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…mehr

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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
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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. Jeff Hammerbacher is the Vice President of Products and Chief Scientist at Cloudera. Jeff was an Entrepreneur in Residence at Accel Partners immediately prior to joining Cloudera. Before Accel, he conceived, built, and led the Data team at Facebook. The Data team was responsible for driving many of the statistics and machine learning applications at Facebook, as well as building out the infrastructure to support these tasks for massive data sets. The team produced several academic papers and two open source projects: Hive, a system for offline analysis built above Hadoop, and Cassandra, a structured storage system on a P2P network. Before joining Facebook, Jeff was a quantitative analyst on Wall Street. Jeff earned his Bachelor's Degree in Mathematics from Harvard University.