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Practical Data Science: A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets de Andreas François Vermeulen

Practical Data Science: A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets

Autor Andreas François Vermeulen
en Limba Engleză Paperback – 22 feb 2018
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 ofpolyglot data types in a data lake for repeatable results
Who This Book Is For

Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers
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Specificații

ISBN-13: 9781484230534
ISBN-10: 1484230531
Pagini: 260
Ilustrații: XXV, 805 p. 57 illus., 9 illus. in color.
Dimensiuni: 178 x 254 x 50 mm
Greutate: 1.42 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

Cuprins

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.- 

Notă biografică

Andreas François Vermeulen is Consulting Manager - Business Intelligence, Big Data, Data Science, Machine Learning, and Computational Analytics at Sopra-Steria, and a doctoral researcher at University St. Andrews on future concepts in massive distributed computing, mechatronics, big data, business intelligence, and deep learning. He owns and incubates the “Rapid Information Factory” data processing framework. He is active in developing next-generation processing frameworks and mechatronics engineering with over 35 years of international experience in data processing, software development, and system architecture. Andre is a data scientist, doctoral trainer, corporate consultant, principal systems architect, and speaker/author/columnist on data science, distributed computing, big data, business intelligence, deep learning, and constraint programming. Andre received his bachelor degree at the North West University at Potchefstroom, his Master of Business Administration at University of Manchester, Master of Business Intelligence and Data Science degree at University of Dundee, and Doctor of Philosophy at University of St Andrews.

Textul de pe ultima copertă

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

Caracteristici

Provides the essential concepts and terminology to gain fluency in data science and data engineering Walks through the steps of building a technology stack on a layered framework to retrieve actionable business knowledge Teaches how to synthesize the polyglot data types in a data lake with repeatable results