
Java for Data Science
Examine the techniques and Java tools supporting the growing field of data science
Packt Publishing
Published on 10. January 2017
Book
Paperback/Softback
386 pages
978-1-78528-011-5 (ISBN)
Description
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 Descriptionpara 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 solutionsWhat you will learn
Understand the nature and key concepts used in the field of data science
Grasp how data is collected, cleaned, and processed
Become comfortable with key data analysis techniques
See specialized analysis techniques centered on machine learning
Master the effective visualization of your data
Work with the Java APIs and techniques used to perform data analysis
Who this book is forWith 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.
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 Descriptionpara 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 solutionsWhat you will learn
Understand the nature and key concepts used in the field of data science
Grasp how data is collected, cleaned, and processed
Become comfortable with key data analysis techniques
See specialized analysis techniques centered on machine learning
Master the effective visualization of your data
Work with the Java APIs and techniques used to perform data analysis
Who this book is forWith 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.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 21 mm
Weight
719 gr
ISBN-13
978-1-78528-011-5 (9781785280115)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Richard M. Reese | Shilpi Saxena | Jennifer L. Reese
Java for Data Science
Examine the techniques and Java tools supporting the growing field of data science
E-Book
07/2025
Packt Publishing
from
€39.59
Available for download
Persons
Richard Reese has worked in the industry and academics for the past 29 years. For 10 years he provided software development support at Lockheed and at one point developed a C based network application. He was a contract instructor providing software training to industry for 5 years. Richard is currently an Associate Professor at Tarleton State University in Stephenville Texas. Richard is the author of various books and video courses some of which are as follows: Natural Language Processing with Java. Java for Data Science Getting Started with Natural Language Processing in Java Shilpi Saxena is an IT professional and also a technology evangelist. She is an engineer who has had exposure to various domains (machine to machine space, healthcare, telecom, hiring, and manufacturing). She has experience in all the aspects of conception and execution of enterprise solutions. She has been architecting, managing, and delivering solutions in the Big Data space for the last 3 years; she also handles a high-performance and geographically-distributed team of elite engineers. Shilpi has more than 12 years (3 years in the Big Data space) of experience in the development and execution of various facets of enterprise solutions both in the products and services dimensions of the software industry. An engineer by degree and profession, she has worn varied hats, such as developer, technical leader, product owner, tech manager, and so on, and she has seen all the flavors that the industry has to offer. She has architected and worked through some of the pioneers' production implementations in Big Data on Storm and Impala with autoscaling in AWS. Shilpi has also authored Real-time Analytics with Storm and Cassandra (https://www.packtpub.com/big-data-and-business-intelligence/learning-real-time-analytics-storm-and-cassandra) with Packt Publishing. Jennifer L. Reese studied computer science at Tarleton State University. She also earned her M.Ed. from Tarleton in December 2016. She currently teaches computer science to high-school students. Her interests include the integration of computer science concepts with other academic disciplines, increasing diversity in computer science courses, and the application of data science to the field of education. She has co-authored two books: Java for Data Science and Java 7 New Features Cookbook. She previously worked as a software engineer. In her free time she enjoys reading, cooking, and traveling-especially to any destination with a beach. She is a musician and appreciates a variety of musical genres.
Content
Table of Contents
Getting started with Data Science
Data Acquisition
Data Cleaning
Data Visualization
Statistical Data Analysis Techniques
Machine Learning
Neural Networks
Deep Learning
Text Analysis
Visual and Audio Analysis
Parallel Techniques for Data Analysis
Bringing It All Together
Getting started with Data Science
Data Acquisition
Data Cleaning
Data Visualization
Statistical Data Analysis Techniques
Machine Learning
Neural Networks
Deep Learning
Text Analysis
Visual and Audio Analysis
Parallel Techniques for Data Analysis
Bringing It All Together