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


Java is a very common platform choice for running production code for many applications across many domains. When data scientists create a model for existing applications, Java is a natural choice, since it can be seamlessly integrated into the code. This case is straightforward, you create a separate package, implement your models there, and make sure other packages use it. Another possible option is packaging the code into a separate JAR file, and include it as a Maven dependency.

But there is a different architectural approach for combining multiple components of a large system--the microservices architecture. The main idea is that a system should be composed of small independent units with their own lifecycle--their development, testing, and deployment cycles are independent of all other components.

These microservices typically communicate via REST API, which is based on HTTP. It is based on four HTTP methods--GET, POST, PUT and DELETE. The first two are most commonly used...