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

Case study - hardware performance

In this project, we will try to predict how much time it will take to multiply two matrices on different computers.

The dataset for this project originally comes from the paper Automatic selection of the fastest algorithm implementation by Sidnev and Gergel (2014), and it was made available at a machine learning competition organized by Mail.RU. You can check the details at


The content is in Russian, so if you do not speak it, it is better to use a browser with translation support.

You will find a copy of the dataset along with the code for this chapter.

This dataset has the following data:

  • m, k, and n represent the dimensionality of the matrices, with m*k being the dimensionality of matrix A and k*n being the dimensionality of matrix B
  • Hardware characteristics such as CPU speed, number of cores, whether hyper-threating is enabled or not, and the type of CPU
  • The operation system

The solution for this problem can be quite...