Hardcover. This advanced undergraduate textbook presents a new approach to teaching mathematical methods for scientists and engineers. It provides a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses. Both analytical and computational examples are integrated from its start. Each chapter concludes with a set of problems designed to help students hone their skills in mathematical techniques, computer programming, and numerical analysis. The book places less emphasis on mathematical proofs, and more emphasis on how to use computers for both symbolic and numerical calculations. It contains 182 extensively documented coding examples, based on topics that students will encounter in their advanced courses in Mechanics, Electronics, Optics, Electromagnetism, Quantum Mechanics etc.An introductory chapter gives students a crash course in Python programming and the most often used libraries (SymPy, NumPy, SciPy, Matplotlib). This is followed by chapters dedicated to differentiation, integration, vectors and multiple integration techniques. The next group of chapters covers complex numbers, matrices, vector analysis and vector spaces. Extensive chapters cover ordinary and partial differential equations, followed by chapters on nonlinear systems and on the analysis of experimental data using linear and nonlinear regression techniques, Fourier transforms, binomial and Gaussian distributions. The book is accompanied by a dedicated GitHub website, which contains all codes from the book in the form of ready to run Jupyter notebooks. A detailed solutions manual is also available for instructors using the textbook in their courses.Key Features:A unique teaching approach which merges mathematical methods and the Python programming skills which physicists and engineering students need in their coursesUses examples and models from physical and engineering systems, to motivate the mathematics being taughtStudents learn to solve scientific problems in three different ways: traditional pen-and-paper methods, using scientific numerical techniques with NumPy and SciPy, and using Symbolic Python (SymPy). This advanced undergraduate textbook presents a new approach to teaching mathematical methods for scientists and engineers. It provides a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

N� de ref. del art�culo 9781032278360

Contactar al vendedor

Denunciar este art�culo

Detalles bibliogr�ficos

T�tulo: Mathematical Methods using Python (Hardcover)
Editor: Taylor & Francis Ltd, London
Fecha de publicaci�n: 2024
Idioma: Ingl�s
ISBN 10: 1032278366
ISBN 13: 9781032278360
Encuadernaci�n: Hardcover
Estado: new

Acerca de

Sinopsis

This advanced undergraduate textbook presents a new approach to teaching mathematical methods for scientists and engineers. It provides a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses.

Acerca del autor

Vasilis Pagonis is Professor of Physics Emeritus at McDaniel College, Maryland, USA. His research area is applications of thermally and optically stimulated luminescence. He taught courses in mathematical physics, classical and quantum mechanics, analog and digital electronics and numerous general science courses. Dr. Pagonis’ resume lists more than 200 peer-reviewed publications in international journals. He is currently associate editor of the journal Radiation Measurements. He is co-author with Christopher Kulp of the undergraduate textbook “Classical Mechanics: a computational approach, with examples in Python and Mathematica” (CRC Press, 2020). He has also co-authored four graduate level textbooks in the field of luminescence dosimetry, and most recently published the book “Luminescence Signal analysis using Python” (Springer, 2022).

Christopher Kulp is the John P. Graham Teaching Professor of Physics at Lycoming College. He has been teaching undergraduate physics at all levels for 20 years. Dr. Kulp’s research focuses on modelling complex systems, time series analysis, and machine learning. He has published 30 peer-reviewed papers in international journals, many of which include student co-authors. He is also co-author of the undergraduate textbook “Classical Mechanics: a computational approach, with examples in Python and Mathematica” (CRC Press, 2020).

"Sobre este t�tulo" puede pertenecer a otra edici�n de este libro.

Descripci�n de la librer�a

Online business

Ver la p�gina web de la librer�a

Informaci�n empresarial del vendedor

ABC BOOKS LIMITED
10 John Street, London, WC1N 2EB, United Kingdom

Condiciones de venta y env�o

Condiciones de venta

Orders can be returned within 30 days of receipt.

Condiciones de env�o

Please note that titles are dispatched from our US, Canadian or Australian warehouses. Delivery times specified in shipping terms. Orders ship within 2 business days. Delivery to your door then takes 7-14 days.