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.

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.

 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!

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."

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