Introduction To Computation And Programming Using Python John V. Guttag Pdf

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Introduction to Computation and Programming Using Python, second edition

The new edition of an introduction to the art of computational problem solving using Python. 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 numpy, matplotlib, random, pandas, and sklearn. 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 as well as substantial material on machine learning.

The book is based on an MIT course and was developed for use not only in a conventional classroom but in a massive open online course MOOC. It contains material suitable for a two-semester introductory computer science sequence. All the code has been rewritten to make it stylistically consistent with the PEP 8 standards.

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.

The book also includes a Python 3 quick reference guide. All of the code in the book and an errata sheet are available on the book's web page on the MIT Press website. John V. Search Search. Search Advanced Search close Close. Guttag The new edition of an introduction to the art of computational problem solving using Python. Request Permissions Exam copy. Overview Author s. Summary The new edition of an introduction to the art of computational problem solving using Python.

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Introduction to Computation and Programming Using Python, Second Edition

This is the 'computational thinking' book we have all been waiting for! With humor and historical. This is not 'a Python book,' although you will. Nor is it a 'programming book,' although you will learn to program. It is a rigorous but.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Guttag Published Computer Science. 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. Save to Library.


Introduction to. Computation and. Programming Using Python. (Spring Edition). John V. Guttag. The MIT Press. Cambridge, Massachusetts. London.


John V. Guttag Introduction to Computation and Programming Using Python

The new edition of an introduction to the art of computational problem solving using Python. 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 numpy, matplotlib, random, pandas, and sklearn. 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 as well as substantial material on machine learning. The book is based on an MIT course and was developed for use not only in a conventional classroom but in a massive open online course MOOC.

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The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. 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.

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Phone or email. Don't remember me. Savuth Dav. 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.

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Introduction to Computation and Programming Using Python by John V. Guttag PDF Download

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