Java for Data Science
Richard M. Reese, Jennifer L. Reese
- 386 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Java for Data Science
Richard M. Reese, Jennifer L. Reese
About This Book
Examine the techniques and Java tools supporting the growing field of data science
About This Book
- 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
Who This Book Is For
This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful.
What 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
In Detail
Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application.
The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation.
The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book.
Style and approach
This book follows a tutorial approach, providing examples of each of the major concepts covered.
With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.
Frequently asked questions
Information
Java for Data Science
Java for Data Science
Credits
Authors Richard M. Reese Jennifer L. Reese | Copy Editors Vikrant Phadkay Safis Editing |
Reviewers Walter Molina Shilpi Saxena | Project Coordinator Nidhi Joshi |
Commissioning Editor Veena Pagare | Proofreader Safis Editing |
Acquisition Editor Tushar Gupta | Indexer Aishwarya Gangawane |
Content Development Editor Aishwarya Pandere | Graphics Disha Haria |
Technical Editor Suwarna Patil | Production Coordinator Nilesh Mohite |
About the Authors
Richard would like to thank his wife, Karla, for her continued support, and to the staff of Packt Publishing for their work in making this a better book.
I would like to thank Dad for his inspiration and guidance, Mom for her patience and perspective, and Jace for his support and always believing in me.
About the Reviewers
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