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005 20200520144314.0
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010   $a1-119-23888-9
010   $a1-119-18138-0
035   $a(CKB)3710000000531774
035   $a(EBL)4189588
035   $a(SSID)ssj0001634870
035   $a(PQKBManifestationID)16388472
035   $a(PQKBTitleCode)TC0001634870
035   $a(PQKBWorkID)14951017
035   $a(PQKB)10684969
035   $a(Au-PeEL)EBL4189588
035   $a(CaPaEBR)ebr11129201
035   $a(CaONFJC)MIL881790
035   $a(OCoLC)933596743
035   $a(CaSebORM)9781119181118
035   $a(MiAaPQ)EBC4189588
035   $z(PPN)272708089
035   $a(PPN)254346650
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100   $a20160104h20162016  uy             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aBig data MBA $edriving business strategies with data science /$fBill Schmarzo
210  1$aIndianapolis, Indiana :$cWiley,$d2016.
210  4$dİ2016
215   $a1 online resource (275 p.)
300   $aDescription based upon print version of record.
311   $a1-119-18111-9 
327   $aBusiness potential of big data. The big data business mandate -- Big data business model maturity index -- The big data strategy document -- The importance of the user experience -- Data science. Differences between business intelligence and data science -- Data science 101 -- The data lake -- Data science for business stakeholders. Thinking like a data scientist -- "By" analysis technique -- Score development technique -- Monetization exercise -- Metamorphosis exercise -- Building cross-organizational support -- Power of envisioning -- Organizational ramifications -- Stories.
606   $aBusiness intelligence$xData processing
606   $aBig data
606   $aBusiness planning$xStatistical methods
606   $aData mining
615  0$aBusiness intelligence$xData processing.
615  0$aBig data.
615  0$aBusiness planning$xStatistical methods.
615  0$aData mining.
676   $a658.4038
700   $aSchmarzo$b Bill$0986632
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910137168103321
996   $aBig data MBA$92285161
997   $aUNINA