INTRODUCTION TO COMPUTATION AND PROGRAMMING USING PYTHON-------PHI

₹395.00 ₹375.00

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.

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. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. 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 understanding data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.

Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

Contents:

Preface

Acknowledgments

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. Stochastic Programs, Probability and Statistics

13. Random Walks and More About Data Visualization

14. Monte Carlo Simulation

15. Understanding Experimental Data

16. Lies, Damned Lies, and Statistics

17. Knapsack and Graph Optimization Problems

18. Dynamic Programming

19. A Quick Look at Machine Learning