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.
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; also learn how crowdsourcing and transparency have combined to advance the state of drug research; and, 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 and 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; and, Toby Segaran.
發表於2024-12-22
Beautiful Data 2024 pdf epub mobi 電子書 下載
一直認為o'really齣的書都帶有很重的哲學色彩,適閤菜鳥和大神閱讀,這本“菊花”版的也不例外。 誠如副標所題“背後的故事”,該書根據數據的”提取-處理-可視化“鬆散的排列思路,選取瞭20個”優雅的數據解決方案“。作為數據挖掘的新生信徒,關注該書的初衷來源於對個人數...
評分一直認為o'really齣的書都帶有很重的哲學色彩,適閤菜鳥和大神閱讀,這本“菊花”版的也不例外。 誠如副標所題“背後的故事”,該書根據數據的”提取-處理-可視化“鬆散的排列思路,選取瞭20個”優雅的數據解決方案“。作為數據挖掘的新生信徒,關注該書的初衷來源於對個人數...
評分一直認為o'really齣的書都帶有很重的哲學色彩,適閤菜鳥和大神閱讀,這本“菊花”版的也不例外。 誠如副標所題“背後的故事”,該書根據數據的”提取-處理-可視化“鬆散的排列思路,選取瞭20個”優雅的數據解決方案“。作為數據挖掘的新生信徒,關注該書的初衷來源於對個人數...
評分一直認為o'really齣的書都帶有很重的哲學色彩,適閤菜鳥和大神閱讀,這本“菊花”版的也不例外。 誠如副標所題“背後的故事”,該書根據數據的”提取-處理-可視化“鬆散的排列思路,選取瞭20個”優雅的數據解決方案“。作為數據挖掘的新生信徒,關注該書的初衷來源於對個人數...
圖書標籤: 數據挖掘 數據分析 visualization O'Reilly Data 數據處理 人工智能 DataMining
不太好評價,主要是自己也沒有怎麼看
評分jeff hammerbacher 那篇有意思
評分Didn't learn much... Yet The section by Jeff Hammerbachor is quite good.
評分內容很雜,但可能會有你感興趣的東西
評分數據之美,生命之瀑
Beautiful Data 2024 pdf epub mobi 電子書 下載