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Python for MBAs418
From the ads that track us to the maps that guide us, the twenty-first century runs on code. The business world is no different. Programming has become one of the fastest-growing topics at business schools around the world. An increasing number of MBAs are choosing to pursue careers in tech. For them and other professionals, having some basic coding knowledge is a must. This book is an introduction to programming with Python for MBA students and others in business positions who need a crash course. One of the most popular programming languages, Python is used for tasks such as building and running websites, data analysis, machine learning, and natural-language processing. Drawing on years of experience providing instruction in this material at Columbia Business School as well as extensive backgrounds in technology, entrepreneurship, and consulting, Mattan Griffel and Daniel Guetta teach the basics of programming from scratch. Beginning with fundamentals such as variables, strings, lists, and functions, they build up to data analytics and practical ways to derive value from large and complex datasets. They focus on business use cases throughout, using the real-world example of a major restaurant chain to offer a concrete look at what Python can do. Written for business students with no previous coding experience and those in business roles that include coding or working with coding teams, Python for MBAs is an indispensable introduction to a versatile and powerful programming language.
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Business leaders everywhere increasingly need top technology and data skills to stay competitive. Mattan Griffel and Daniel Guetta bring Python to life through clear and compelling stories and case studies, showing you how to use the power of variables, strings, and lists to immediately help your business and analytics. -- Glenn Hubbard, dean emeritus and Russell L. Carson Professor of Finance and Economics, Columbia Business School In the data-driven economy, there is an enormous demand for hybrid professionals who are simultaneously broad and deep across business and technical fields. Mattan Griffel and Daniel Guetta have done a great job providing a practical, step-by-step guide for commercially minded individuals to upskill quickly in the technical arena. This will be required reading for all those in my team who need to rapidly learn fundamental data and analytical skills. -- Afsheen Afshar, founder and CEO, Pilot Wave Holdings Management Business education is changing to prepare MBA students for careers in the digital age and to provide an understanding of the technological capabilities and analytics tools driving this digital transformation. Griffel and Guetta are experts in Python and its use in business analytics. This book will be an incredible resource for teaching programming to students in MBA programs and for business practitioners and managers. -- Costis Maglaras, dean and David and Lyn Silfen Professor of Business, Columbia Business School
Mattan Griffel is an award-winning adjunct assistant professor at Columbia Business School. He is also a two-time Y Combinator-backed entrepreneur and the cofounder of Ophelia and One Month. He has taught at and advised companies such as Bloomberg, JPMorgan, American Express, and PepsiCo. He studied philosophy and finance at New York University. Daniel Guetta is associate professor of professional practice at Columbia Business School and the director of the Columbia Business Analytics Initiative. He has consulted with companies around the world in fields ranging from finance to pharmaceuticals to help them solve their hardest problems using data. He studied physics and mathematics at the University of Cambridge and MIT and holds a PhD in operations research from Columbia University.
Introduction Part I 1. Getting Started with Python 2. Python Basics, Part 1 3. Python Basics, Part 2 4. Python Basics, Part 3 Part II 5. Introduction to Data in Python 6. Exploring, Plotting, and Modifying Data in Python 7. Bringing Together Datasets 8. Aggregation 9. Practice What's Next? Notes Index