Details

#MakeoverMonday


#MakeoverMonday

Improving How We Visualize and Analyze Data, One Chart at a Time
1. Aufl.

von: Andy Kriebel, Eva Murray

24,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 02.10.2018
ISBN/EAN: 9781119510727
Sprache: englisch
Anzahl Seiten: 496

DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.

Beschreibungen

<p><b>Explore different perspectives and approaches to create more effective visualizations</b></p> <p><i>#MakeoverMonday</i> offers inspiration and a giant dose of perspective for those who communicate data. Originally a small project in the data visualization community, <i>#MakeoverMonday</i> features a weekly chart or graph and a dataset that community members reimagine in order to make it more effective. The results have been astounding; hundreds of people have contributed thousands of makeovers, perfectly illustrating the highly variable nature of data visualization. Different takes on the same data showed a wide variation of theme, focus, content, and design, with side-by-side comparisons throwing more- and less-effective techniques into sharp relief.</p> <p>This book is an extension of that project, featuring a variety of makeovers that showcase various approaches to data communication and a focus on the analytical, design and storytelling skills that have been developed through #MakeoverMonday. Paging through the makeovers ignites immediate inspiration for your own work, provides insight into different perspectives, and highlights the techniques that truly make an impact.</p> <ul> <li>Explore the many approaches to visual data communication</li> <li>Think beyond the data and consider audience, stakeholders, and message</li> <li>Design your graphs to be intuitive and more communicative</li> </ul> <ul> <li>Assess the impact of layout, color, font, chart type, and other design choices</li> </ul> <p>Creating visual representation of complex datasets is tricky. There’s the mandate to include all relevant data in a clean, readable format that best illustrates what the data is saying—but there is also the designer’s impetus to showcase a command of the complexity and create multidimensional visualizations that “look cool.” <i>#MakeoverMonday</i> shows you the many ways to walk the line between simple reporting and design artistry to create exactly the visualization the situation requires.  </p>
<p>Foreword ix</p> <p>Acknowledgments xi</p> <p>About the Authors xv</p> <p><b>Part I </b></p> <p><b>Introduction 3</b></p> <p>What Is Makeover Monday? 3</p> <p>How Did Makeover Monday Start? 4</p> <p>The Community Project 7</p> <p>Pillars of Makeover Monday 16</p> <p>How to Use this book 29</p> <p><b>Part II </b></p> <p><b>Chapter 1 Habits of a Good Data Analyst 33</b></p> <p>Approaching Unfamiliar Data 33</p> <p>Analysis versus Visualization 44</p> <p>Take Your Time 47</p> <p>Build Context Through Additional Research 48</p> <p>Find Insights 50</p> <p>Communicate Clearly 54</p> <p>Ask Questions 57</p> <p>Summary 61</p> <p><b>Chapter 2 Data Quality and Accuracy 63</b></p> <p>Working with Incomplete Data 64</p> <p>Overcounting Data 74</p> <p>Sense-Checking Data 76</p> <p>Is the Data Aggregable? 80</p> <p>Substantiating Claims with Data 88</p> <p>Summary 90</p> <p><b>Chapter 3 Know and Understand the Data 91</b></p> <p>Using Appropriate Aggregations 92</p> <p>Explaining Metrics 109</p> <p>Identifying and Correcting Mistakes 115</p> <p>Time Series Analysis 119</p> <p>Summary 133</p> <p><b>Chapter 4 Keep It Simple 135</b></p> <p>What Is Simplicity? 135</p> <p>Simplicity in Design 136</p> <p>Simplicity in Analysis 150</p> <p>Simplicity in Storytelling 153</p> <p>Summary 157</p> <p><b>Chapter 5 Attention to Detail 159</b></p> <p>Typos 161</p> <p>Punctuation 162</p> <p>Formatting 162</p> <p>Crediting Images and Data Sources 182</p> <p>Summary 183</p> <p><b>Chapter 6 Designing for the Audience 185</b></p> <p>Creating an Effective Design 186</p> <p>Designing for Mobile 196</p> <p>Using Visual Cues for Additional Information 207</p> <p>Using Icons and Shapes 208</p> <p>Storytelling 211</p> <p>Reviewing Your Work to Improve Its Quality 216</p> <p>Summary 218</p> <p><b>Chapter 7 Trying New Things 219</b></p> <p>Developing a Sharing Culture 221</p> <p>Summary 235</p> <p><b>Chapter 8 Iterate to Improve 237</b></p> <p>Why Iterate? 