Beautiful Data - Segaran, Toby; Hammerbacher, Jeff

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With this unique book, programmers, administrators, and others who handle data can learn by example from the best data practitioners in the history of the field. Modeled after O'Reilly's highly-acclaimed book, Beautiful Code, Beautiful Data lets readers look over the shoulders of prominent data designers, managers, and handlers for a glimpse into some of the most interesting projects involving data. In an engaging narrative format, the authors think aloud as they explain their work, highlighting the simple and elegant solutions to problems they encountered along the way.
The stories in
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Produktbeschreibung
With this unique book, programmers, administrators, and others who handle data can learn by example from the best data practitioners in the history of the field. Modeled after O'Reilly's highly-acclaimed book, Beautiful Code, Beautiful Data lets readers look over the shoulders of prominent data designers, managers, and handlers for a glimpse into some of the most interesting projects involving data. In an engaging narrative format, the authors think aloud as they explain their work, highlighting the simple and elegant solutions to problems they encountered along the way.

The stories in Beautiful Data cover every facet of data acquisition, storage, retrieval, management, manipulation, and visualization. You'll find important lessons as well as best practices for everything from scientific data to financial and institutional data, technical data, and government data. This is a truly fascinating book for anyone interested in the history of modern computing.
In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video. With Beautiful Data, you will: * Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web * Learn how to visualize trends in urban crime, using maps and data mashups * Discover the challenges of designing a data processing system that works within the constraints of space travel * Learn how crowdsourcing and transparency have combined to advance the state of drug research * Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data * Learn about the massive infrastructure required to create, capture, and process DNA data That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include: * Nathan Yau * Jonathan Follett and Matt Holm * J.M. Hughes * Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava * Jeff Hammerbacher * Jason Dykes and Jo Wood * Jeff Jonas and Lisa Sokol * Jud Valeski * Alon Halevy and Jayant Madhavan * Aaron Koblin with Valdean Klump * Michal Migurski * Jeff Heer * Coco Krumme * Peter Norvig * Matt Wood and Ben Blackburne * Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen * Lukas Biewald and Brendan O'Connor * Hadley Wickham, Deborah Swayne, and David Poole * Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza * Toby Segaran
  • Produktdetails
  • Verlag: O'Reilly Media, Inc. / O'Reilly UK Ltd.
  • Seitenzahl: 382
  • Erscheinungstermin: August 2009
  • Englisch
  • Abmessung: 235mm x 179mm x 25mm
  • Gewicht: 614g
  • ISBN-13: 9780596157111
  • ISBN-10: 0596157118
  • Artikelnr.: 26009568
Autorenporträt
Toby Segaran is the author of Programming Collective Intelligence, a very popular O'Reilly title. He was the founder of Incellico, a biotech software company later acquired by Genstruct. He currently holds the title of Data Magnate at Metaweb Technologies and is a frequent speaker at technology conferences.
Inhaltsangabe
Inhaltsverzeichnis
Chapter 1 Seeing Your Life in Data
Personal Environmental Impact Report (PEIR)
your.flowingdata (YFD)
Personal Data Collection
Data Storage
Data Processing
Data Visualization
The Point
How to Participate
Chapter 2 The Beautiful People: Keeping Users in Mind When Designing Data Collection Methods
Introduction: User Empathy Is the New Black
The Project: Surveying Customers About a New Luxury Product
Specific Challenges to Data Collection
Designing Our Solution
Results and Reflection
Chapter 3 Embedded Image Data Processing on Mars
Abstract
Introduction
Some Background
To Pack or Not to Pack
The Three Tasks
Slotting the Images
Passing the Image: Communication Among the Three Tasks
Getting the Picture: Image Download and Processing
Image Compression
Downlink, or, It's All Downhill from Here
Conclusion
Chapter 4 Cloud Storage Design in a PNUTShell
Introduction
Updating Data
Complex Queries
Comparison with Other Systems
Conclusion
Acknowledgments
References
Chapter 5 Information Platforms and the Rise of the Data Scientist
Libraries and Brains
Facebook Becomes Self-Aware
A Business Intelligence System
The Death and Rebirth of a Data Warehouse
Beyond the Data Warehouse
The Cheetah and the Elephant
The Unreasonable Effectiveness of Data
New Tools and Applied Research
MAD Skills and Cosmos
Information Platforms As Dataspaces
The Data Scientist
Conclusion
Chapter 6 The Geographic Beauty of a Photographic Archive
Beauty in Data: Geograph
Visualization, Beauty, and Treemaps
A Geographic Perspective on Geograph Term Use
Beauty in Discovery
Reflection and Conclusion
Acknowledgments
References
Chapter 7 Data Finds Data
Introduction
The Benefits of Just-in-Time Discovery
Corruption at the Roulette Wheel
Enterprise Discoverability
Federated Search Ain't All That
Directories: Priceless
Relevance: What Matters and to Whom?
Components and Special Considerations
Privacy Considerations
Conclusion
Chapter 8 Portable Data in Real Time
Introduction
The State of the Art
Social Data Normalization
Conclusion: Mediation via Gnip
Chapter 9 Surfacing the Deep Web
What Is the Deep Web?
Alternatives to Offering Deep-Web Access
Conclusion and Future Work
References
Chapter 10 Building Radiohead's House of Cards
How It All Started
The Data Capture Equipment
The Advantages of Two Data Capture Systems
The Data
Capturing the Data, aka "The Shoot"
Processing the Data
Post-Processing the Data
Launching the Video
Conclusion
Chapter 11 Visualizing Urban Data
Introduction
Background
Cracking the Nut
Making It Public
Revisiting
Conclusion
Chapter 12 The Design of Sense.us
Visualization and Social Data Analysis
Data
Visualization
Collaboration
Voyagers and Voyeurs
Conclusion
References
Chapter 13 What Data Doesn't Do
When Doesn't Data Drive?
Conclusion
References
Chapter 14 Natural Language Corpus Data
Word Segmentation
Secret Codes
Spelling Correction
Other Tasks
Discussion and Conclusion
Acknowledgments
Chapter 15 Life in Data: The Story of DNA
DNA As a Data Store
DNA As a Data Source
Fighting the Data Deluge
The Future of DNA
Acknowledgments
Chapter 16 Beautifying Data in the Real World
The Problem with Real Data
Providing the Raw Data Back to the Notebook
Validating Crowdsourced Data
Representing the Data Online
Closing the Loop: Visualizations to Suggest New Experiments
Building a Data Web from Open Data and Free Services
Acknowledgments
References
Chapter 17 Superficial Data Analysis: Exploring Millions of Social Stereotypes
Introduction
Preprocessing the Data
Exploring the Data
Age, Attractiveness, and Gender
Looking at Tags
Which Words Are Gendered?
Clustering
Conclusion
Acknowledgments
References
Chapter 18 Bay Area Blues: The Effect of the Housing Crisis
Introduction
How Did We Get the Data?
Geocoding
Data Checking
Analysis
The Influence of Inflation
The Rich Get Richer and the Poor Get Poorer
Geographic Differences
Census Information
Exploring San Francisco
Conclusion
References
Chapter 19 Beautiful Political Data
Example 1: Redistricting and Partisan Bias
Example 2: Time Series of Estimates
Example 3: Age and Voting
Example 4: Public Opinion and Senate Voting on Supreme Court Nominees
Example 5: Localized Partisanship in Pennsylvania
Conclusion
References
Chapter 20 Connecting Data
What Public Data Is There, Really?
The Possibilities of Connected Data
Within Companies
Impediments to Connecting Data
Possible Solutions
Conclusion
Appendix Contributors
COLOPHON