| Record ID | marc_openlibraries_sanfranciscopubliclibrary/sfpl_chq_2018_12_24_run06.mrc:14097345:3419 |
| Source | marc_openlibraries_sanfranciscopubliclibrary |
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003 OCoLC
005 20170825110521.0
008 161003s2017 caua b 001 0 eng d
019 $a961160705
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020 $a9781491972953
035 $a(OCoLC)959596273$z(OCoLC)961160705
037 $bOreilly & Associates Inc, C/O Ingram Pub Services 1 Ingram Blvd, LA Vergne, TN, USA, 37086$nSAN 631-8673
040 $aYDX$beng$erda$cYDX$dOCLCQ$dBTCTA$dCLE$dEYM$dSFR
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050 4 $aQA76.9.D343$bR93 2017
082 04 $a006.3/12$223
092 $a006.312$bSp261r 2017
100 1 $aRyza, Sandy,$eauthor.
245 10 $aAdvanced analytics with Spark :$bpatterns for learning from data at scale /$cSandy Ryza, Uri Laserson, Sean Owen, and Josh Wills.
250 $aSecond edition.
264 1 $aSebastopol, CA :$bO'Reilly,$c2017.
300 $axii, 264 pages :$billustrations ;$c24 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
504 $aIncludes bibliographical references and index.
505 0 $aAnalyzing big data -- Introduction to data analysis with Scala and Spark -- Recommending music and the audioscrobbler data set -- Predicting forest cover with decision trees -- Anomaly detection in network traffic with K-means clustering -- Understanding Wikipedia with latent semantic analysis -- Analyzing co-occurrence networks with GraphX -- Geospatial and temporal data analysis on the New York City taxi trip data -- Estimating financial risk through Monte Carlo simulation -- Analyzing genomics data and the BDG project -- Analyzing neuroimaging data with PySpark and Thunder.
520 $aThe authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by presenting examples and a set of self-contained patterns for performing large-scale data analysis with Spark. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for working on your own data applications.
630 00 $aSpark (Electronic resource : Apache Software Foundation)
650 0 $aBig data.
650 0 $aData mining$xComputer programs.
700 1 $aLaserson, Uri,$d1983-$eauthor.
700 1 $aOwen, Sean,$eauthor.
700 1 $aWills, Josh,$eauthor.
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