Introduction to Computation and Programming Using Python: With Application to Understanding Data (MIT Press)by: John V. Guttag
ISBN-10: 0262529629
ISBN-13: 9780262529624
Edition 版次: second edition
Publication Date 出版日期: 2016-08-12
Print Length 页数: 472
Book Description
By finelybook
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.
Contents
Chapter 1 Getting Started
Chapter 2 Introduction To Python
Chapter 3 Some Simple Numerical Programs
Chapter 4 Functions,Scoping,And Abstraction
Chapter 5 Structured Types,Mutability,And Higher-Order Functions
Chapter 6 Testing And Debugging
Chapter 7 Exceptions And Assertions
Chapter 8 Classes And Object-Oriented Programming
Chapter 9 A Simplistic Introduction To Algorithmic Complexity
Chapter 10 Some Simple Algorithms And Data Structures
Chapter 11 Plotting And More About Classes
Chapter 12 Knapsack And Graph Optimization Problems
Chapter 13 Dynamic Programming
Chapter 14 Random Walks And More About Data Visualization
Chapter 15 Stochastic Programs,Probability,And Distributions
Chapter 16 Monte Carlo Simulation
Chapter 17 Sampling And Confidence Intervals
Chapter 18 Understanding Experimental Data
Chapter 19 Randomized Trials And Hypothesis Checking
Chapter 20 Conditional Probability And Bayesian Statistics
Chapter 21 Lies,Damned Lies,And Statistics
Chapter 22 A Quick Look At Machine Learning
Chapter 23 Clustering
Chapter 24 Classification Methods
图书说明
本书介绍了具有很少或没有先前的编程经验的学生，使用Python和各种Python库（包括PyLab）来解决计算问题的技术。它为学生提供了能够使他们有效利用计算技术的技能，包括使用计算来建模和解释数据的数据科学的一些工具和技术。该书基于麻省理工学院课程（成为麻省理工学院开放式课程提供的最受欢迎课程），不仅在常规教室中开发，而且在大量开放式在线课程（MOOC）中开发。这个新版本已经为Python 3进行了更新，重新组织，使其更容易用于仅覆盖材料的一部分的课程，并提供了额外的材料，包括五个新章节。
学生被介绍到Python和编程的基础上，在诸如穷举枚举，平分搜索和高效近似算法之类的计算概念和技术的上下文中。虽然它涵盖了诸如计算复杂性和简单算法这样的传统主题，但是本书着重于大多数介绍性文本中没有发现的广泛主题，包括信息可视化，模型随机性的模拟，理解数据的计算技术以及通知（并误导）以及两个相关但相对较高级的课题: 优化问题和动态规划。本版本提供有关统计学和机器学习的扩展材料以及关于常数和贝叶斯统计的新章节。
目录
第1章入门
第2章Python简介
第3章一些简单的数值程序
第4章功能，范围和抽象
第5章结构类型，可变性和高阶函数
第6章测试和调试
第七章例外与断言
第8章类和面向对象编程
第9章简单的算法复杂性介绍
第10章一些简单的算法和数据结构
第11章绘制和更多关于课程
第12章背包和图优化问题
第13章动态规划
第14章随机游走更多关于数据可视化
第15章随机程序，概率和分布
第16章蒙特卡罗模拟
第17章取样和置信区间
第18章了解实验数据
第19章随机试验和假设检查
第二十条条件概率与贝叶斯统计
第二十一章谎言，死亡谎言和统计
第22章快速查看机器学习
第23章聚类
第24章分类方法
The MIT Press Introduction to Computation and Programming Using Python 2nd Edition 0262529629.epub
The MIT Press Introduction to Computation and Programming Using Python 2nd Edition 0262529629.pdf[/erphpdown]
