Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modelling - that is, the art of describing and simulating real-world systems. Readers are guided through modelling 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.
Rezensionen / Stimmen
"An excellent choice for students and professionals alike . . . Straightaway, the book takes us into modeling, using basic Python concepts. With each chapter more complex modeling use cases and language features are being introduced. . . . I like the way A. Downey combined teaching modeling with building Python development skills. It is, in my view, a very effective (and more enjoyable) way of learning."
-Peter Schmidt, host of the Code for Thought podcast and Senior Software Engineer at University of College London
"Modeling and Simulation in Python is an introduction to physical modeling using a computational approach . . . making it possible to work with more realistic models than what you typically see in a first-year physics class."
-Python Kitchen
"Downey's top-down approach, context-rich and motivating, dramatically lowers the barrier to gaining literacy in programming and explicitly and insightfully teaches modeling. . . . I'm grateful for this book."
-Phat Vu, Director of the Science & Mathematics Program, Soka University of America
"An impressive introduction to physical modeling and Python programming, featuring clear, concise explanations and examples. . . . perfect for readers of any level."
-Christian Mayer, founder of the Coding Academy Finxter.com and author of Python One-Liners
"Downey uses a combination of Python, calculus, bespoke helper functions, and easily accessible online materials to model a diverse and interesting set of simulation projects. In the process, he presents a practical and reusable framework for modeling dynamical systems with Python."
-Lee Vaughan, former Senior Principal Scientist for Geological Modeling at ExxonMobil and author of Python Tools for Scientists, Real-World Python, and Impractical Python Projects
"Provides a wealth of instructive examples of all kinds of modeling. . . . a valuable textbook for classes on scientific computation or guide to exploration for interested amateurs."
-Bradford Tuckfield, author of Dive into Algorithms and Dive Into Data Science
"An ideal introduction to Python and its predictive applications, [Modeling and Simulation in Python] is comprehensive, exceptionally well organized, and thoroughly 'user friendly' in presentation."
-Midwest Book Review
"It's a lovely book that doesn't take long to read, while managing to cover lots of different ideas...Definitely worth a read if you want to play around modeling some equations."
-Frances Buontempo, The Magazine of the ACCU
"Through a blend of accessible science and practical examples, Downey's book demystifies the complex world of simulations, offering readers an invaluable arsenal of modeling techniques. With Python at its core, this guide illuminates the path from theory to application, making it an essential resource for anyone looking to master the art of simulation in science and technology."
-c't Magazin
Sprache
Verlagsort
Maße
Höhe: 234 mm
Breite: 180 mm
Dicke: 22 mm
Gewicht
ISBN-13
978-1-7185-0216-1 (9781718502161)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
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
