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Practical data sciencea guide to bui...

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Practical data sciencea guide to building the technology stack for turning data lakes into business assets /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practical data scienceby Andreas Francois Vermeulen.
其他題名:	a guide to building the technology stack for turning data lakes into business assets /
作者:	Vermeulen, Andreas Francois.
出版者:	Berkeley, CA :Apress :2018.
面頁冊數:	xxv, 805 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Data structures (Computer science)
電子資源:	http://dx.doi.org/10.1007/978-1-4842-3054-1
ISBN:	9781484230541$q(electronic bk.)

Practical data sciencea 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$q(electronic bk.)

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

183917
Data structures (Computer science)

LC Class. No.: QA76.9.D35

Dewey Class. No.: 005.73

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