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 2. Data Processing Toolbox

In the previous chapter, we discussed the best practices for approaching data science problems. We looked at CRISP-DM, which is the methodology for dealing with data mining projects, and one of the first steps there is data preprocessing. In this chapter, we will take a closer look at how to do this in Java.

Specifically, we will cover the following topics:

  • Standard Java library
  • Extensions to the standard library
  • Reading data from different sources such as text, HTML, JSON, and databases
  • DataFrames for manipulating tabular data

In the end, we will put everything together to prepare the data for the search engine.

By the end of this chapter, you will be able to process data such that it can be used for machine learning and further analysis.