 10: Data Preparation with Tableau Prep 221</b></p> <p>Connecting to Data 222</p> <p>Wildcard Unions 226</p> <p>Additional Connections 228</p> <p>Inspecting the Data 229</p> <p>Removing Unneeded Fields 230</p> <p>Data Cleaning and Formatting 231</p> <p>Cleaning Steps and the Profile Pane 231</p> <p>Calculated Fields 233</p> <p>Built-in Cleaning Features 235</p> <p>Renaming Cleaning Steps 235</p> <p>Unions 237</p> <p>Joins 238</p> <p>Splits 239</p> <p>Grouping 240</p> <p>Joining 240</p> <p>Running the Flow and Outputting the Data 242</p> <p>Saving Flows 244</p> <p>Index 245</p>

<p><b>Alexander Loth</b> is a data scientist with over 10 years' experience in the Enterprise software space, focused primarily on Digital Transformation, Big Data, Machine Learning, and Business Analytics. Since 2015 he has been with Tableau Software as Digital Strategist in Frankfurt, Germany where he guides organizations to evolve their data-driven culture. Prior to Tableau, Alexander was a Data Scientist at CERN and worked as a consultant for Capgemini and in software engineering at SAP.

<p><b>A 4-COLOR JOURNEY THROUGH A COMPLETE TABLEAU VISUALIZATION FOR NON-TECHNICAL BUSINESS USERS</b> <p>Tableau is a popular data visualization and analytics tool favored by financial analysts, marketers, statisticians, business and sales professionals, data scientists, developers, and many others who need to explore insights and present visual, easy-to-understand data. <i>Visual Analytics with Tableau</i> is an accessible, step-by-step introduction to the world of visual analytics. This up-to-date guide is ideal for both beginners and more experienced users seeking a practical introduction to the fields of data analysis and visualization. Through hands-on examples and exercises, readers learn how to analyze their own data and clearly communicate the results. <p>This guide covers everything you need to get started with Tableau, from the first steps of connecting to data, creating different types of charts, and adding calculation fields to more advanced features such as table calculations, forecasts, clusters, and R, Python, and MATLAB integration for sophisticated statistical modelling. User-friendly instructions for existing options within the Tableau ecosystem—Tableau Desktop, Tableau Prep, Tableau Server, Tableau Online, and Tableau Public—enable you to integrate, clean, and prepare your data and share your work with others. Visual Analytics with Tableau: <ul> <li>Covers the newest versions of Tableau 2018.3 and 2019.1 plus Tableau Prep, Tableau's brand-new data integration application</li> <li>Requires no background in mathematics nor any programming experience</li> <li>Focuses on the visual analytics functionality of Tableau rather than complex statistical programming</li> <li>Offers expert guidance from popular Tableau Germany employee and visualization expert Alexander Loth</li> <li>Discusses advanced Tableau functionality and working with different data structures</li> <li>Provides easy-to-follow instructions, full-color illustrations, learning tools, online resources, and more</li> </ul> <p>If you're getting started with visual analytics and Tableau, this book will teach you everything you need to know to build the foundations and understand how and why to explore your data visually. Alexander has created a fantastic resource that guides you step by step through the process of preparing your data, using Tableau Desktop to analyse it and finding insights.</br> <b>—Eva Murray</b>, Head of BI and Tableau Zen Master at Exasol <p>If you're keen to go from beginner to expert in Tableau, Alexander's excellent book gives you everything you need to know. With a crisp and clear style, he talks the reader through all aspects of Tableau, from data cleaning through data analysis and into sharing insight with others.</br> <b>—Andy Cotgreave</b>, Author of <i>Big Book of Dashboards</i> and Technical Evangelist at Tableau Software <p><i>Visual Analytics with Tableau</i> – an easy to understand book by Alexander Loth, one of Tableau's very first employees based in Germany, a recognized speaker on countless conferences, and a Tableau Jedi. It contains both the basics and advanced Tableau features. If you ask me: there is nothing more you need to get started with Tableau!</br> <b>—Klaus Schulte</b>, Professor at Münster School of Business & 2019 Tableau Zen Master

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