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Introduction To Computation And Programming Using Python: With Application To Understanding Data

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. 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.


  • Introduction to Computation and Programming Using Python by John Guttag.
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With humor and historical anecdotes, John Guttag conveys the breadth and joy of computer science without compromise to technical detail. This book is perfect for any student who wants to explore the essence of computer science. Perhaps having been an undergraduate English major -- an uncommon stepping stone to the leadership of the world's top EECS department -- has something to do with this.

《Introduction to Computation and Programming Using Python》的笔记-Week 3 Structured Types

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.

But if you had to pick only one, this would be a great choice. You'll begin by getting a solid introduction to programming in Python.

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Armed with that, you'll go hands-on with important computing ideas like random methods, statistics, and optimization, using tools of great theoretical beauty and great practical importance. Convert currency.

Python Tutorial for Beginners [Full Course] 2019

Add to Basket. For a better shopping experience, please upgrade now. Javascript is not enabled in your browser. Enabling JavaScript in your browser will allow you to experience all the features of our site. Learn how to enable JavaScript on your browser. 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.

Introduction to Computational Thinking and Data Science

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.