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Practical data science = a guide to ...

Vermeulen, Andreas Francois.

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Practical data science = a guide to building the technology stack for turning data lakes into business assets /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Practical data science/ by Andreas Francois Vermeulen.
Reminder of title:	a guide to building the technology stack for turning data lakes into business assets /
Author:	Vermeulen, Andreas Francois.
Published:	Berkeley, CA :Apress : : 2018.,
Description:	xxv, 805 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Data structures (Computer science) -
Online resource:	http://dx.doi.org/10.1007/978-1-4842-3054-1
ISBN:	9781484230541

Practical data science = a guide to building the technology stack for turning data lakes into business assets /
Vermeulen, Andreas Francois.

Practical data sciencea guide to building the technology stack for turning data lakes into business assets /[electronic resource] :by Andreas Francois Vermeulen. - Berkeley, CA :Apress :2018. - xxv, 805 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9781484230541

Standard No.: 10.1007/978-1-4842-3054-1doiSubjects--Topical Terms:

527210
Data structures (Computer science)

LC Class. No.: QA76.9.D35

Dewey Class. No.: 005.73

Practical data science = a guide to building the technology stack for turning data lakes into business assets /
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505 0 $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.
520 $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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