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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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Apache Spark

Apache Spark is a framework for scalable data processing. It was designed to be better than Hadoop: it tries to process data in memory and not to save intermediate results on disk. Additionally, it has more operations, not just map and reduce, and thus richer APIs.

The main unit of abstraction in Apache Spark is Resilient Distributed Dataset (RDD), which is a distributed collection of elements. The key difference from usual collections or streams is that RDDs can be processed in parallel across multiple machines, in the same way, Hadoop jobs are processed.

There are two types of operations we can apply to RDDs: transformations and actions.

Transformations: As the name suggests, it only changes data from one form to another. As input, they receive an RDD, and they also output an RDD. Operations such as map, flatMap, or filter are examples of transformation operations.
Actions: These take...