Python Programming In Context Pdf
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Introduction to Computation and Programming Using Python, second edition
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.
Python Programming in Context
Author: Julie Anderson
language: en
Publisher: Jones & Bartlett Learning
Release Date: 2024-04-15
Python Programming in Context, Fourth Edition provides a comprehensive and accessible introduction to Python fundamentals. Updated with Python 3.10, the Fourth Edition offers a thorough overview of multiple applied areas, including image processing, cryptography, astronomy, the Internet, and bioinformatics. Taking an active learning approach, each chapter starts with a comprehensive real-world project that teaches core design techniques and Python programming to immediately engage students. An ideal first language for learners entering the rapidly expanding fields of computer science, data science, and scientific programing, Python gives students a solid platform of key problem-solving skills that translate easily across programming languages. This text is designed to be a first course in computer science that focuses on problem-solving, with language features being introduced as needed to solve the problem at hand.
A Primer on Scientific Programming with Python
Author: Hans Petter Langtangen
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-07-04
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.