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 (11 chapters)

Preface

Data science has become a quite important tool for organizations nowadays: they have collected large amounts of data, and to be able to put it into good use, they need data science--the discipline about methods for extracting knowledge from data. Every day more and more companies realize that they can benefit from data science and utilize the data that they produce more effectively and more profitably.

It is especially true for IT companies, they already have the systems and the infrastructure for generating and processing the data. These systems are often written in Java--the language of choice for many large and small companies across the world. It is not a surprise, Java offers a very solid and mature ecosystem of libraries that are time proven and reliable, so many people trust Java and use it for creating their applications.

Thus, it is also a natural choice for many data processing applications. Since the existing systems are already in Java, it makes sense to use the same technology stack for data science, and integrate the machine learning model directly in the application's production code base.

This book will cover exactly that. We will first see how we can utilize Java’s toolbox for processing small and large datasets, then look into doing initial exploration data analysis. Next, we will review the Java libraries that implement common Machine Learning models for classification, regression, clustering, and dimensionality reduction problems. Then we will get into more advanced techniques and discuss Information Retrieval and Natural Language Processing, XGBoost, deep learning, and large scale tools for processing big datasets such as Apache Hadoop and Apache Spark. Finally, we will also have a look at how to evaluate and deploy the produced models such that the other services can use them.

We hope you will enjoy the book. Happy reading!