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


In this chapter, we briefly discussed data science and what role machine learning plays in it. Then we talked about doing a data science project, and what methodologies are useful for it. We discussed one of them, CRISP-DM, the steps it defines, how these steps are related and the outcome of each step.

Finally, we spoke about why doing a data science project in Java is a good idea, it is statically compiled, it's fast, and often the existing production systems already run in Java. We also mentioned libraries and frameworks one can use to successfully accomplish a data science project using the Java language.

With this foundation, we will now go to the most important (and most time-consuming) step in a data science project--Data Preparation.