Python for MBAs

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Python for MBAs by Mattan Griffel

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HELLO! We are Mattan Griffel and Daniel Guetta, and we’re going to teach you about Python. Before we introduce ourselves, we want to tell you a little bit about the intended audience for this book, what we’ll be learning together, and some tips on how you can get the most out of reading this book.

This book is designed for people who have never coded before; so if you’re feeling intimidated, don’t be. In fact, even if the only kind of Python you’ve heard of is a snake, or if you’re not sure exactly what coding is, you’ll feel right at home—we’ll discuss both in chapter 1. Businesspeople without a technical background decide to start learning how to code for a lot of different reasons. Some want an introduction to a different way of thinking—they realize the world runs on code, and they don’t want to be left out. Some are looking for ways to write simple scripts to streamline or automate their work. Others work with coders and technical teams on a day-to-day basis and want to better understand what those teams do. Some are tired of relying on overworked business intelligence teams to get answers from their data and want to be more self-sufficient.

Whichever category you fall into—this book is for you. The material is based on classes we have taught for a number of years at Columbia Business School to professionals just like you. We are going to show you how to use Python to do all kinds of useful things, like automating repetitive tasks to save yourself time and money and performing data analyses to answer important business questions on files far too large and complex to be handled in a spreadsheet.

Hopefully, you’ll find that this book provides valuable insight into what’s possible using technology and gives you a new skill that you can immediately use in a business context.

We have divided this book into two parts. In part 1, we will learn the basics of Python (loops, variables, lists, and whatnot), and in part 2, we’ll dive into ways Python can be used to analyze datasets in a real-world business context.

Unless you are already familiar with Python, you should begin by reading part 1 in order, from start to finish—resist the temptation to jump around. You will learn the fundamental knowledge you need to do anything in Python. This part of the book also contains a number of exercises, and we encourage you to spend some time working on them. If you just read the book without trying these problems, you’ll still learn something, but you may not remember it as well. The companion website for this book contains digital versions of every piece of code, but for the same reason, we recommend typing it in by hand rather than just copying and pasting the code. If some questions pop into your mind like, “What happens when I do X?” then try doing it! In the worst-case scenario, it doesn’t work. Then, you can revisit what we were showing you. Either way, you’ll learn something new.

Part 2 is about using Python to analyze data in a business context. You should begin by reading chapter 5, which introduces a different way to write code in Python. This chapter also introduces the story of Dig, a restaurant chain based in New York that we will return to again and again in this part of the book. We have found that many Python books, even basic ones, seem to be written for engineers—they focus on functionality rather than on how that functionality might be used. By rooting part 2 in a real-life case study, we show you what Python can do for you rather than teaching it in a vacuum. In the remaining chapters, we discuss how Dig’s challenges can be addressed using data. We build on the Python fundamentals you learned in part 1 to show you how to use massive datasets to answer these questions.

Our aim is to teach you the basics of Python and to provide you with a map so that you can decide what you want to learn more about on your own. As a result, we’ll sometimes use informal terminology, and skip over some more technical details. This is a deliberate choice on our part—it will prevent us from getting too bogged down with details that aren’t essential and will help you get to applications as quickly as possible. In our conclusion, we will point you to resources you can use to take what you’ve learned to the next level, if you’re interested.

One of Python’s key strengths is the speed at which it evolves. Thousands of developers around the world donate their time and energy to improve the language and make it faster, richer, and more powerful. The speed at which Python develops is so rapid that some features we cover in part 2 didn’t even exist when we started writing it. We have created a companion website to this book (available at to ensure that it stays up to date as the language evolves. As we become aware of relevant changes, we will endeavor to post them there. In addition, the website will contain digital versions of every piece of code in the book and the datasets we use in part 2, as well as appendices on topics we did not have the space to cover in this book.

In writing this book, we benefited from the help of many people to whom we are infinitely grateful. First and foremost, the students who sat in our classes as we developed the material and gave us their invaluable feedback—if only we were sure not to forget anyone, we would name them all here. We are particularly thankful to (in alphabetical order) Joao Almeida, George Curtis, Nicholas Faville, Nicola Kornbluth, Royd Lim, Brett Martin, Veronica Miranda, Jason Prestinario, and Saron Yitbarek, all of whom took the time to read earlier versions of the manuscripts and give us their comments. It has been a privilege to be at Columbia Business School at a time of tremendous innovation in the area of technology and analytics. The school encouraged us to push the boundaries of what a “traditional” MBA class looks like, and this book was born of these efforts. We are particularly grateful to Dean Costis Maglaras and to members of the Decision, Risk, and Operations Division for their steadfast support. Last but not least, we are eternally grateful to Shereen Asmat, Molly Fisher, and Dig’s leadership team for graciously volunteering their time to talk to us about their company. At their request and to protect proprietary information pertaining to their business, many of the details in this book are based on our conversations but otherwise are fictionalized, and the dataset we work with was synthetically generated.

We both love coding, and we love sharing our excitement with others—there is nothing like seeing a student run their first line of code, or their first data analysis. It has been a true pleasure taking our experience doing this in classrooms and converting it to a book for you to read. We look forward to going on this journey with you all!

From coronavirus-induced self-isolation in New York April 2020



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