Data Analytics Made Accessible

296 Pages·2017·3.41 MB·English

by  Anil Maheshwari

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Data Analytics Made Accessible
Copyright © 2015 by Anil K. Maheshwari, Ph.D.
By purchasing this book, you agree not to copy the book by any means,
mechanical or electronic.
No part of this book may be copied or transmitted without written permission.

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Preface
There are many good books in the market on Data Analytics. So, why should
anyone write another book on this topic? I have been teaching courses in
business intelligence and data mining for a few years. More recently, I have
been teaching this course to combined classes of MBA and Computer Science
students. Existing textbooks seem too long, too technical, and too complex for
use by students. This book fills a need for an accessible book on this topic.
My goal was to write a conversational book that feels easy and informative.
This is an accessible book that covers everything important, with concrete
examples, and invites the reader to join this field.
The book has developed from my own class notes. It reflects my decades of
IT industry experience, as well as many years of academic teaching
experience. The chapters are organized for a typical one-semester graduate
course. The book contains caselets from real-world stories at the beginning of
every chapter. There is a running case study across the chapters as exercises.
Many thanks are in order. My father Mr. Ratan Lal Maheshwari encouraged
me to put my thoughts in writing, and make a book out of it. My wife Neerja
helped me find the time and motivation to write this book. My brother Dr.
Sunil Maheshwari was the sources of many encouraging conversations about
it. My colleague Dr. Edi Shivaji provided advice during my teaching the Data
Analytics courses. Another colleague Dr. Scott Herriott served as a role model
as an author of many textbooks. Yet another colleague, Dr. Greg Guthrie
provided many ideas and ways to disseminate the book. Our department
assistant Ms. Karen Slowick at MUM proof-read the first draft of this book.
Ms. Adri-Mari Vilonel in South Africa helped create an opportunity to use
this book for the first time at a corporate MBA program.
Thanks are also due to to my many students at MUM and elsewhere who
proved good partners in my learning more about this area. Finally, thanks to
Maharishi Mahesh Yogi for providing a wonderful university, MUM, where
students develop their intellect as well as their consciousness.
Dr. Anil K. Maheshwari
Fairfield, IA.
November 2015
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Contents

Preface
Chapter 1: Wholeness of Data Analytics
Business Intelligence
Caselet: MoneyBall - Data Mining in Sports
Pattern Recognition
Data Processing Chain
Data
Database
Data Warehouse
Data Mining
Data Visualization
Organization of the book
Review Questions
Section 1
Chapter 2: Business Intelligence Concepts and Applications
Caselet: Khan Academy – BI in Education
BI for better decisions
Decision types
BI Tools
BI Skills
BI Applications
Customer Relationship Management
Healthcare and Wellness
Education
Retail
Banking
Financial Services
Insurance
Manufacturing
Telecom
Public Sector
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Conclusion
Review Questions
Liberty Stores Case Exercise: Step 1
Chapter 3: Data Warehousing
Caselet: University Health System – BI in Healthcare
Design Considerations for DW
DW Development Approaches
DW Architecture
Data Sources
Data Loading Processes
Data Warehouse Design
DW Access
DW Best Practices
Conclusion
Review Questions
Liberty Stores Case Exercise: Step 2
Chapter 4: Data Mining
Caselet: Target Corp – Data Mining in Retail
Gathering and selecting data
Data cleansing and preparation
Outputs of Data Mining
Evaluating Data Mining Results
Data Mining Techniques
Tools and Platforms for Data Mining
Data Mining Best Practices
Myths about data mining
Data Mining Mistakes
Conclusion
Review Questions
Liberty Stores Case Exercise: Step 3
Chapter 5: Data Visualization
Caselet: Dr Hans Gosling - Visualizing Global Public Health
Excellence in Visualization
Types of Charts
Visualization Example
Visualization Example phase -2
Tips for Data Visualization
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Conclusion
Review Questions
Liberty Stores Case Exercise: Step 4
Section 2
Chapter 6: Decision Trees
Caselet: Predicting Heart Attacks using Decision Trees
Decision Tree problem
Decision Tree Construction
Lessons from constructing trees
Decision Tree Algorithms
Conclusion
Review Questions
Liberty Stores Case Exercise: Step 5
Chapter 7: Regression
Caselet: Data driven Prediction Markets
Correlations and Relationships
Visual look at relationships
Regression Exercise
Non-linear regression exercise
Logistic Regression
Advantages and Disadvantages of Regression Models
Conclusion
Review Exercises:
Liberty Stores Case Exercise: Step 6
Chapter 8: Artificial Neural Networks
Caselet: IBM Watson - Analytics in Medicine
Business Applications of ANN
Design Principles of an Artificial Neural Network
Representation of a Neural Network
Architecting a Neural Network
Developing an ANN
Advantages and Disadvantages of using ANNs
Conclusion
Review Exercises
Chapter 9: Cluster Analysis
Caselet: Cluster Analysis
Applications of Cluster Analysis
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Definition of a Cluster
Representing clusters
Clustering techniques
Clustering Exercise
K-Means Algorithm for clustering
Selecting the number of clusters
Advantages and Disadvantages of K-Means algorithm
Conclusion
Review Exercises
Liberty Stores Case Exercise: Step 7
Chapter 10: Association Rule Mining
Caselet: Netflix: Data Mining in Entertainment
Business Applications of Association Rules
Representing Association Rules
Algorithms for Association Rule
Apriori Algorithm
Association rules exercise
Creating Association Rules
Conclusion
Review Exercises
Liberty Stores Case Exercise: Step 8
Section 3
Chapter 11: Text Mining
Caselet: WhatsApp and Private Security
Text Mining Applications
Text Mining Process
Term Document Matrix
Mining the TDM
Comparing Text Mining and Data Mining
Text Mining Best Practices
Conclusion
Review Questions
Chapter 12: Web Mining
Web content mining
Web structure mining
Web usage mining
Web Mining Algorithms
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Conclusion
Review Questions
Chapter 13: Big Data
Caselet: Personalized Promotions at Sears
Defining Big Data
Big Data Landscape
Business Implications of Big Data
Technology Implications of Big Data
Big Data Technologies
Management of Big Data
Conclusion
Review Questions
Chapter 14: Data Modeling Primer
Evolution of data management systems
Relational Data Model
Implementing the Relational Data Model
Database management systems (DBMS)
Structured Query Language
Conclusion
Review Questions
Appendix 1: Data Mining Tutorial with Weka
Appendix 1: Data Mining Tutorial with R
Additional Resources
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