Beautiful Data pdf epub mobi txt 电子书 下载 2024


Beautiful Data

简体网页||繁体网页
Toby Segaran
O'Reilly Media
2009-7-31
386
USD 44.99
Paperback
Beautiful X Series
9780596157111

图书标签: 数据挖掘  数据分析  visualization  O'Reilly  Data  数据处理  人工智能  DataMining   


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发表于2024-12-23

Beautiful Data epub 下载 mobi 下载 pdf 下载 txt 电子书 下载 2024

Beautiful Data epub 下载 mobi 下载 pdf 下载 txt 电子书 下载 2024

Beautiful Data pdf epub mobi txt 电子书 下载 2024



图书描述

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.

Beautiful Data 下载 mobi epub pdf txt 电子书

著者简介

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.


图书目录


Beautiful Data pdf epub mobi txt 电子书 下载
想要找书就要到 小哈图书下载中心
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用户评价

评分

对其他领域说welcome的IT专业书,很受启发,但还是做不来geek,有些章节对我太晦涩看不懂,不过还是觉得,data is beautiful

评分

内容很杂,但可能会有你感兴趣的东西

评分

大数据的故事书,浅显易懂且富有启发性。

评分

不太好评价,主要是自己也没有怎么看

评分

amazing book, talking about the most cutting-edge applications in industry.

读后感

评分

总的来说,这本书有点老旧了,只能用来借鉴,2010年的书籍,放在现在依然能够得到一部分人的认可,说明了一些问题:表现形式在变化,但内在的分析逻辑没有变化,大家依然遵循着简单,高效的做事逻辑,在分析当下的数据。 目前数据维度更高,数据错综复杂,不再是所谓的业务和领...  

评分

一直认为o'really出的书都带有很重的哲学色彩,适合菜鸟和大神阅读,这本“菊花”版的也不例外。 诚如副标所题“背后的故事”,该书根据数据的”提取-处理-可视化“松散的排列思路,选取了20个”优雅的数据解决方案“。作为数据挖掘的新生信徒,关注该书的初衷来源于对个人数...

评分

一直认为o'really出的书都带有很重的哲学色彩,适合菜鸟和大神阅读,这本“菊花”版的也不例外。 诚如副标所题“背后的故事”,该书根据数据的”提取-处理-可视化“松散的排列思路,选取了20个”优雅的数据解决方案“。作为数据挖掘的新生信徒,关注该书的初衷来源于对个人数...

评分

总的来说,这本书有点老旧了,只能用来借鉴,2010年的书籍,放在现在依然能够得到一部分人的认可,说明了一些问题:表现形式在变化,但内在的分析逻辑没有变化,大家依然遵循着简单,高效的做事逻辑,在分析当下的数据。 目前数据维度更高,数据错综复杂,不再是所谓的业务和领...  

评分

一直认为o'really出的书都带有很重的哲学色彩,适合菜鸟和大神阅读,这本“菊花”版的也不例外。 诚如副标所题“背后的故事”,该书根据数据的”提取-处理-可视化“松散的排列思路,选取了20个”优雅的数据解决方案“。作为数据挖掘的新生信徒,关注该书的初衷来源于对个人数...

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