Chapter 1, Obtaining and Cleaning Data, covers different ways to read and write data as well as to clean it to get rid of noise. It also familiarizes the readers with different data file types, such as PDF, ASCII, CSV, TSV, XML, and JSON. The chapter also covers recipes for extracting web data.

Chapter 2, Indexing and Searching Data, covers how to index data for fast searching using Apache Lucene. The techniques described in this chapter can be seen as the basis for modern-day search techniques.

Chapter 3, Analyzing Data Statistically, covers the application of Apache Math API to collect and analyze statistics from data. The chapter also covers higher level concepts such as the statistical significance test, which is the standard tool for researchers when they compare their results with benchmarks.

Chapter 4, Learning from Data - Part 1, covers basic classification, clustering, and feature selection exercises using the Weka machine learning Workbench.

Chapter 5, Learning from Data - Part 2, is a follow-up chapter that covers data import and export, classification, and feature selection using another Java library named the Java Machine Learning (Java-ML) Library. The chapter also covers basic classification with the Stanford Classifier and Massive Online Access (MOA).

Chapter 6, Retrieving Information from Text Data, covers the application of data science to text data for information retrieval. It covers the application of core Java as well as popular libraries such as OpenNLP, Stanford CoreNLP, Mallet, and Weka for the application of machine learning to information extraction and retrieval tasks.

Chapter 7, Handling Big Data, covers the application of big data platforms for machine learning, such as Apache Mahout and Spark-MLib.

Chapter 8, Learn Deeply from Data, covers the very basics of deep learning using the Deep Learning for Java (DL4j) library. We cover the word2vec algorithm, belief networks, and auto-encoders.

Chapter 9, Visualizing Data, covers the GRAL package to generate an appealing and informative display based on data. Among the many functionalities of the package, fundamental and basic plots have been selected.

We have used Java to solve real-world data science problems. Our focus was to deliver content that can be effective for anyone who wants to know how to solve problems with Java. A minimum knowledge of Java is required, such as classes, objects, methods, arguments and parameters, exceptions, and exporting Java Archive (JAR) files. The code is well supported with narrations, information, and tips to help the readers understand the context and purpose. The theories behind the problems solved in this book, on many occasions, are not thoroughly discussed, but references for interested readers are provided whenever necessary.

The book is for anyone who wants to know how to solve real-world problems related to data science using Java. The book, as it is very comprehensive in terms of coverage, can also be very useful for practitioners already engaged with data science and looking for solving issues in their projects using Java.

In this book, you will find several headings that appear frequently (Getting ready, How to do it..., How it works..., There's more..., and See also).

To give clear instructions on how to complete a recipe, we use these sections as follows:

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: "Among them, you will find a folder named lib, which is the folder of interest."

A block of code is set as follows:

    classVals = new ArrayList<String>(); 
      for (int i = 0; i < 5; i++){ 
        classVals.add("class" + (i + 1)); 
    }

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

@relation MyRelation 
 
@attribute age numeric 
@attribute name string 
@attribute dob date yyyy-MM-dd 
@attribute class {class1,class2,class3,class4,class5} 
 
@data 
35,'John Doe',1981-01-20,class3 
30,'Harry Potter',1986-07-05,class1

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: "Select System info from the Administration panel."

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To send us general feedback, simply e-mail [email protected], and mention the book's title in the subject of your message.

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