Educational Philosophy

The philosophical underpinnings of the book are that real world problems are usually complex and often will defined; they do not come with labels, meaning that they are not necessarily regression problems or optimization problems. The only objective reality is data, which itself may be incomplete and of questionable quality, and the role of models is to facilitate the solution of real world problems. Problems and data play a leading role in this book, while models play an essential but supporting role. This is in contrast with the vast majority of books and classes available today, in which methods play the leading role.

Distinguishing Characteristics

Most chapters in the book start with a real world problem (typically from our experience) and a data set, and then use a variety of methods to address the problem. The book is organized into several application areas in which analytics has had a significant impact. It is structured in seven parts:

• Humans and Machines

• Sports and Games

• Healthcare

• The Internet

• Combating Crime

• Management of Operations

• Finance

• Methods and Exercises

The topics covered in this book include:

• IBM Deep Blue and IBM Watson

• Sports Analytics and the MIT Blackjack Team

• The Framingham Heart Study and Kidney Allocation

• Google's Search Engine and Recommendation Systems

• Fraud Detection and Predictive Policing

• Revenue Management and Emergency Room Operations

• Asset Management and Options Pricing

In the last chapters of the book, we give a brief overview of the analytics techniques used throughout the chapters, and provide exercises to help the reader put analytics into action. We have used this book to teach a one semester class at the Massachusetts Institute of Technology for an audience that included MBA, undergraduate and graduate students. We have also taught half a semester class for Executive MBA students. A pre-requisite of these courses was a basic class on regression, probability and optimization. However, we have also created a Massive Open Online Course (MOOC) through edX (15.071x), based on this book that teaches the material with no assumed background knowledge.

For All Readers

• Datasets for some chapters and the exercises.

• R Instruction Manual and R Script files.

Click here to initiate download

For Instructors

This book can be used to teach several different types of courses to a variety of students. We have used this book to teach a one semester class at the Massachusetts Institute of Technology (MIT) Sloan School of Management for an audience that included MBA, undergraduate and graduate students. We also taught a shorter class for Executive MBA students focusing on Chapters 1, 3, 4, 7, 8, 9, 11, 13, 14 and 19. A pre-requisite of these courses was a basic class on regression, probability and optimization. However, we have also created a Massive Open Online Course (MOOC) through edX (15.071x), based on this book that teaches the material with no assumed background knowledge. An instructor resource form with solutions to the exercises will be provided upon request for those faculty who adopt the book as a required text book for their course. Please fill out the form in the "For Instructors" section under Resources section.