Preface

Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyze patterns or predict future behavior. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.

The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings.

By the end of this course, you will be up and running with various facets of data science using Java, in no time at all.

 

What this learning path covers

Module 1, Java for data science, this module takes an expansive yet cursory approach to various aspects of data science. A brief introduction to the field is presented in the first chapter. Subsequent chapters cover significant aspects of data science, such as data cleaning and the application of neural networks. The last chapter combines topics discussed throughout the book to create a comprehensive data science application.

Module 2, Mastering Java for data science, in this module we will see how we can utilize Java’s toolbox for processing small and large datasets, then look into doing initial exploration data analysis.

Next, we will review the Java libraries that implement common Machine Learning models for classification, regression, clustering, and dimensionality reduction problems. Then we will get into more advanced techniques and discuss Information Retrieval and Natural Language Processing, XGBoost, deep learning, and large scale tools for processing big datasets such as Apache Hadoop and Apache Spark. Finally, we will also have a look at how to evaluate and deploy the produced models such that the other services can use them.

What you need for this learning path

Many of the examples in the book use Java 8 features. There are a number of Java APIs demonstrated, each of which is introduced before it is applied. An IDE is not required but is desirable.

You need to have any latest system with at least 2GB RAM and a Windows 7 /Ubuntu 14.04/Mac OS X operating system. Further, you will need to have Java 1.8.0 or above and Maven 3.0.0 or above installed.

Who this learning path is for

 This course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you!

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Here, we create SummaryStatistics objects and add all body content lengths."

A block of code is set as follows:

SummaryStatistics statistics = new SummaryStatistics(); data.stream().mapToDouble(RankedPage::getBodyContentLength)
    .forEach(statistics::addValue); 
System.out.println(statistics.getSummary());

Any command-line input or output is written as follows:

mvn dependency:copy-dependencies -DoutputDirectory=lib 
mvn compile

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "If, instead, our model outputs some score such that the higher the values of the score the more likely the item is to be positive, then the binary classifier is called a ranking classifier."

Note

Warnings or important notes appear in a box like this.

Note

Tips and tricks appear like this.

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Questions

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