Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

By: Alexey Grigorev

Overview of this book

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Chapter 1. Data Science Using Java

This book is about building data science applications using the Java language. In this book, we will cover all the aspects of implementing projects from data preparation to model deployment.

The readers of this book are assumed to have some previous exposure to Java and data science, and the book will help to take this knowledge to the next level. This means learning how to effectively tackle a specific data science problem and get the most out of the available data.

This is an introductory chapter where we will prepare the foundation for all the other chapters. Here we will cover the following topics:

  • What is machine learning and data science?
  • Cross Industry Standard Process for Data Mining (CRIPS-DM), a methodology for doing data science projects
  • Machine learning libraries in Java for medium and large-scale data science applications

By the end of this chapter, you will know how to approach a data science project and what Java libraries to use to do that.