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Mastering Java for Data Science

By : Alexey Grigorev

5 (1)

Buy this Book

Mastering Java for Data Science

5 (1)

By: Alexey Grigorev

Buy this Book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Data Science Using Java

Data science

Data science process models

Data science in Java

Summary

Data Processing Toolbox

Standard Java library

Extensions to the standard library

Accessing data

Search engine - preparing data

Summary

Exploratory Data Analysis

Exploratory data analysis in Java

Interactive Exploratory Data Analysis in Java

Summary

Supervised Learning - Classification and Regression

Classification

Case study - page prediction

Regression

Case study - hardware performance

Summary

Unsupervised Learning - Clustering and Dimensionality Reduction

Dimensionality reduction

Cluster analysis

Summary

Working with Text - Natural Language Processing and Information Retrieval

Natural Language Processing and information retrieval

Machine learning for texts

Summary

Extreme Gradient Boosting

Gradient Boosting Machines and XGBoost

XGBoost in practice

Summary

Deep Learning with DeepLearning4J

Neural Networks and DeepLearning4J

Deep learning for cats versus dogs

Summary

Scaling Data Science

Apache Hadoop

Apache Spark

Link prediction

Summary

Deploying Data Science Models

Microservices

Online evaluation

Summary

Customer Reviews

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Summary

In this chapter, we briefly discussed data science and what role machine learning plays in it. Then we talked about doing a data science project, and what methodologies are useful for it. We discussed one of them, CRISP-DM, the steps it defines, how these steps are related and the outcome of each step.

Finally, we spoke about why doing a data science project in Java is a good idea, it is statically compiled, it's fast, and often the existing production systems already run in Java. We also mentioned libraries and frameworks one can use to successfully accomplish a data science project using the Java language.

With this foundation, we will now go to the most important (and most time-consuming) step in a data science project--Data Preparation.