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 talked about Exploratory Data Analysis, or EDA for short. We discussed how to do EDA in Java, which included creating summaries and simple visualizations.

Throughout the chapter, we used our search engine example and analyzed the data we collected previously. Our analysis showed that the distribution of some variables looks different for URLs coming from different pages of the search engine results. This suggests that it is possible to use these differences to build a model that will predict whether a URL comes from the first page or not.

In the next chapter, we will look at how to do it and discuss of supervised machine learning algorithms, such as classification and regression.