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Data Analytics Made Accessible Maheshwari, Anil pdf.pdf

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    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
    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
    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
    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
    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 ⠀䐀䈀䴀匀)
    Structured Query Language
    Conclusion
    Review Questions
    Appendix 1: Data Mining Tutorial with Weka
    Appendix 1: Data Mining Tutorial with R
    Additional Resources
    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.
    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
    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 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 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 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 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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