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Modeling and Simulation in Python teaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math.
Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modeling that is, the art of describing and simulating real-world systems. Readers are guided through modeling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions.
Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations.
List of contents
Introduction
Part I: Discrete Systems
Chapter 1: Modeling
Chapter 2 Bike Share System
Chapter 3: Iteration
Chapter 4: Sweeping Parameters
Chapter 5: World Population
Chapter 6: Proportional Growth
Chapter 7: Limits to Growth
Chapter 8: Projecting Population Growth
Chapter 9: Analysis of Population Growth
Chapter 10: Case Studies Part 1
Part II: First Order Systems
Chapter 11: Epidemiology
Chapter 12: Modeling Vaccination
Chapter 13: Sweeping Parameters
Chapter 14: Nondimensionalization
Chapter 15: Cooling Coffee
Chapter 16: Adding Milk
Chapter 17: Pharmacokinetics
Chapter 18: Glucose and Insulin
Chapter 19: Case Studies Part 2
Part III: Second Order Systems
Chapter 20: Pennies
Chapter 21: Drag
Chapter 22: Baseball
Chapter 23: Optimization
Chapter 24: Rotation
Chapter 25: Torque
Chapter 26: Case Studies Part 3
Appendix A Under the Hood
About the author
Allen Downey is a Staff Scientist at DrivenData and Professor Emeritus at Olin College, where he taught Modeling and Simulation and other classes related to software and data science. He is the author of several textbooks, including Think Python, Think Bayes, and Elements of Data Science. Previously, he taught at Wellesley College and Colby College. He received his Ph.D. in computer science from the University of California, Berkeley in 1997. His undergraduate and master's degrees are from the Civil Engineering department at MIT. He is the author of Probably Overthinking It, a blog about data science and Bayesian statistics.
Summary
Modeling and Simulation in Python teaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math.
Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modeling—that is, the art of describing and simulating real-world systems. Readers are guided through modeling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions.
Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations.
Additional text
"Modeling and Simulation in Python is an introduction to physical modeling using a computational approach . . . Taking a computational approach makes it possible to work with more realistic models than what you typically see in a first-year physics class, with the option to include features like friction and drag."
—Python Kitchen
Report
"Modeling and Simulation in Python is an introduction to physical modeling using a computational approach . . . Taking a computational approach makes it possible to work with more realistic models than what you typically see in a first-year physics class, with the option to include features like friction and drag."
Python Kitchen
