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Data science with Java : practical methods for scientists and engineers

By: Material type: TextTextPublication details: Mumbai : Shroff Publishers, 2017Edition: First editionDescription: xii, 220 pages : illustrationsISBN:
  • 9781491934111
  • 9789352135738
Subject(s): DDC classification:
  • 005.133 BRZ
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. --
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Item type Current library Collection Call number Status Date due Barcode Item holds
Lending Books Lending Books Main Library Stacks REF 005.133 BRZ (Browse shelf(Opens below)) Available 015582
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Originally published in Sebastopol, CA. by O'Reilly Media

Includes index.

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

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. --

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