Practical Data Science                                                A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets                                                                                                                                                                                                                                     
                                                                                                               Author: Andreas François VermeulenISBN-10: 1484230531Year: 2018Pages: 805Language: EnglishFile size: 7.7 MBFile format: PDFCategory: Data Mining                                 
                                               
                                                                                                                                                                                            Book Description:                                                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
  

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