Anil Maheshwari - Data Analytics Made Accessible: 2021 edition
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Data Analytics Made Accessible
Copyright 2014-21 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.
Other Books by the same author:
Big Data Made Accessible
Moksha: Liberation Through Transcendence
Transformational Leadership: Spirit In Action
Marketing Made Accessible
Select list of Universities that use this book as a Textbook
- James Madison University, Virginia
- San Diego State University, California
- Boston University, Massachusetts
- Boise State University, Idaho
- Maharishi University of Management, Iowa
- Morehead State University, Kentucky
- Regent University, Iowa
- University of Alaska, Anchorage
- Victoria University of Wellington, New Zealand
- Letterkenny Institute of Technology, Ireland
- University of Los Andes, Chile
- Adolfo Ibez University, Chile
Select Reviewer Comments for this book
Dr. Maheshwari's book is a very nice introduction to analytics. He explains concepts very clear and to the point. I like particularly his chapter on decision trees and the process to obtain them. His explanation is very clear. Dr. Ramon A. Mata Toledo, Professor of Computer Science, James Madison University, Virginia.
This book is a splendid and valuable addition to this subject. The whole book is well-written and I have no hesitation to recommend that this can be adapted as a textbook for graduate courses in Business Intelligence and Data Mining. Dr. Edi Shivaji, St. Louis, Missouri.
Really well written and timely as the world gets in the Big Data mode! I think this can be a good bridge and primer for the uninitiated manager who knows Big Data is the future but doesn't know where to begin! Dr. Alok Mishra, Singapore.
This book has done a great job of taking a complex, highly important subject area and making it accessible to everyone. It begins by simply connecting to what you know, and then bang - you've suddenly found out about Decision Trees, Regression Models and Artificial Neural Networks, not to mention cluster analysis, web mining and Big Data. Ms. Charmaine Oak, United Kingdom.
Bottom line is anyone interested in learning about Data Analytics this is THE book to start your learning path with and hope it will spur your interest in the field and enable you to grasp more in-depth topics and further your skills. Keith S Safford, Maryland.
Data Science continues to be the hottest discipline, along with Artificial Intelligence, as a new decade emerges. This 2021 edition continues to build on new topics such as Data Privacy and Artificial Intelligence, which have emerged as hot topics. I have been teaching courses in Data Analytics and Big Data for many years. My students found other textbooks seem too long, too technical, too complex, or too focused on specific tools. 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. Many people have entered this field upon reading this book.
This book can be easily read in a short period by anyone use wants to understand data-based decision-making for their business or other organizations, without any expertise with software tools. The text is almost entirely devoid of complex jargon or programming code. It includes a primer on Statistics, Databases, Big Data, and Artificial Intelligence for those interested in these topics. A simple yet extensive tutorial on R, a popular, free, open-source, high-level programming language that is very popular with data scientists, is included at the end of the book. The book also includes a short tutorial on Python, another very popular high-level general-purpose programming language.
This book reflects my three decades of global IT experience in academia and industry. The chapters are organized for a typical one-semester graduate course. There are case-lets from real-world stories at the beginning of every chapter. There is a running case study across the chapters as exercises. There are review questions at the end of each chapter. Many universities around the world have adopted this as a textbook for their courses. The book has continued to evolve in response to the thoughts and suggestions expressed by the reviewers and students. Thanks to many reviewers for sharing many ideas for improvement.
My family had encouraged and supported me in writing and improving this book. I am also grateful to my students and others who read this book and provide feedback and suggestions. Finally, thanks to Maharishi Mahesh Yogi for providing a wonderful environment of consciousness-based education that provided the inspiration for writing this book from wholeness. This book is a guru-dakshina , or homage, to Maharishi.
Dr. Anil K. Maheshwari
Fairfield, IA;
December 2020
Contents
Business is the act of doing something productive to serve someones needs, and thus earn a living and make the world a better place. Business activities are recorded on paper or using electronic media, and then these records become data. There is more data from customers responses and on the industry as a whole. All this data can be analyzed and mined using special tools and techniques to generate patterns and intelligence, which reflect how the business is functioning. These ideas can then be fed back into the business so that it can evolve to become more effective and efficient in serving customer needs. And the cycle continues on (Figure 1.1).
Figure 1.1: Business Intelligence and Data Mining (BIDM) Cycle
Any business organization needs to continually monitor its business environment and its own performance, and then rapidly adjust its future plans. This includes monitoring the industry, the competitors, the suppliers, and the customers. The organization needs to also develop a balanced scorecard to track its own health and vitality. Executives typically determine what they want to track based on their key performance Indexes (KPIs) or key result areas (KRAs). Customized reports need to be designed to deliver the required information to every executive. These reports can be converted into customized dashboards that deliver the information rapidly and in easy-to-grasp formats.
Analytics in sports was made popular by the book and movie, Moneyball. Statistician Bill James and Oakland A's general manager, Billy Bean, placed emphasis on crunching numbers and data instead of watching an athlete's style and looks. Their goal was to make a team better while using fewer resources. The key action plan was to pick important role players at a lower cost while avoiding the famous players who demand higher salaries but may provide a low return on a team's investment. Rather than relying on the scouts' experience and intuition Bean selected players based almost exclusively on their on-base percentage (OBP). By finding players with a high OBP but, with characteristics that lead scouts to dismiss them, Bean assembled a team of undervalued players with far more potential than the A's hamstrung finances would otherwise allow.
Using this strategy, they proved that even small market teams can be competitive a case in point, the Oakland A's. In 2004, two years after adopting the same sabermetric model, the Boston Red Sox won their first World Series since 1918. (Source: Moneyball, 2004).
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