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

Exploratory data analysis in Java

Exploratory Data Analysis is about taking a dataset and extracting the most important information from it, in such a way that it is possible to get an idea of what the data looks like. This includes two main parts: summarization and visualization.

The summarization step is very helpful for understanding data. For numerical variables, in this step we calculate the most important sample statistics: 

  • The extremes (the minimal and the maximal values)
  • The mean value, or the sample average
  • The standard deviation, which describes the spread of the data

Often we consider other statistics, such as the median and the quartiles (25% and 75%).

As we have already seen in the previous chapter, Java offers a great set of tools for data preparation. The same set of tools can be used for EDA, and especially for creating summaries.

Search engine datasets

In this chapter, we will use our running example--building a search engine. In Chapter 2Data Processing Toolbox, we extracted...