Introduction to Computation and Programming Using Python: With Application to Understanding Data, 2nd Edition

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.

Table of 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