Carlos Reis Pinheiro - Introduction to Statistical and Machine Learning Methods for Data Science

Carlos Andre Reis Pinheiro
Mike Patetta

sas.com/books

The correct bibliographic citation for this manual is as follows: Pinheiro, Carlos Andre Reis and Mike Patetta. 2021. Introduction to Statistical and Machine Learning Methods for Data Science . Cary, NC: SAS Institute Inc.

Introduction to Statistical and Machine Learning Methods for Data Science

Copyright 2021, SAS Institute Inc., Cary, NC, USA

ISBN 978-1-953329-64-6 (Hardcover)
ISBN 978-1-953329-60-8 (Paperback)
ISBN 978-1-953329-61-5 (Web PDF)
ISBN 978-1-953329-62-2 (EPUB)
ISBN 978-1-953329-63-9 (Kindle)

All Rights Reserved. Produced in the United States of America.

For a hard copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc.

For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication.

The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others rights is appreciated.

U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 227.7202-4, and, to the extent required under U.S. federal law, the minimum restricted rights as set out in FAR 52.227-19 (DEC 2007). If FAR 52.227-19 is applicable, this provision serves as notice under clause (c) thereof and no other notice is required to be affixed to the Software or documentation. The Governments rights in Software and documentation shall be only those set forth in this Agreement.

SAS Institute Inc., SAS Campus Drive, Cary, NC 27513-2414

August 2021

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration.

Other brand and product names are trademarks of their respective companies.

SAS software may be provided with certain third-party software, including but not limited to open-source software, which is licensed under its applicable third-party software license agreement. For license information about third-party software distributed with SAS software, refer to http://support.sas.com/thirdpartylicenses .

Contents

About This Book

What Does This Book Cover?

This book gives an overview of the statistical and machine learning methods used in data science projects, with an emphasis on the applicability to business problem solving. No software is shown, and the mathematical details are kept to a minimum. The book describes the tasks associated with all stages of the analytical life cycle, including data preparation and data exploration, feature engineering and selection, analytical modeling considering supervised and unsupervised techniques, and model assessment and deployment. It describes the techniques and provides real-world case studies to exemplify the techniques. Readers will learn the most important techniques and methods related to data science and when to apply them for different business problems. The book provides a comprehensive overview about the statistical and machine learning techniques associated with data science initiatives and guides readers through the necessary steps to successfully deploy data science projects.

This book covers the most important data science skills, the types of different data science applications, the phases in the data science lifecycle, the techniques assigned to the data preparation steps for data science, some of the most common techniques associated to supervised machine learning models (linear and logistic regression, decision tree, forest, gradient boosting, neural networks, support vector machines, and factorization machines), advanced supervised modeling methods like ensemble models and two-stage models, the most important techniques associated to unsupervised machine learning models (clustering, association rules, sequence analysis, link analysis, path analysis, network analysis, and network optimization), the method and fits statistics to assess model results, different approaches to deploy analytical models in production, and the main topics related to the model operationalization process.

This book does not cover the techniques for data engineering in depth. It also does not provide any programming code for the supervised and unsupervised models, nor does it show in practice how to deploy models in production.

Is This Book for You?

The audience of this book is data scientists, data analysts, data engineers, business analysts, market analysts, or computer scientists. However, anyone who wants to learn more about data science skills could benefit from reading this book.

What Are the Prerequisites for This Book?

There are no prerequisites for this book.

We Want to Hear from You

SAS Press books are written by SAS Users for SAS Users. We welcome your participation in their development and your feedback on SAS Press books that you are using. Please visit sas.com/books to do the following:

• Sign up to review a book
• Recommend a topic
• Request information about how to become a SAS Press author
• Provide feedback on a book