Introduction To Programming Using Python An Global Edition
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Introduction to Programming Using Python, An, Global Edition
Author: David I. Schneider
language: en
Publisher: Pearson Higher Ed
Release Date: 2015-10-07
For college-level Computer Science courses in Python Basic Programming and Problem Solving in Python As one of the most widely used programming languages in the software industry, Python is desirable to both learn and teach. Introduction to Programming Using Python is designed for students eager to learn about the world of programming. Applicable to a range of skill levels, this First Edition textbook provides students with the tools to harness the powerful syntax of Python and understand how to develop computer programs. The compactly written text leverages highly focused chapters, diving deep into the most significant topics to give students an in-depth (rather than superficial) understanding of the language. Using real-world examples and data, the author illustrates practical usage of Python in a way to which students can relate. The text itself is readable, organised, and informative, discussing main points of each topic first and then addressing the peripheral details. Students learn good programming habits the first time—bringing them in line with the best modern programming practices. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.
Introduction to Computation and Programming Using Python, third edition
The new edition of an introduction to the art of computational problem solving using Python. 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 numpy, matplotlib, random, pandas, and sklearn. 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 as well as substantial material on machine learning. All of the code in the book and an errata sheet are available on the book’s web page on the MIT Press website.
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.