Java Data Science Cookbook

Rushdi Shams

• Editorial: Packt Publishing
• Año: 2017
• ISBN:
  9781787127654
• Edición: 1
• Categoría: Ciencia de la computación
• Categoría: Tratamiento de datos

Recipes to help you overcome your data science hurdles using JavaAbout This Book• This book provides modern recipes in small steps to help an apprentice cook become a master chef in data science• Use these recipes to obtain, clean, analyze, and learn from your data• Learn how to get your data science applications to production and enterprise environments effortlesslyWho This Book Is ForThis book is for Java developers who are familiar with the fundamentals of data science and want to improve their skills to become a pro.What You Will Learn• Find out how to clean and make datasets ready so you can acquire actual insights by removing noise and outliers• Develop the skills to use modern machine learning techniques to retrieve information and transform data to knowledge. retrieve information from large amount of data in text format.• Familiarize yourself with cutting-edge techniques to store and search large volumes of data and retrieve information from large amounts of data in text format• Develop basic skills to apply big data and deep learning technologies on large volumes of data• Evolve your data visualization skills and gain valuable insights from your data• Get to know a step-by-step formula to develop an industry-standard, large-scale, real-life data product• Gain the skills to visualize data and interact with users through data insightsIn DetailIf you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to.This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data.Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more—things that will come in handy at work.Style and approachThis book contains short yet very effective recipes to solve most common problems. Some recipes cater to very specific, rare pain points. The recipes cover different data sets and work very closely to real production environments

Java Data Science Cookbook

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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.

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First published: March 2017

Production reference: 1240317

Published by Packt Publishing Ltd.

Livery Place

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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 ...