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E-raamat: Visual Data Storytelling with Tableau

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This is the first end-to-end, full-color guide to telling powerful, actionable data stories using Tableau, the world’s #1 visualization software. Renowned expert Lindy Ryan shows you how to communicate the full business implications of your data analyses by combining Tableau’s remarkable capabilities with a deep understanding of storytelling and design.

 

Each chapter illuminates key aspects of design practice and data visualization, and guides you step-by-step through applying them in Tableau. Ryan demonstrates how “data stories” resemble and differ from traditional storytelling, and helps you use Tableau to analyze, visualize, and communicate insights that are meaningful to any stakeholder, in any medium.

 

Information Visualization in Tableau presents exercises that give you hands-on practice with the most up-to-date capabilities available through Tableau 10 and the full Tableau software ecosystem. Ryan’s classroom-tested exercises won’t just help you master the software: they’ll show you to craft data stories that inspire action.

 

Coverage includes:

  • The visual data storytelling paradigm: moving beyond static charts to powerful visualizations that combine narrative with interactive graphics
  • How to think like a data scientist, a storyteller, and a designer -- all in the same project
  • Data storytelling case studies: the good, the bad, and the ugly
  • Shaping data stories: blending data science, genre, and visual design
  • Seven best practices for visual data storytelling -- and common pitfalls to avoid
  • Tricks and hacks you can use with any toolset, not just Tableau
Foreword xii
Preface xiii
Acknowledgments xxiv
About the Author xxvi
1 Storytelling in a Digital Era 1(14)
A Visual Revolution
2(4)
From Visualization to Visual Data Storytelling: An Evolution
6(2)
From Visual to Story: Bridging the Gap
8(5)
Summary
13(2)
2 The Power of Visual Data Stories 15(18)
The Science of Storytelling
16(4)
The Brain on Stories
16(2)
The Human on Stories
18(2)
The Power of Stories
20(12)
The Classic Visualization Example
20(3)
Using Small Personal Data for Big Stories
23(4)
The Two-or-Four Season Debate
27(2)
Napoleon's March
29(2)
Stories Outside of the Box
31(1)
Summary
32(1)
3 Getting Started with Tableau 33(18)
Using Tableau
34(1)
Why Tableau?
34(2)
The Tableau Product Portfolio
36(2)
Tableau Server
37(1)
Tableau Desktop
37(1)
Tableau Online
37(1)
Tableau Public
37(1)
Getting Started
38(1)
Connecting to Data
38(6)
Connecting to Tables
39(2)
Live Versus Extract
41(1)
Connecting to Multiple Tables with Joins
42(2)
Basic Data Prep with Data Interpreter
44(1)
Navigating the Tableau Interface
45(3)
Menus and Toolbar
46(1)
Data Window
47(1)
Shelves and Cards
47(1)
Legends
47(1)
Understanding Dimensions and Measures
48(1)
Dimensions
48(1)
Measures
48(1)
Continuous and Discrete
48(1)
Summary
49(2)
4 Importance of Context in Storytelling 51(14)
Context in Action
53(3)
Harry Potter: Hero or Menace?
53(2)
Ensuring Relevant Context
55(1)
Exploratory versus Explanatory Analysis
56(2)
Structuring Stories
58(3)
Story Plot
59(1)
Story Genre
60(1)
Audience Analysis for Storytelling
61(3)
Who
61(1)
What
62(1)
Why
62(1)
How
63(1)
Summary
64(1)
5 Choosing the Right Visual 65(42)
The Bar Chart
66(4)
Tableau How-To: Bar Chart
68(2)
The Line Chart
70(3)
Tableau How-To: Line Chart
72(1)
The Pie and Donut Charts
73(5)
Tableau How-To: Pie and Donut Charts
74(4)
The Scatter Plot
78(5)
Tableau How-To: Scatter Plots
79(4)
The Packed Bubble Chart
83(2)
Tableau How-To: Packed Bubble Charts
83(2)
The Treemap
85(3)
Tableau How-To: Treemaps
86(2)
The Heat Map
88(3)
Tableau How-To: Heat Maps
89(2)
Maps
91(15)
Connecting to Geographic Data
92(1)
Assigning Geographic Roles
93(2)
Creating Geographic Hierarchies
95(2)
Proportional Symbol Maps
97(3)
Choropleth Map
100(6)
Summary
106(1)
6 Curating Visuals for Your Audience 107(36)
Visual Design Building Blocks
110(1)
Color
110(8)
Stepped Color
114(1)
Reversed Color
115(3)
Color Effects
118(3)
Opacity
118(1)
Mark Borders
119(1)
Mark Halos
120(1)
The Truth about Red and Green
121(3)
Lines
124(10)
Formatting Grid Lines, Zero Lines, and Drop Lines
128(3)
Formatting Borders
131(3)
Formatting, Shading, and Banding
134(5)
Shapes
139(3)
Shape Marks Card
139(1)
Custom Shapes
140(2)
Summary
142(1)
7 Preparing Data for Storytelling 143(24)
Basic Data Prep in Tableau: Data Interpreter
144(4)
Data Interpreter in Action
145(2)
Handling Nulls in Tableau
147(1)
Cleaning Messy Survey Data in Excel
148(7)
Step 1: Surface Cleaning
150(1)
Step 2: Creating a Numeric Copy
151(2)
Step 3: Creating the Meta Helper File
153(2)
Pivoting Data from Wide to Tall
155(1)
Reshaping Survey Data with Tableau 10
156(9)
Step 1: Creating Extracts
156(4)
Step 2: Joining Data Sources
160(5)
Summary
165(2)
8 Storyboarding Frame by Frame 167(24)
Understanding Stories in Tableau
168(8)
Individual Visualizations (Sheets)
169(1)
Dashboards
169(3)
Story Points
172(4)
The Storyboarding Process
176(2)
Planning Your Story's Purpose
176(1)
Storyboarding Your Data Story
177(1)
Building a Story
178(12)
Making Meta Meaningful
179(1)
Visualizing Survey Demographics
179(6)
Act One: Demographic Dashboard and Key Question
185(2)
Act Two: Questioning Character Aggression
187(1)
Act Three: The Reveal
188(2)
Summary
190(1)
9 Advanced Storytelling Charts 191(34)
Timelines
192(7)
Bar-in-Bar Charts
199(3)
Likert Visualizations
202(13)
100% Stacked Bar Chart
203(2)
Divergent Stacked Bar Chart
205(10)
Lollipop Charts
215(6)
Labeled Lollipops
219(2)
Word Clouds
221(3)
Summary
224(1)
10 Closing Thoughts 225(8)
Five Steps to Visual Data Storytelling
226(2)
Step
1. Find Data That Supports Your Story
226(1)
Step
2. Layer Information for Understanding
227(1)
Step
3. Design to Reveal
227(1)
Step
4. Beware the False Reveal
227(1)
Step
5. Tell It Fast
228(1)
The Important Role of Feedback
228(1)
Ongoing Learning
229(4)
Teach Yourself: External Resources
229(2)
Companion Materials to This Text
231(2)
Index 233
Lindy Ryan is passionate about telling stories with data. She specializes in translating raw data into insightful stories through carefully curated visuals and engaging narrative frameworks.

