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|a 9781484230541
|9 978-1-4842-3054-1
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|a 10.1007/978-1-4842-3054-1
|2 doi
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|a 006.312
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|a Vermeulen, Andreas François.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Practical Data Science
|h [electronic resource] :
|b A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets /
|c by Andreas François Vermeulen.
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| 250 |
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|a 1st ed. 2018.
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|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2018.
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| 300 |
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|a XXV, 805 p. 57 illus., 9 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a Chapter 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 -- .
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|a Learn 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.
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| 650 |
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|a Data mining.
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| 650 |
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|a Big data.
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| 650 |
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|a Data structures (Computer science).
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| 650 |
1 |
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|a Data Mining and Knowledge Discovery.
|0 http://scigraph.springernature.com/things/product-market-codes/I18030
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| 650 |
2 |
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|a Big Data/Analytics.
|0 http://scigraph.springernature.com/things/product-market-codes/522070
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| 650 |
2 |
4 |
|a Big Data.
|0 http://scigraph.springernature.com/things/product-market-codes/I29120
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| 650 |
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|a Data Storage Representation.
|0 http://scigraph.springernature.com/things/product-market-codes/I15025
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| 710 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9781484230534
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| 776 |
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|i Printed edition:
|z 9781484230558
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| 776 |
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8 |
|i Printed edition:
|z 9781484248218
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| 856 |
4 |
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|u https://doi.org/10.1007/978-1-4842-3054-1
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-CWD
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| 950 |
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|a Professional and Applied Computing (Springer-12059)
|
