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Introduction to Computation and Programming Using Python : With Application to Understanding Data (second edition)

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Download Introduction to Computation and Programming Using Python : With Application to Understanding Data (second edition) written by John V. Guttag in PDF format. This book is under the category Computers - Programming and bearing the isbn/isbn13 number 0262529629/9780262529624. You may reffer the table below for additional details of the book. We do NOT provide access codes, we provide eBooks ONLY. Instant access will be granted as soon as you complete the payment.

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Specifications

book-author

John V. Guttag

file-type

PDF

isbn10

0262529629

isbn13

9780262529624

language

English

pages

466

publisher

The MIT Press


Book Description

Students who have had little to no prior experience with programming will find this book to be an excellent resource for learning the art of computational problem solving with Python and a variety of Python modules, including PyLab. It teaches students the abilities that will allow 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. It does this by providing students with skills that will allow them to make productive use of computational techniques. The book, which was intended for use not only in traditional classrooms but also in massive open online courses, is based on a course that was taught at MIT and eventually became the most popular course that was made available through MIT's OpenCourseWare (MOOC). This new edition has been brought up to date for Python 3, it has been rearranged to make it simpler to use for classes that cover only a fraction of the content, and it provides more material, which includes five new chapters.

Students are given an overview of Python as well as the fundamentals of programming within the framework of various concepts and approaches to computing, such as exhaustive enumeration, bisection search, and effective approximation methods. The book focuses on a wide range of topics that are not found in the majority of introductory texts. These topics include information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform). Additionally, the book focuses on two related but relatively advanced topics: optimization problems and dynamic programming. This edition adds new chapters on frequentist and Bayesian statistics, in addition to expanding the previous material on statistics and machine learning.

book-author

John V. Guttag

file-type

PDF

isbn10

0262529629

isbn13

9780262529624

language

English

pages

466

publisher

The MIT Press

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