Scientific Computation: Python Hacking for Math Junkies - Couverture souple

Shapiro, Bruce E

Couverture souple

ISBN 10 : 0692366938 ISBN 13 : 9780692366936

Editeur : Sherwood Forest Books, 2015

Synopsis

There is a newer edition (ISBN 978-1725894662) of this book that actually covers Python 3.6.

This is a book about hacking, but not just any kind of hacking. It is about mathematical hacking, or scientific computing. If you like math and want to use computers to do math or solve mathematical problems, then this book is the for you.

This is version 3, which includes over 80 additional pages, with extensive additions on Python classes, iPython with jupyter notebooks, magic functions, random numbers, and probability and statistics. The emphasis throughout the text is on programming in the iPython notebook. Version 3.0.3 (2017) is revised and corrected.

Part I gives an introduction to programming, the Python shell, and using iPython notebooks.

Part II gives a thorough tour of the Python language. Topics include data types, simple statements, assignments, conditionals, loops, list comprehension, sorting, Numpy, pyplot, using dictionaries, functions, classes, functional programs, lambda functions, and recursion.

Part III gives applications, including probability and statistics, and topics from machine learning such as clustering, linear regression, logistic regression and principal component analysis. Other topics include image analysis, the singular value decomposition, solving initial value problems, discrete systems, fractals, estimating pi, geographic maps, and satellite orbit analysis.

Over 300 problems are included that can be used for student programming projects.

No programming background required. Some basic understanding of math through calculus expected for the applications.

A propos du livre

Présentation de l'éditeur

Computers are wonderful things. They improve our lives and produce works of art. Information and communication are free and available in ways that were undreamed of by many a generation ago.

And yet computers inspire fear when they are used improperly or misunderstood.

Everyone uses computers today. The typical student uses at least three: a portable notebook computer, a tablet, and a cell phone. None of these even existed before this year's college graduating class was born. So a reasonable amount of computer literacy is expected of everyone these days.

Though inspired by mathematics, much of the mathematical beauty of computation is largely ignored by both elementary computer science and introductory programming books.

This book is designed to help math junkies - anyone who likes math, studies math, or uses math in their daily life - learn about computation. The emphasis is on algorithms. It is appropriate for students with no prior programming experience as well as professional scientists.

Contents: Python expressions, statements, types, lists, arrays, functions, classes, plotting list comprehension and recursion are coverd in detail. Applicatications include linear systems, computatational geometry, root finding, interpolation, polynomial least squares, discrete systems, differential equations, pricipal component analysis, fractals and chaos.

Target audience: undergraduate math and science students entering their upper level curriculum. Calculus and some linear algebra background is helpful; a review of linear algebra is included as an appendix. No prior programming experience is expected.

Présentation de l'éditeur

There is a newer edition (ISBN 978-1725894662) of this book that actually covers Python 3.6.

This is a book about hacking, but not just any kind of hacking. It is about mathematical hacking, or scientific computing. If you like math and want to use computers to do math or solve mathematical problems, then this book is the for you.

This is version 3, which includes over 80 additional pages, with extensive additions on Python classes, iPython with jupyter notebooks, magic functions, random numbers, and probability and statistics. The emphasis throughout the text is on programming in the iPython notebook. Version 3.0.3 (2017) is revised and corrected.

Part I gives an introduction to programming, the Python shell, and using iPython notebooks.

Part II gives a thorough tour of the Python language. Topics include data types, simple statements, assignments, conditionals, loops, list comprehension, sorting, Numpy, pyplot, using dictionaries, functions, classes, functional programs, lambda functions, and recursion.

Part III gives applications, including probability and statistics, and topics from machine learning such as clustering, linear regression, logistic regression and principal component analysis. Other topics include image analysis, the singular value decomposition, solving initial value problems, discrete systems, fractals, estimating pi, geographic maps, and satellite orbit analysis.

Over 300 problems are included that can be used for student programming projects.

No programming background required. Some basic understanding of math through calculus expected for the applications.

Détails de l'édition

• Éditeur: Sherwood Forest Books
• Date d'édition: 2015
• Langue: anglais
• ISBN 10: 0692366938
• ISBN 13: 9780692366936
• Reliure: Broché
• Nombre de pages: 554
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible