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- Herausgeber: Packt Publishing
- Kategorie: Lebensstil
- Sprache: Englisch
This book is for Java developers who are familiar with the fundamentals of data science and want to improve their skills to become a pro.
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Seitenzahl: 298
Veröffentlichungsjahr: 2017
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Table of Contents
Java Data Science Cookbook
Java Data Science Cookbook
Copyright © 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: March 2017
Production reference: 1240317
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-78712-253-6
www.packtpub.com
Credits
Author
Rushdi Shams
Copy Editors
Vikrant Phadke
Manisha Sinha
Reviewer
Prashant Verma
Project Coordinator
Nidhi Joshi
Commissioning Editor
Veena Pagare
Proofreader
Safis Editing
Acquisition Editor
Ajith Menon
Indexer
Aishwarya Gangawane
Content Development Editor
Cheryl Dsa
Graphics
Tania Dutta
Technical Editor
Dharmendra Yadav
Production Coordinator
Arvindkumar Gupta
About the Author
Rushdi Shams has a PhD on application of machine learning in Natural Language Processing (NLP) problem areas from Western University, Canada. Before starting work as a machine learning and NLP specialist in industry, he was engaged in teaching undergrad and grad courses. He has been successfully maintaining his YouTube channel named "Learn with Rushdi" for learning computer technologies.
I would like to acknowledge the Almighty Allah for giving me the strength, support, and knowledge to finish the book.
I extend my thanks to my family members, friends, and colleagues for continuous support, encouragement, and constructive criticism.
I would also like to thank Ajith and Cheryl from Packt for their continuous and spontaneous collaboration with me.
About the Reviewer
Prashant Verma started his IT career in 2011 as a Java developer at Ericsson, working in the telecom domain. After a couple of years of Java EE experience, he moved into the big data domain, and has worked on almost all the popular big data technologies such as Hadoop, Spark, Kafka, Flume, Mongo, Cassandra, and so on. He has also worked in Scala and Python. Currently, he works with QA Infotech as Lead Data Engineer, working on solving e-learning domain problems using data analytics and machine learning.
Prashant has also worked on Apache Spark for Java Developers, Packt as a Technical Reviewer.
I want to thank Packt Publishing for giving me the chance to review the book, as well as my employer and my family for their patience while I was busy working on this book.
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To my lovely wife, Mah-Zereen, and adorable daughter, Ruayda.
Preface
Data science is a popular field for specialization nowadays and covers the broad spectrum of artificial intelligence, such as data processing, information retrieval, machine learning, natural language processing, big data, deep neural networks, and data visualization. In this book, we will understand the techniques that are both modern and smart and presented as easy-to-follow recipes for over 70 problems.
Keeping in mind the high demand for quality data scientists, we have compiled recipes using core Java as well as well-known, classic, and state-of-the-art data science libraries written in Java. We start with the data collection and cleaning process. Then we see how the obtained data can be indexed and searched. Afterwards, we cover statistics both descriptive and inferential and their application to data. Then, we have two back-to-back chapters on the application of machine learning on data that can be foundation for building any smart system. Modern information retrieval and natural language processing techniques are also covered. Big data is an emerging field, and a few aspects of this popular field are also covered. We also cover the very basics of deep learning using deep neural networks. Finally, we learn how to represent data and information obtained from data using meaningful visuals or graphs.
The book is aimed at anyone who has an interest in data science and plans to apply data science using Java to understand underlying data better.
What this book covers
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.
What you need for this book
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.
Who this book is for
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.
Sections
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:
Getting ready
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
How to do it...
This section contains the steps required to follow the recipe.
How it works...
This section usually consists of a detailed explanation of what happened in the previous section.
There's more...
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
See also
This section provides helpful links to other useful information for the recipe.
Reader feedback
Feedback from our readers is always welcome. Let us know what you think about this book-what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of.
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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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Java-Data-Science-Cookbook. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Downloading the color images of this book
We also provide you with a PDF file that has color images of the screenshots/diagrams used in this book. The color images will help you better understand the changes in the output. You can download this file from https://www.packtpub.com/sites/default/files/downloads/JavaDataScienceCookbook_ColorImages.pdf.
Errata
Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books-maybe a mistake in the text or the code-we would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this book. If you find any errata, please report them by visiting http://www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded to our website or added to any list of existing errata under the Errata section of that title.
To view the previously submitted errata, go to https://www.packtpub.com/books/content/support and enter the name of the book in the search field. The required information will appear under the Errata section.
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Please contact us at [email protected] with a link to the suspected pirated material.
We appreciate your help in protecting our authors and our ability to bring you valuable content.
Questions
If you have a problem with any aspect of this book, you can contact us at [email protected], and we will do our best to address the problem.
Chapter 1. Obtaining and Cleaning Data
In this chapter, we will cover the following recipes:
Introduction
Every data scientist needs to deal with data that is stored on disks in several formats, such as ASCII text, PDF, XML, JSON, and so on. Also, data can be stored in database tables. The first and foremost task for a data scientist before doing any analysis is to obtain data from these data sources and of these formats, and apply data-cleaning techniques to get rid of noises present in them. In this chapter, we will see recipes to accomplish this important task.
We will be using external Java libraries (Java archive files or simply JAR files) not only for this chapter but throughout the book. These libraries are created by developers or organizations to make everybody's life easier. We will be using Eclipse IDE for code development, preferably on the Windows platform, and execution throughout the book. Here is how you can include any external JAR file, and in many recipes, where I instruct you to include external JAR files into your project, this is what you need to do.
You can add a JAR file in a project in Eclipse by right-clicking on the Project | Build Path | Configure Build Path. Under the Libraries tab, click on Add External JARs..., and select the external JAR file(s) that you are going to use for a particular project:
