Data Science and Analytics for Ordinary People

237 Pages·2015·4.6 MB·English

by  Strickland Jeffrey

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Dr. Jeffrey Strickland is the proprietor of c Data Science and
Simulation Educators, Inc. He has been ie
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performing modeling and simulation for c
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over 20 years, both in the private sector
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and in defense. He has earned advanced n Analytics for
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degrees in mathematics, and has taught
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mathematics and operations research at
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several institutions. He has authored a a
ly Ordinary People
number of technical books on this subject. t
HeresidesinColorado. ic
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This book is about really about Big Data Analytics. Yet, it is O
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written for people who do not have a strong background in data d
analysis or statistics. So one might think of this as a survey of in
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analytics for ordinary people. This is not to say the the rest us
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(Geeks) are extraordinary. I suppose I use ordinary because I do y
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not care for the “Dummy” series of books. Consquently, you will
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not find any formulas or tutorials on tools like SAS, SPSS and R. o
Ifyouarelookingforthiscontent,youneed"PredictiveAnalytics p
usingR". le
Data science and analytics have been used as synonyms on
occasion. In reality data science includes data modeling, data
mining,data analysis,databasearchitectureand so on.Analytics
is what we do to make sense of the data. That is, we take data
and turn it into information for business decision makers. This,
ourcourse,impliesthatwetranslateourdatasciencejargoninto
English, so that ordinary people can use the results. After all, all
ofourworkboilsdownto,"Isitusefultodecisionmakers?".
Data Science and Analytics for Ordinary People is a collection of
blogsIhavewrittenonLinkedInoverthepastyear.AsIcontinue
to perform big data analytics, I continue to discover, not only my
weaknesses in communicating the information, but new insights
into using the information obtained from analytics and
communicating it. These are the kinds of things I blog about and
arecontainedherein.
If you are a manager, marketer or any other ordinary person, this
bookisforyou.
J
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y Jeffrey Strickland
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ISBN 978-1-329-28062-5 k
90000 la
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ID:16909563
9 781329 280625
www.lulu.com

Data Science and Analytics for Ordinary People

By
Jeffrey S. Strickland

Data Science and Analytics for Ordinary People

Copyright 2014 by Jeffrey S. Strickland.
All rights reserved. This book or any portion thereof may not be
reproduced or used in any manner whatsoever without the express
written permission of the publisher except for the use of brief quotations
in a book review.
Printed in the United States of America
First Printing, 2015
ISBN 978-1-329-28062-5
Published by Lulu, Inc.

Table of Contents
TABLE OF CONTENTS .......................................................................................... VII
ACKNOWLEDGEMENTS........................................................................................ XIX
PREFACE .......................................................................................................... XXI
PART I – BIG DATA ......................................................................................... 1
LIFE BEFORE BIG DATA ......................................................................................... 1
Headlines .................................................................................................... 1
Background ................................................................................................ 2
NASA’s Bigger Data .................................................................................... 2
Other Bigger Data ...................................................................................... 2
Conclusion .................................................................................................. 3
Works Cited ................................................................................................ 3
BIG DATA, SMALL DATA, HORSES AND UNICORNS .................................................... 4
I didn’t know it was sick… ........................................................................... 4
The end came quickly… .............................................................................. 5
Life after the horses… ................................................................................. 5
PART II - ANALYTICS ....................................................................................... 7
WHAT IS BUSINESS ANALYTICS? ............................................................................. 7
Types .......................................................................................................... 7
Descriptive analytics .......................................................................................... 8
Predictive analytics ............................................................................................ 8
Prescriptive analytics ......................................................................................... 8
Applications ................................................................................................ 9
PREDICTIVE ANALYTICS & MODELING .................................................................... 10
Definition .................................................................................................. 11
Not Statistics ............................................................................................ 11
Types ........................................................................................................ 12
Predictive models ............................................................................................ 12
Descriptive models .......................................................................................... 13
Decision models ............................................................................................... 13
Applications ............................................................................................. 14
Analytical customer relationship management (CRM) .................................... 14
Clinical decision support systems .................................................................... 14
Collection analytics .......................................................................................... 15
Cross-sell .......................................................................................................... 15
Customer retention ......................................................................................... 15
Direct marketing .............................................................................................. 16
Fraud detection ............................................................................................... 16
Portfolio, product or economy-level prediction .............................................. 17
Risk management ............................................................................................ 17
Underwriting .................................................................................................... 17
Technology and big data influences ........................................................ 18
Analytical Techniques .............................................................................. 18
Regression techniques ..................................................................................... 19
Classification and regression trees .................................................................. 19
Machine learning techniques .......................................................................... 19
Criticism ................................................................................................... 19
Tools ........................................................................................................ 21
References ............................................................................................... 21
A DANGEROUS GAME WE PLAY .......................................................................... 23
What are our assumptions? .................................................................... 23
Can we really predict the Unpredictable?................................................ 24
What are models really? .......................................................................... 24
Can models be “good enough”? .............................................................. 25
Did we build the “Right” Model? ............................................................. 26
Is it still dangerous? .................................................................................. 27
PREDICTIVE ANALYTICS: THE SKILLS AND TOOLS ........................................................ 28
What is Predictive Analytics? ................................................................... 28
Statistical Modeling & Tools ..................................................................... 29
Data Processing ........................................................................................ 30
WHAT THE HECK IS DATA MINING? ...................................................................... 31
Data Mining ............................................................................................. 31
Data Miners .............................................................................................. 31
The Data Mining Process .......................................................................... 32
Exploration ....................................................................................................... 32
Model Building and Validation ......................................................................... 32
Deployment ..................................................................................................... 33
Data Mining and Statistics ....................................................................... 33
Data Mining and Exploratory Data Analysis ............................................ 33
Data Mining and Machine Learning ......................................................... 34
Data Mining Techniques ........................................................................... 34
COMMONLY MISUNDERSTOOD ANALYTICS TERMS ................................................... 36
"At least" .................................................................................................. 36
"At most" .................................................................................................. 36
"Sample"................................................................................................... 37
"95% Confidence" ..................................................................................... 37
"Variance" ................................................................................................ 38
"Skewness" ............................................................................................... 38
"Central Limit Theorem" ........................................................................... 39
Conclusion ................................................................................................ 40
PART III – MODELS AND MODELS AND MORE MODELS................................ 43
HOW DO YOU BUILD A MODEL? ............................................................................ 43
Define the problem ................................................................................... 44