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Data science with Java : practical methods for scientists and engineers / Michael R. Brzustowicz, PhD.

By: Material type: TextTextPublisher: Sebastopol, CA : O'Reilly Media, 2017Copyright date: ©2017Edition: First editionDescription: xii, 220 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781491934111
  • 1491934115
Subject(s):
Contents:
Data I/O -- Linear algebra -- Statistics -- Data operations -- Learning and prediction -- Hadoop MapReduce -- Datasets.
Summary: Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications. -- Provided by publisher.
List(s) this item appears in: Computer Science & Coding for Adults
Holdings
Item type Home library Collection Call number Materials specified Status Date due Barcode Item holds
Adult Book Adult Book Main Library NonFiction 005.133 B916 Available 33111009235462
Total holds: 0

Enhanced descriptions from Syndetics:

Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java.

You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications.

Examine methods for obtaining, cleaning, and arranging data into its purest form Understand the matrix structure that your data should take Learn basic concepts for testing the origin and validity of data Transform your data into stable and usable numerical values Understand supervised and unsupervised learning algorithms, and methods for evaluating their success Get up and running with MapReduce, using customized components suitable for data science algorithms

Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications. -- Provided by publisher.

Includes index.

Data I/O -- Linear algebra -- Statistics -- Data operations -- Learning and prediction -- Hadoop MapReduce -- Datasets.

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