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🔔Einführung in die Berechnung und Programmierung mit Python von J.V. Guttag
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eBay-Artikelnr.:305982481177
Artikelmerkmale
- Artikelzustand
- ISBN
- 9780262529624
Über dieses Produkt
Product Identifiers
Publisher
MIT Press
ISBN-10
0262529629
ISBN-13
9780262529624
eBay Product ID (ePID)
221599978
Product Key Features
Number of Pages
472 Pages
Publication Name
Introduction to Computation and Programming Using Python, Second Edition : With Application to Understanding Data
Language
English
Publication Year
2016
Subject
Programming / General, Programming Languages / Python
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Height
0.9 in
Item Weight
26.1 Oz
Item Length
8.9 in
Item Width
7.1 in
Additional Product Features
Edition Number
2
Intended Audience
Trade
LCCN
2016-019367
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
005.133
Synopsis
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. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). 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., 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. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). 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.
LC Classification Number
QA76.73.P98G88 2017
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