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010   $a9781484230541
010   $a148423054X
024 7 $a10.1007/978-1-4842-3054-1
035   $a(CKB)4100000002485253
035   $a(MiAaPQ)EBC5307290
035   $a(DE-He213)978-1-4842-3054-1
035   $a(CaSebORM)9781484230541
035   $a(PPN)22464100X
035   $a(OCoLC)1028639698
035   $a(OCoLC)on1028639698
035   $a(EXLCZ)994100000002485253
100   $a20180221d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $2rdacontent
182   $2rdamedia
183   $2rdacarrier
200 10$aPractical Data Science $eA Guide to Building the Technology Stack for Turning Data Lakes into Business Assets /$fby Andreas François Vermeulen
205   $a1st ed. 2018.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2018.
215   $a1 online resource (824 pages)
300   $aIncludes index.
311 08$a9781484230534 
311 08$a1484230531 
327   $aChapter 1: Data Science Technology Stack -- Chapter 2: Vermeulen - Krennwallner - Hillman - Clark -- Chapter 3: Layered Framework -- Chapter 4: Business Layer -- Chapter 5: Utility Layer -- Chapter 6: Three Management Layers -- Chapter 7: Retrieve Super Step -- Chapter 8: Assess Super Step -- Chapter 9: Process Super Step -- Chapter 10: Transform Super Step -- Chapter 11: Organize and Report Super Step -- .
330   $aLearn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn: Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results.
606   $aData mining
606   $aBig data
606   $aData structures (Computer science)
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
606   $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aData Storage Representation$3https://scigraph.springernature.com/ontologies/product-market-codes/I15025
615  0$aData mining.
615  0$aBig data.
615  0$aData structures (Computer science)
615 14$aData Mining and Knowledge Discovery.
615 24$aBig Data/Analytics.
615 24$aBig Data.
615 24$aData Storage Representation.
676   $a005.73
700   $aVermeulen$b Andreas Franc?ois$4aut$4http://id.loc.gov/vocabulary/relators/aut$00
801  0$bUMI
801  1$bUMI
906   $aBOOK
912   $a9910300364903321
996   $aPractical Data Science$92519497
997   $aUNINA