Автор: Langtangen Hans Petter Название: A Primer on Scientific Programming with Python ISBN: 3662498863 ISBN-13(EAN): 9783662498866 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Есть Описание: The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches 'Matlab-style' and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science.From the reviews: Langtangen … does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. … Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science.Alex Small, IEEE, CiSE Vol. 14 (2), March?/April 2012 “This fourth edition is awonderful, inclusive textbook that covers pretty much everything one needs toknow to go from zero to fairly sophisticated scientific programming in Python…”Joan Horvath, Computing Reviews, March2015
Автор: Hill Название: Learning Scientific Programming with Python ISBN: 1107075416 ISBN-13(EAN): 9781107075412 Издательство: Cambridge Academ Рейтинг: Цена: 91860.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Learn to master basic programming tasks from scratch with real-life scientific examples drawn from many different areas of science and engineering. This complete introduction to using Python teaches Numpy, SciPy and Matplotlib libraries and is supported by extensive online resources to provide a targeted package for students and researchers.
The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization.
This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters.
Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.
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