University of Texas at Dallas Week 1 Big Data Analytics Lifecycle Essay Paper 1 : Review Chapter 2 information relative to the Data Analytics Lifecycle

University of Texas at Dallas Week 1 Big Data Analytics Lifecycle Essay Paper 1 :

Review Chapter 2 information relative to the Data Analytics Lifecycle.

Construct an essay that incorporates the following information:

a. Briefly describe the significance of correctly framing a business analytics problem to be solved.

b. Choose one industry that is of interest to you. Using this industry as an example, describe issues that may arise from a poorly framed problem.

– Minimum word length: 550

– Minimum documented sources: 2

– Essay formatted per APA specifications including both in-text and final references Data Science &
Big Data Analytics
Data Science &
Big Data Analytics
Discovering, Analyzing, Visualizing
and Presenting Data
EMC Education Services
Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
Published by
John Wiley & Sons, Inc.
10475 Crosspoint Boulevard
Indianapolis, IN 46256
www.wiley.com
Copyright © 2015 by John Wiley & Sons, Inc., Indianapolis, Indiana
Published simultaneously in Canada
ISBN: 978-1-118-87613-8
ISBN: 978-1-118-87622-0 (ebk)
ISBN: 978-1-118-87605-3 (ebk)
Manufactured in the United States of America
10 9 8 7 6 5 4 3 2 1
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,
recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA
01923, (978) 750-8400, fax (978) 646-8600. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc.,
111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions.
Limit of Liability/Disclaimer of Warranty: The publisher and the author make no representations or warranties with respect to the accuracy or completeness of
the contents of this work and specifically disclaim all warranties, including without limitation warranties of fitness for a particular purpose. No warranty may be
created or extended by sales or promotional materials. The advice and strategies contained herein may not be suitable for every situation. This work is sold with
the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If professional assistance is required, the
services of a competent professional person should be sought. Neither the publisher nor the author shall be liable for damages arising herefrom. The fact that an
organization or Web site is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher
endorses the information the organization or website may provide or recommendations it may make. Further, readers should be aware that Internet websites
listed in this work may have changed or disappeared between when this work was written and when it is read.
For general information on our other products and services please contact our Customer Care Department within the United States at (877) 762-2974, outside the
United States at (317) 572-3993 or fax (317) 572-4002.
Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be
included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download
this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.
Library of Congress Control Number: 2014946681
Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates, in the United States and other countries, and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated
with any product or vendor mentioned in this book.
Credits
Executive Editor
Carol Long
Project Editor
Kelly Talbot
Production Manager
Kathleen Wisor
Copy Editor
Karen Gill
Manager of Content Development
and Assembly
Mary Beth Wakefield
Marketing Director
David Mayhew
Marketing Manager
Carrie Sherrill
Professional Technology and Strategy Director
Barry Pruett
Business Manager
Amy Knies
Associate Publisher
Jim Minatel
Project Coordinator, Cover
Patrick Redmond
Proofreader
Nancy Carrasco
Indexer
Johnna VanHoose Dinse
Cover Designer
Mallesh Gurram
About the Key Contributors
David Dietrich heads the data science education team within EMC Education Services, where he leads the
curriculum, strategy and course development related to Big Data Analytics and Data Science. He co-authored the first course in EMC’s Data Science curriculum, two additional EMC courses focused on teaching
leaders and executives about Big Data and data science, and is a contributing author and editor of this
book. He has filed 14 patents in the areas of data science, data privacy, and cloud computing.
David has been an advisor to several universities looking to develop academic programs related to data
analytics, and has been a frequent speaker at conferences and industry events. He also has been a a guest lecturer at universities in the Boston area. His work has been featured in major publications including Forbes, Harvard Business Review, and the
2014 Massachusetts Big Data Report, commissioned by Governor Deval Patrick.
Involved with analytics and technology for nearly 20 years, David has worked with many Fortune 500 companies over his
career, holding multiple roles involving analytics, including managing analytics and operations teams, delivering analytic consulting engagements, managing a line of analytical software products for regulating the US banking industry, and developing
Software-as-a-Service and BI-as-a-Service offerings. Additionally, David collaborated with the U.S. Federal Reserve in developing predictive models for monitoring mortgage portfolios.
Barry Heller is an advisory technical education consultant at EMC Education Services. Barry is a course developer and curriculum advisor in the emerging technology areas of Big Data and data science. Prior to his current role, Barry was a consultant research scientist leading numerous analytical initiatives within EMC’s Total Customer Experience
organization. Early in his EMC career, he managed the statistical engineering group as well as led the
data warehousing efforts in an Enterprise Resource Planning (ERP) implementation. Prior to joining EMC,
Barry held managerial and analytical roles in reliability engineering functions at medical diagnostic and
technology companies. During his career, he has applied his quantitative skill set to a myriad of business
applications in the Customer Service, Engineering, Manufacturing, Sales/Marketing, Finance, and Legal
arenas. Underscoring the importance of strong executive stakeholder engagement, many of his successes
have resulted from not only focusing on the technical details of an analysis, but on the decisions that will be resulting from
the analysis. Barry earned a B.S. in Computational Mathematics from the Rochester Institute of Technology and an M.A. in
Mathematics from the State University of New York (SUNY) New Paltz.
Beibei Yang is a Technical Education Consultant of EMC Education Services, responsible for developing several open courses
at EMC related to Data Science and Big Data Analytics. Beibei has seven years of experience in the IT industry. Prior to EMC she
worked as a software engineer, systems manager, and network manager for a Fortune 500 company where she introduced
new technologies to improve efficiency and encourage collaboration. Beibei has published papers to
prestigious conferences and has filed multiple patents. She received her Ph.D. in computer science from
the University of Massachusetts Lowell. She has a passion toward natural language processing and data
mining, especially using various tools and techniques to find hidden patterns and tell stories with data.
Data Science and Big Data Analytics is an exciting domain where the potential of digital information is
maximized for making intelligent business decisions. We believe that this is an area that will attract a lot of
talented students and professionals in the short, mid, and long term.
Acknowledgments
EMC Education Services embarked on learning this subject with the intent to develop an “open” curriculum and
certification. It was a challenging journey at the time as not many understood what it would take to be a true
data scientist. After initial research (and struggle), we were able to define what was needed and attract very
talented professionals to work on the project. The course, “Data Science and Big Data Analytics,” has become
well accepted across academia and the industry.
Led by EMC Education Services, this book is the result of efforts and contributions from a number of key EMC
organizations and supported by the office of the CTO, IT, Global Services, and Engineering. Many sincere
thanks to many key contributors and subject matter experts David Dietrich, Barry Heller, and Beibei Yang
for their work developing content and graphics for the chapters. A special thanks to subject matter experts
John Cardente and Ganesh Rajaratnam for their active involvement reviewing multiple book chapters and
providing valuable feedback throughout the project.
We are also grateful to the following experts from EMC and Pivotal for their support in reviewing and improving
the content in this book:
Aidan O’Brien
Joe Kambourakis
Alexander Nunes
Joe Milardo
Bryan Miletich
John Sopka
Dan Baskette
Kathryn Stiles
Daniel Mepham
Ken Taylor
Dave Reiner
Lanette Wells
Deborah Stokes
Michael Hancock
Ellis Kriesberg
Michael Vander Donk
Frank Coleman
Narayanan Krishnakumar
Hisham Arafat
Richard Moore
Ira Schild
Ron Glick
Jack Harwood
Stephen Maloney
Jim McGroddy
Steve Todd
Jody Goncalves
Suresh Thankappan
Joe Dery
Tom McGowan
We also thank Ira Schild and Shane Goodrich for coordinating this project, Mallesh Gurram for the cover design, Chris Conroy
and Rob Bradley for graphics, and the publisher, John Wiley and Sons, for timely support in bringing this book to the
industry.
Nancy Gessler
Director, Education Services, EMC Corporation
Alok Shrivastava
Sr. Director, Education Services, EMC Corporation
Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
……………………………………… 1
1.1 Big Data Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Data Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1.2 Analyst Perspective on Data Repositories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2 State of the Practice in Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.2.1 BI Versus Data Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.2.2 Current Analytical Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.2.3 Drivers of Big Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.2.4 Emerging Big Data Ecosystem and a New Approach to Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16
1.3 Key Roles for the New Big Data Ecosystem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.4 Examples of Big Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.1 Data Analytics Lifecycle Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.1.1 Key Roles for a Successful Analytics Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26
2.1.2 Background and Overview of Data Analytics Lifecycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28
2.2 Phase 1: Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.2.1 Learning the Business Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30
2.2.2 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31
2.2.3 Framing the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32
2.2.4 Identifying Key Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33
2.2.5 Interviewing the Analytics Sponsor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33
2.2.6 Developing Initial Hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35
2.2.7 Identifying Potential Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35
2.3 Phase 2: Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.3.1 Preparing the Analytic Sandbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37
2.3.2 Performing ETLT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38
2.3.3 Learning About the Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39
2.3.4 Data Conditioning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40
2.3.5 Survey and Visualize. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41
2.3.6 Common Tools for the Data Preparation Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42
2.4 Phase 3: Model Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.4.1 Data Exploration and Variable Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44
2.4.2 Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45
2.4.3 Common Tools for the Model Planning Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45
x
CONTENTS
2.5 Phase 4: Model Building. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.5.1 Common Tools for the Model Building Phase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .48
2.6 Phase 5: Communicate Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.7 Phase 6: Operationalize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
2.8 Case Study: Global Innovation Network and Analysis (GINA). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.8.1 Phase 1: Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .54
2.8.2 Phase 2: Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55
2.8.3 Phase 3: Model Planning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56
2.8.4 Phase 4: Model Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56
2.8.5 Phase 5: Communicate Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .58
2.8.6 Phase 6: Operationalize. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.1 Introduction to R. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.1.1 R Graphical User Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67
3.1.2 Data Import and Export. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .69
3.1.3 Attribute and Data Types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . …
Purchase answer to see full
attachment