Introduction To Computation & Programming Using Python

Description

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. 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.

“This is the ‘computational thinking’ book we have all been waiting for! With humor and historical anecdotes, John Guttag conveys the breadth and joy of computer science without compromising technical detail. The second edition includes brand new material that focuses on computational approaches to understanding data, complementing traditional computational problem solving.”

—Jeannette M. Wing, Corporate Vice President, Microsoft Research, and Consulting Professor of Computer Science and former Department Head,Carnegie Mellon University

John Guttag is an extraordinary teacher and an extraordinary writer. This is not ‘a Python book,’ although you will learn Python. Nor is it a ‘programming book,’ although you will learn to program. It is a rigorous but eminently readable introduction to computational problem solving, and now also to data science—this second edition has been expanded and reorganized to reflect Python’s role as the language of data science.

—Ed Lazowska, Bill & Melinda Gates Chair in Computer Science & Engineering, and Director of the eScience Institute, University of Washington

This textbook is recommended in many prestigious universities and institutes including

• Aligarh Muslim University
• Anna University
• GLA University
• Gujarat Technological University
• SRM University
• University of Calcutta
• University of Delhi
• Vishwakarma Institute of Technology

Table of Contents

1. Preface
2. Acknowledgments
   
3. Getting Started
   
4. Introduction to Python
   
5. Some Simple Numerical Programs
   
6. Functions, Scoping, and abstraction
   
7. Structured Types, Mutability, and Higher-Order Functions
   
8. Testing and Debugging
   
9. Exceptions and Assertions
   
10. Classes and Object-Oriented Programming
    
11. A Simplistic Introduction to Algorithmic Complexity
    
12. Some Simple Algorithms and Data Structures
    
13. Plotting and More About Classes
    
14. Knapsack and Graph Optimization Problems
    
15. Dynamic Programming
    
16. Random Walks and More About Data Visualization
    
17. Stochastic Programs, Probability, and Distributions
    
18. Monte Carlo Simulation
    
19. Sampling and Confidence Intervals
    
20. Understanding Experimental Data
    
21. Randomized Trials and Hypothesis Checking
    
22. Conditional Probability and Bayesian Statistics
    
23. Lies, Damned Lies, and Statistics
    
24. A Quick Look at Machine Learning
    
25. Clustering
    
26. Classification Methods
    
27. Python 3.5 Quick Reference
28. Index

Author Biography

John V. Guttag, Dugald C. Jackson Professor of Computer Science and Electrical Engineering, MIT.