Before joining academia, Lindy was the Research Director for research and advisory firm Radiant Advisors from 2011 through 2016. In this role Lindy led Radiants analyst activities in the confluence of data discovery, visualization, and visual analytics. She also developed the methodology for the Data Visualization Competency Center (DVCC), a framework for helping data-driven organizations to effectively implement data visualization for enterprise-wide visual data analysis and communication. Her tool-agnostic approach has been successfully implemented at a variety of organizations across several industries and with multiple visualization technologies, including Tableau, Qlik, and GoodData. She remains a respected analyst in the data visualization community and is a regular contributor to several industry publications as well as a speaker at conferences worldwide.

Lindy began her academic career as an associate faculty member at City University of Seattles School of Applied Leadership where she taught graduate courses in business leadership from 2012 to 2016. In early 2016 she joined the ambitious team at the Rutgers Discovery Informatics Institute and began contributing to multidisciplinary research focused on designing solutions for the next generation of supercomputers tasked with enabling cutting-edge extreme-scale science. Currently, Lindy leads RDI2s research on understanding and preventing cyberbullying behaviors in emerging technology users through advanced computing approaches.

Today, Lindy teaches courses in visual analytics and data visualization in Rutgers Universitys Professional Science Masters program and in Montclair State Universitys Business Analytics program. She is a recipient of the MSU Professing Excellence Award, which recognizes professors teaching excellence, particularly those who inspire and motivate students. This honor is especially meaningful to Lindy because in addition to her passion for teaching, her research includes a commitment to STEM advocacy, and she spends time on research related to increasing gender equity in CS&E and finding new and novel ways to nurture visual data literacy skills in early STEM learners.



Lindy is an active committee member of the New Jersey Big Data Alliance, a partnership of New Jersey-based academic institutions that serves as the States legislated consortium on research, education and outreach in advanced computation and big data. She is the author of The Visual Imperative: Creating a Culture of Visual Discovery released by Morgan Kaufmann in 2016, and the owner of Black Spot Books, a traditional, analytics-driven small-press publishing house.

Learn more about Lindy at www.visualdatastorytelling.com. You can also follow her on Twitter @lindy_ryan or view samples of her work on her Tableau Public page at https:// public.tableau.com/profile/lindyryan#!/.