Description

Book Synopsis
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

    Table of Contents


Practical Data Science

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£41.24

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RRP £54.99 – you save £13.75 (25%)

Order before 4pm tomorrow for delivery by Fri 9 Jan 2026.

A Paperback / softback by Andreas François Vermeulen

1 in stock


    View other formats and editions of Practical Data Science by Andreas François Vermeulen

    Publisher: APress
    Publication Date: 22/02/2018
    ISBN13: 9781484230534, 978-1484230534
    ISBN10: 1484230531

    Description

    Book Synopsis
    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

      Table of Contents


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