Sprache: Englisch
Verlag: O'Reilly Media (edition 1), 2011
ISBN 10: 1449309925 ISBN 13: 9781449309923
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Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
Sprache: Englisch
Verlag: O'Reilly Media, Incorporated, 2012
ISBN 10: 1449321887 ISBN 13: 9781449321888
Anbieter: Better World Books: West, Reno, NV, USA
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Zustand: New. R is a wonderful thing, indeed: in recent years this free, open-source product has become a popular toolkit for statistical analysis and programming. Two of R's limitations -- that it is single-threaded and memory-bound -- become especially troublesome in the current era of large-scale data analysis. Num Pages: 122 pages, Illustrations. BIC Classification: UMX. Category: (XV) Technical / Manuals. Dimension: 233 x 178 x 8. Weight in Grams: 244. . 2011. Paperback. . . . . Books ship from the US and Ireland.
Zustand: New. 2012. 1st Edition. Paperback. Welcome to data science's dirty secret: real-world data is messy. Data scientists must spend a good deal of time playing software developer, writing code to clean up data before they can actually do anything constructive with it. This is a necessary evil, but we can still make the most of it. Num Pages: 264 pages, Illustrations. BIC Classification: UNA. Category: (XV) Technical / Manuals. Dimension: 231 x 178 x 14. Weight in Grams: 442. . . . . . Books ship from the US and Ireland.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbKartoniert / Broschiert. Zustand: New. R is a wonderful thing, indeed: in recent years this free, open-source product has become a popular toolkit for statistical analysis and programming. Two of R s limitations -- that it is single-threaded and memory-bound -- become especially troublesome in .
Taschenbuch. Zustand: Neu. Neuware - It's tough to argue with R as a high-quality, cross-platform, open source statistical software productunless you're in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You'll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they don't.With these packages, you can overcome R's single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R's memory barrier.\* Snow: works well in a traditional cluster environment\* Multicore: popular for multiprocessor and multicore computers\* Parallel: part of the upcoming R 2.14.0 release\* R+Hadoop: provides low-level access to a popular form of cluster computing\* RHIPE: uses Hadoop's power with R's language and interactive shell\* Segue: lets you use Elastic MapReduce as a backend for lapply-style operations.
EUR 43,62
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In den WarenkorbZustand: New. Welcome to data science s dirty secret: real-world data is messy. Data scientists must spend a good deal of time playing software developer, writing code to clean up data before they can actually do anything constructive with it. This is a necessary evil, b.
Taschenbuch. Zustand: Neu. Neuware - What is bad data Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they've recovered from nasty data problems.From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line Bad data is data that gets in the way. This book explains effective ways to get around it.Among the many topics covered, you'll discover how to: Test drive your data to see if it's ready for analysis Work spreadsheet data into a usable form Handle encoding problems that lurk in text data Develop a successful web-scraping effort Use NLP tools to reveal the real sentiment of online reviews Address cloud computing issues that can impact your analysis effort Avoid policies that create data analysis roadblocks Take a systematic approach to data quality analysis.