Common Data Sense for Professionals

Data is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections of data to determine strategy and marketing. Data scientists take data, analyze it, and create models to help solve problems. You may have heard of companies having data management teams or chief information officers (CIOs) or chief data officers (CDOs), etc. They are all people who work with data, but their function is more related to vetting data and preparing it for use by data scientists.

The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think big data analysis requires tools held only by advanced degree holders. Although advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a data science project. Because we are all already data users, with some simple strategies and exposure to basic analytical software programs, anyone who has the proper tools and determination can solve data science problems. The process presented in this book will help empower individuals to work on and solve data-related challenges.

The goal of this book is to provide a step-by-step guide to the data science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study, illustrating how the various topics discussed can be applied. Essentially, this book helps traditional businesspeople solve data-related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity.

Table of Contents

Chapter 1 The Meeting of Manju and Jim

Chapter 2 Understanding the Problem

Chapter 3 Analyzing the Problem and Collecting Data

Chapter 4 Creating and Analyzing Models

Chapter 5 Project Structure

Chapter 6 Data Science Stories

Chapter 7 Concept Review

Chapter 8 Manju and Jim’s Concluding Meeting

About the Author

Rajesh Jugulum, PhD, is a data science innovation, analytics and process-engineering leader. He has experience in these areas in different types of industries including healthcare, finance and manufacturing. He held executive positions in the areas of process engineering and data science at Cigna, Citi Group and Bank of America. Rajesh completed his PhD under the guidance of Dr. Genichi Taguchi. Before joining the financial industry, Rajesh was at Massachusetts Institute of Technology (MIT) where he was involved in research and teaching.

Currently, Rajesh is Co-founder and Chief Data Science & Analytics Officer at dataDragon, a cloud based data science/analytics firm. He also teaches at Northeastern University, Boston as an affiliate professor. He is also an affiliate graduate faculty at University of Arkansas, Little Rock.

Rajesh is the author/co-author of several papers and five books including books on robust quality, data quality and design for lean six sigma. Rajesh also holds two US patents. Rajesh is a Fellow of American Society for Quality (ASQ) and also a Fellow of Royal Statistical Society (RSS) and his other honors include ASQ’s Feigenbaum Medal, International Technology Institute’s Rockwell Medal and 2012 Recognition Award from Industrial and Systems Engineering Department of Wayne State University. He has been listed in the “Who’s Who in the World” list by Marquis Who’s Who publication board. He was featured as “Face of Quality” in the September, 2001 issue of Quality Progress magazine and his Profile was also published in the October 2002 issue of the Journal of Quality Engineering Society.

Rajesh has delivered talks as the keynote speaker at several conferences, symposiums, and events related to data science, analytics and process engineering. He has also delivered lectures at several universities/companies across the globe and participated as a judge in data-related competitions.