Practical Data Science

1. Front Matter

   Pages i-xxv

   PDF

2. Data Science Technology Stack

   • Andreas François Vermeulen

   Pages 1-13

3. Vermeulen-Krennwallner-Hillman-Clark

   • Andreas François Vermeulen

   Pages 15-38

4. Layered Framework

   • Andreas François Vermeulen

   Pages 39-51

5. Business Layer

   • Andreas François Vermeulen

   Pages 53-83

6. Utility Layer

   • Andreas François Vermeulen

   Pages 85-117

7. Three Management Layers

   • Andreas François Vermeulen

   Pages 119-145

8. Retrieve Superstep

   • Andreas François Vermeulen

   Pages 147-273

9. Assess Superstep

   • Andreas François Vermeulen

   Pages 275-420

10. Process Superstep

    • Andreas François Vermeulen

    Pages 421-526

11. Transform Superstep

    • Andreas François Vermeulen

    Pages 527-684

12. Organize and Report Supersteps

    • Andreas François Vermeulen

    Pages 685-786

13. Back Matter

    Pages 787-805

    PDF

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

• data science
• polyglot data science
• data engineering
• data lake
• data vault and data mart
• data warehouse bus matrix
• data scrubbing techniques
• data science technology stack
• actionable business knowledge
• Spark, Mesos, Akka, Cassandra, Kafka, Elasticsearch, R
• machine-to-machine
• machine learning
• IoT and embedded systems
• fog computing
• MQTT
• graph database
• super steps of the functional layer
• grids and clusters
• torus network

• West Kilbride North Ayrshire, United Kingdom

  Andreas François Vermeulen

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.

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