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Advanced data analytics using Python: with machine learning, deep learning and NLP examples
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Mukhopadhyay / Sayan
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APRESS
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2018
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Sayan Mukhopadhyay

Advanced Data Analytics Using Python With Machine Learning, Deep Learning and NLP Examples

Sayan Mukhopadhyay

Kolkata, West Bengal, India

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-3449-5 . For more detailed information, please visit www.apress.com/source-code .

ISBN 978-1-4842-3449-5 e-ISBN 978-1-4842-3450-1

https://doi.org/10.1007/978-1-4842-3450-1

Library of Congress Control Number: 2018937906

Sayan Mukhopadhyay 2018

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.

Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

Printed on acid-free paper

Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.

This is dedicated to all my math teachers, especially to Kalyan Chakraborty.

Acknowledgments

Thanks to Labonic Chakraborty (Ripa) and Kusumika Mukherjee.

Table of Contents

Index

About the Author and About the Technical Reviewer

About the Author

Sayan Mukhopadhyay

has more than 13 years of industry experience and has been associated with - photo 3

has more than 13 years of industry experience and has been associated with companies such as Credit Suisse, PayPal, CA Technologies, CSC, and Mphasis. He has a deep understanding of applications for data analysis in domains such as investment banking, online payments, online advertisement, IT infrastructure, and retail. His area of expertise is in applying high-performance computing in distributed and data-driven environments such as real-time analysis, high-frequency trading, and so on.

He earned his engineering degree in electronics and instrumentation from Jadavpur University and his masters degree in research in computational and data science from IISc in Bangalore.

About the Technical Reviewer

Sundar Rajan Raman

has more than 14 years of full stack IT experience in machine learning deep - photo 4

has more than 14 years of full stack IT experience in machine learning, deep learning, and natural language processing. He has six years of big data development and architect experience, including working with Hadoop and its ecosystems as well as other NoSQL technologies such as MongoDB and Cassandra. In fact, he has been the technical reviewer of several books on these topics.

He is also interested in strategizing using Design Thinking principles and in coaching and mentoring people.

Sayan Mukhopadhyay 2018

Sayan Mukhopadhyay Advanced Data Analytics Using Python https://doi.org/10.1007/978-1-4842-3450-1_1

1. Introduction

Sayan Mukhopadhyay

(1)

Kolkata, West Bengal, India

In this book, I assume that you are familiar with Python programming. In this introductory chapter, I explain why a data scientist should choose Python as a programming language. Then I highlight some situations where Python is not a good choice. Finally, I describe some good practices in application development and give some coding examples that a data scientist needs in their day-to-day job.

Why Python?

So, why should you choose Python?

It has versatile libraries. You always have a ready-made library in Python for any kind of application. From statistical programming to deep learning to network application to web crawling to embedded systems, you will always have a ready-made library in Python. If you learn this language, you do not have to stick to a specific use case. R has a rich set of analytics libraries, but if you are working on an Internet of Things ( IoT ) application and need to code in a device-side embedded system, it will be difficult in R.
It is very high performance. Java is also a versatile language and has lots of libraries, but Java code runs on a Java virtual machine, which adds an extra layer of latency. Python uses high-performance libraries built in other languages. For example, SciPy uses LAPACK, which is a Fortran library for linear algebra applications. TensorFlow uses CUDA, which is a C library for parallel GPU processing.
It is simple and gives you a lot of freedom to code. Python syntax is just like a natural language. It is easy to remember, and it does not have constraints in variables (like constants or public / private ).

When to Avoid Using Python

Python has some downsides too.

When you are writing very specific code, Python may not always be the best choice. For example, if you are writing code that deals only with statistics, R is a better choice. If you are writing MapReduce code only, Java is a better choice than Python.
Python gives you a lot of freedom in coding. So, when many developers are working on a large application, Java/C++ is a better choice so that one developer/architect can put constraints on another developers code using public / private and constant keywords.
For extremely high-performance applications , there is no alternative to C/C++.

OOP in Python

Before proceeding, I will explain some features of object-oriented programming ( OOP ) in a Python context.

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