237</p> <p>Examples of Effective Iteration 241</p> <p>Giving and Receiving Feedback 256</p> <p>Summary 263</p> <p><b>Chapter 9 Effective Use of Color 265</b></p> <p>The Significance of Color in Data Visualization 266</p> <p>Using Color to Evoke Emotions 267</p> <p>Using Color to Create Associations 273</p> <p>Using Color to Highlight 281</p> <p>Best Practices for Using Color 283</p> <p>Using Background Colors 287</p> <p>Using Text as a Color Legend 291</p> <p>Summary 294</p> <p><b>Chapter 10 Choosing the Right Chart Type 295</b></p> <p>Area Charts 296</p> <p>Stacked Bar Charts 299</p> <p>Diverging Bar Charts 304</p> <p>Filled Maps 309</p> <p>Donut and Pie Charts 318</p> <p>Packed Bubble Charts 325</p> <p>Treemaps 331</p> <p>Slopegraphs 338</p> <p>Connected Scatterplots 345</p> <p>Circular Histograms 353</p> <p>Radial Bar Charts 360</p> <p>Resources 366</p> <p>Summary 366</p> <p><b>Chapter 11 Effective Use of Text 367</b></p> <p>Effective Titles and Subtitles 367</p> <p>What Is Your Key Message? 377</p> <p>Instructions and Explanations 386</p> <p>Summary 398</p> <p><b>Chapter 12 Using Context to Inform 399</b></p> <p>The Importance of Context 400</p> <p>Using Simple Metrics 402</p> <p>Methods for Communicating Context 418</p> <p>Summary 428</p> <p><b>Part III</b></p> <p><b>The Community 431</b></p> <p>Long-Term Contributors 431</p> <p>Educators 446</p> <p>Employers 446</p> <p>Organizations 447</p> <p>Nonprofits 448</p> <p>Social Impact 448</p> <p>Makeover Monday Live Events 449</p> <p>Makeover Monday Enterprise Edition 450</p> <p>Source Lines 453</p> <p>Index 457</p>
<p><b>ANDY KRIEBEL</b> is Head Coach at The Information Lab Data School and a member of the Tableau Zen Master Hall of Fame. The Makeover Monday series originated from his blog at vizwiz.com, where he shares data visualization tips, tricks, and best practices.</p> <p><b>EVA MURRAY</b> is the Head of Business Intelligence at Exasol, a Tableau Ambassador, and a 2018 Tableau Zen Master. She has co-hosted Makeover Monday since 2017, and blogs about Tableau, travel, and triathlon at trimydata.com.</p>
<p><b>Praise for #MakeoverMonday</b> <p>"We humans can't learn alone or only by studying rules and principles. We learn in collaboration — copying and getting feedback from others — and by practicing, to grasp how principles apply to the real world, and when it's appropriate to tweak them or even break them. This book, the product of the Makeover Monday collaborative initiative, exemplifies this understanding of how learning really works. If you've already read some classics in the literature about data visualization, the next step is to grab this book and peruse its many before-and-after examples. You'll love some, dislike others, and even loathe a few, but in the process of mulling over all them you'll become a better visualization designer."</br> <b> —Alberto Cairo,</b> Knight Chair in Visual Journalism at the University of Miami, and author of <i>The Truthful Art: Data, Charts, and Maps for Communication</i> <p>"Visualizing data effectively is something you learn through practicing: making something, getting feedback, and iterating to refine and improve. The weekly #MakeoverMonday project has been a place for many people to do exactly that. With <i>#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time,</i> Andy and Eva have curated the thousands of visuals they've seen into a guide packed with lessons, tips, anecdotes, and examples. If you work with data, you'll appreciate the varied and creative approaches, learn mistakes to avoid, and be inspired to do your own practicing to take your data visualization game to the next level!"<br/> <b>—Cole Nussbaumer Knaflic,</b><i></i> author of<i> storytelling with data</i> <p>"#MakeoverMonday grew from a weekly blog series into a worldwide social data project, with some of the best talent in the data visualization community participating each week. This book compiles years of learning and hundreds of visualizations from that project into a practical guide that will prove essential for anyone working with data."</br> <b> —Jeffrey A. Shaffer,</b> Co-author of <i>The Big Book of Dashboards,</i> Adjunct Professor teaching Data Visualization at the University of Cincinnati <p>"Real-word case studies? Captivating visualizations? Practical career tips? This book has it all. Phenomenal."<br/> <b>—Kirill Eremenko,</b> CEO, SuperDataScience

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