Introduction to Computation and Programming Using Python (2版)

書名：Introduction to Computation and Programming Using Python 2/E 作者：GUTTAG 出版社：Norton 出版日期：2016/00/00 ISBN：9780262529624 內容簡介 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. 目錄 1 Getting Started 2 Introduction to Python 3 Some Simple Numerical Programs 4 Functions, Scoping, and Abstraction 5 Structured Types, Mutability, and Higher-Order Functions 6 Testing and Debugging 7 Exceptions and Assertions 8 Classes and Object-Oriented Programming 9 A Simplistic Introduction to Algorithmic Complexity 10 Some Simple Algorithms and Data Structures 11 Plotting and More About Classes 12 Knapsack and Graph Optimization Problems 13 Dynamic Programming 14 Random Walks and More About Data Visualization 15 Stochastic Programs, Probability, and Distributions 16 Monte Carlo Simulation 17 Sampling and Confidence Intervals 18 Understanding Experimental Data 19 Randomized Trials and Hypothesis Checking 20 Conditional Probability and Bayesian Statistics 21 Lies, Damned Lies, and Statistics 22 A Quick Look at Machine Learning 23 Clustering 24 Classification Methods