This book is an introduction to programming with Python for MBA students and others in business positions who need a crash course. Beginning with fundamentals such as variables, strings, lists, and functions, it builds up to data analytics and practical ways to derive value from large and complex datasets.
This book is an introduction to programming with Python for MBA students and others in business positions who need a crash course. Beginning with fundamentals such as variables, strings, lists, and functions, it builds up to data analytics and practical ways to derive value from large and complex datasets.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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