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Beautiful Data by Toby Segaran, Jeff Hammerbacher

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Preface

WHEN WE WERE FIRST APPROACHED WITH THE IDEA OF A FOLLOW-UP TO BEAUTIFUL CODE, THIS TIME about data, we found the idea exciting and very ambitious. Collecting, visualizing, and processing data now touches every professional field and so many aspects of daily life that a great collection would have to be almost unreasonably broad in scope. So we contacted a highly diverse group of people whose work we admired, and were thrilled that so many agreed to contribute.

This book is the result, and we hope it captures just how wide-ranging (and beautiful) working with data can be. In it you'll learn about everything from fighting with governments to working with the Mars lander; you'll learn how to use statistics programs, make visualizations, and remix a Radiohead video; you'll see maps, DNA, and something we can only really call "data philosophy."

The royalties for this book are being donated to Creative Commons and the Sunlight Foundation, two organizations dedicated to making the world better by freeing data. We hope you'll consider how your own encounters with data shape the world.

How This Book Is Organized

The chapters in this book follow a loose arc from data collection through data storage, organization, retrieval, visualization, and finally, analysis.

Chapter 1, Seeing Your Life in Data, by Nathan Yau, looks at the motivations and challenges behind two projects in the emerging field of personal data collection.

Chapter 2, The Beautiful People: Keeping Users in Mind When Designing Data Collection Methods, by Jonathan Follett and Matthew Holm, discusses the importance of trust, persuasion, and testing when collecting data from humans over the Web.

Chapter 3, Embedded Image Data Processing on Mars, by J. M. Hughes, discusses the challenges of designing a data processing system that has to work within the constraints of space travel.

Chapter 4, Cloud Storage Design in a PNUTShell, by Brian F. Cooper, Raghu Ramakrishnan, and Utkarsh Srivastava, describes the software Yahoo! has designed to turn its globally distributed data centers into a universal storage platform for powering modern web applications.

Chapter 5, Information Platforms and the Rise of the Data Scientist, by Jeff Hammerbacher, traces the evolution of tools for information processing and the humans who power them, using specific examples from the history of Facebook's data team.

Chapter 6, The Geographic Beauty of a Photographic Archive, by Jason Dykes and Jo Wood, draws attention to the ubiquity and power of colorfully visualized spatial data collected by a volunteer community.

Chapter 7, Data Finds Data, by Jeff Jonas and Lisa Sokol, explains a new approach to thinking about data that many may need to adopt in order to manage it all.

Chapter 8, Portable Data in Real Time, by Jud Valeski, dives into the current limitations of distributing social and location data in real time across the Web, and discusses one potential solution to the problem.

Chapter 9, Surfacing the Deep Web, by Alon Halevy and Jayant Madhavan, describes the tools developed by Google to make searchable the data currently trapped behind forms on the Web.

Chapter 10, Building Radiohead's House of Cards, by Aaron Koblin with Valdean Klump, is an adventure story about lasers, programming, and riding on the back of a bus, and ending with an award-winning music video.

Chapter 11, Visualizing Urban Data, by Michal Migurski, details the process of freeing and beautifying some of the most important data about the world around us.

Chapter 12, The Design of Sense.us, by Jeffrey Heer, recasts data visualizations as social spaces and uses this new perspective to explore 150 years of U.S. census data.

Chapter 13, What Data Doesn't Do, by Coco Krumme, looks at experimental work that demonstrates the many ways people misunderstand and misuse data.

Chapter 14, Natural Language Corpus Data, by Peter Norvig, takes the reader through some evocative exercises with a trillion-word corpus of natural language data pulled down from across the Web.

Chapter 15, Life in Data: The Story of DNA, by Matt Wood and Ben Blackburne, describes the beauty of the data that is DNA and the massive infrastructure required to create, capture, and process that data.

Chapter 16, Beautifying Data in the Real World, by Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen, shows how crowdsourcing and extreme transparency have combined to advance the state of drug discovery research.

Chapter 17, Superficial Data Analysis: Exploring Millions of Social Stereotypes, by Brendan O'Connor and Lukas Biewald, shows the correlations and patterns that emerge when people are asked to anonymously rate one another's pictures.

Chapter 18, Bay Area Blues: The Effect of the Housing Crisis, by Hadley Wickham, Deborah F. Swayne, and David Poole, guides the reader through a detailed examination of the recent housing crisis in the Bay Area using open source software and publicly available data.

Chapter 19, Beautiful Political Data, by Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza, shows how the tools of statistics and data visualization can help us gain insight into the political process used to organize society.

Chapter 20, Connecting Data, by Toby Segaran, explores the difficulty and possibilities of joining together the vast number of data sets the Web has made available.

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