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Java for Data Science Cover

Java for Data Science

Examine the techniques and Java tools supporting the growing field of data science

Publisher:Packt Publishing Limited
By: Richard M. Reese and  Jennifer L. Reese  
Paid access
|Sep 2025
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Examine the techniques and Java tools supporting the growing field of data science

Key Features

  • Your entry ticket to the world of data science with the stability and power of Java
  • Explore, analyse, and visualize your data effectively using easy-to-follow examples
  • Make your Java applications more capable using machine learning

Book Description

para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions

What you will learn

    Who this book is for

    With its tutorial approach, this data science book has been written for experienced Java programmers who want to better understand the field of data science and learn how Java supports its underlying techniques. The step-by-step instructional style also makes Java for Data Science ideal for beginners, allowing you to get up and running quickly.

    Table of Contents

    1. Getting started with Data Science
    2. Data Acquisition
    3. Data Cleaning
    4. Data Visualization
    5. Statistical Data Analysis Techniques
    6. Machine Learning
    7. Neural Networks
    8. Deep Learning
    9. Text Analysis
    10. Visual and Audio Analysis
    11. Parallel Techniques for Data Analysis
    12. Bringing It All Together
    https://github.com/packtpublishing/java-for-data-science
    PDF ISBN: 978-1-78528-124-2
    Publisher: Packt Publishing Limited
    Copyright owner: © 2017 Packt Publishing Limited
    Publication date: 2025
    Language: English
    Pages: 386
    Related subjects:
    Computer sciences,
    Computer sciences, other

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