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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.

Key benefits

An overview of modern Data Science and Machine Learning libraries available in Java

Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks.

Easy-to-follow illustrations and the running example of building a search engine.

Description

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.

Who is this book for?

This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you!

What you will learn

Get a solid understanding of the data processing toolbox available in Java

Explore the data science ecosystem available in Java

Find out how to approach different machine learning problems with Java

Process unstructured information such as natural language text

or images

Create your own search engine

Get state-of-the-art performance with XGBoost

Learn how to build deep neural networks with DeepLearning4j

Build applications that scale and process large amounts of data

Deploy data science models to production and evaluate their

performance

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Instant access to your digital eBook copy whilst your Print order is Shipped

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Luca Massaron May 07, 2017

Disclaimer: I am one of the technical reviewers of this book and an author of other data science and machine learning books.I strongly recommend reading this book in order to have a complete well-rounded overview of what it means doing data science in a company environment today. Commonly we think of data science as just the series of data preparation, experimentation and model development that can lead to a PoC, a Proof of Concept. Clearly, such can be easily done in Python or R in a successful way, but to discover soon after that the project has to be completely rewritten and rethought, if it has to go beyond the PoC status and become an integrated part of the software of company that sustains its competitive advantage in the market.Alexey clearly describes what Java can do for your data science projects, allowing you to go into production with your data science projects leveraging an existing Java ecosystem, which is common to so many small and large companies around the World. Most IT people consider Java as solid, mature, trusted, and reliable and it is (most likely) already been applied to existing ETL processing on the data you are using for your projects. Java can really be the key allowing your data science projects to go smoothly and successfully into production.Moreover, Alexey illustrates in the book many original interesting examples, uncommon to many other books around (no, really no Iris dataset here) and it presents so many fabulous tricks he clearly derived from his many years of practice in data science, that the book is a gem in itself. I found myself learning something new each chapter I read :-)

Amazon Verified review

Mastering Java for Data Science: Analytics and more for production-ready applications

What do you get with Print?

Mastering Java for Data Science

Data Processing Toolbox

Standard Java library

Collections

Extensions to the standard library

Apache Commons

Accessing data

Text data and CSV

Search engine - preparing data

Standard Java library

Collections

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Mastering Java for Data Science: Analytics and more for production-ready applications

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