Overview
- 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
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Table of contents (11 chapters)
Keywords
- 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
About this book
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
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Practical Data Science
Book Subtitle: A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets
Authors: Andreas François Vermeulen
DOI: https://doi.org/10.1007/978-1-4842-3054-1
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Andreas Fran�ois Vermeulen 2018
Softcover ISBN: 978-1-4842-3053-4Published: 22 February 2018
eBook ISBN: 978-1-4842-3054-1Published: 21 February 2018
Edition Number: 1
Number of Pages: XXV, 805
Number of Illustrations: 48 b/w illustrations, 9 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Big Data/Analytics, Big Data, Data Storage Representation