When Matt Taddy earned his PhD in applied mathematics and statistics from the University of California, Santa Cruz, in 2008, the notion of a data-science specialization was still in its infancy.
Today, the business-analytics profession, or the discipline of using data to make business, public policy, public health, and other decisions, is blossoming, and Taddy is excited about how the field is becoming more multi-disciplinary, incorporating statistics, machine learning, economics, and even the social sciences.
“I benefitted from getting involved in the early stages before it became more specialized,” says Taddy.
Since earning his PhD, Taddy has been a research assistant at NASA Ames Research Center and Sandia National Laboratories, a research fellow at eBay, the head of economics and data science for Business AI at Microsoft, a professor of econometrics and statistics at the University of Chicago Booth School of Business, the chief economist for Amazon’s North America Consumer organization, and now vice president of Amazon’s Private Brands business. His first textbook, Business Data Science, was published by McGraw Hill in 2019. At the time, he told Amazon Science that he began work on the book ten years prior when teaching a class of MBA students at the University of Chicago.
“I realized that there was an appetite for the material covered in the book from people who weren’t specialists in statistics or machine learning,” he said. “This idea that we could teach this material to non-specialists really motivated me not to just write this book, but also to push for changing the curriculum at the University of Chicago.”
Since publishing that textbook in 2019, his role at Amazon has evolved as has his interest in making great decisions from data. The result is a new textbook, Modern Business Analytics: Practical Data Science for Decision Making, which Taddy co-authored with Leslie Hendrix, PhD, associate professor at the Darla Moore School of Business at the University of South Carolina, and Matthew C. Harding, PhD, professor of economics and statistics at the University of California, Irvine.
According to the authors, “This book is a primer for those who want to gain the skills to use data science to help make decisions in business and beyond. The modern business analyst uses tools from machine learning, economics, and statistics to not only track what has happened but predict the future for their businesses.”
McGraw Hill, the book’s publisher, says: “This new higher-ed text takes a practical, modern approach to data science and business analytics for the analytics student and professional. It gives students the opportunity to learn by doing, with real data analysis examples that explain the ‘why’, rather than the ‘what’ in decision-making discussions. It uses R as the primary technology through the text and includes an end-of-chapter reference to the basic R recipes in each chapter. Modern Business Analytics: Practical Data Science for Decision Making has crossed the boundaries and created something truly interdisciplinary.”
Amazon Science connected with Taddy to discuss how his thinking about the topic has evolved in the past three years, his belief that deeper business decisions require focusing on why things happen versus what has happened, and how he’s applying modern business analytics techniques in running Amazon’s Private Brands business.
