[Télécharger] Practical Data Science: A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets de Andreas Francois Vermeulen En Ligne
Télécharger Practical Data Science: A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets de Andreas Francois Vermeulen Francais PDF

Télécharger "Practical Data Science: A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets" de Andreas Francois Vermeulen Francais PDF
Auteur : Andreas Francois Vermeulen
Catégorie : Livres anglais et étrangers,Business & Investing,Industries & Professions
Broché : * pages
Éditeur : *
Langue : Français, Anglais
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 LearnBecome fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteriaMaster the methods for retrieving actionable business knowledgeCoordinate the handling of polyglot data types in a data lake for repeatable resultsWho 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
Télécharger Practical Data Science: A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets de Andreas Francois Vermeulen Livres En Ligne
What is a data lake? ~ Data Lakes allow you to store relational data like operational databases and data from line of business applications, and non-relational data like mobile apps, IoT devices, and social media. They also give you the ability to understand what data is in the lake through crawling, cataloging, and indexing of data. Finally, data must be secured to ensure your data assets are protected.
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