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

ISBN-13: 9781484230534 / Angielski / Miękka / 2018 / 805 str.

Andreas Francois Vermeulen

O autorze

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.

Blurb

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