JOHN GUTTAG PYTHON BOOK PDF

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 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.

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Goodreads helps you keep track of books you want to read. Want to Read saving…. Want to Read Currently Reading Read. Other editions. Enlarge cover. Error rating book. Refresh and try again. Open Preview See a Problem? Details if other :. Thanks for telling us about the problem. Return to Book Page. 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 mod 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 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.

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To ask other readers questions about Introduction to Computation and Programming Using Python , please sign up. Lists with This Book. Community Reviews. Showing Average rating 4. Rating details.

More filters. Sort order. I chose to read and review this book purely based on the title rather than doing my normal level of research. I was interested in learning more about the Python language, partly because I use Jython on a regular basis Jython is an implementation of Python, written in Java. The difference may sound quite subtle, and perhaps a bit negative. However, that is the precise opposite of what I want to suggest - this is an excellent book, one that I would have dearly loved to have when I first started to learn the subject of computer science back in the s.

The book begins by introducing Python, including the basic elements of the language such as objects, expressions, typing, variables, branches and strings.

Much of this is very translatable to other languages and would be useful to anyone learning the basic fundamentals of computer programming, regardless of the language. It then continues to explain the fundamentals of programming, including functions, abstraction, testing, debugging, exception handling and object-oriented OO programming. At that point, the book dives into the science element of computer science, including algorithmic complexity, probability theory, graphing and statistical analysis.

In its entirety, the book should be considered an excellent introduction to computer science and programming - as mentioned previously, I'd have loved to have this book when I started out in information science some 30 years ago.

However, if one's objective is to learn Python, it will be necessary to supplement this book with some specific Python tutorials, although the first few chapters will definitely help to establish context and understand the fundamental building blocks of the language.

Even one such as I, who has been working in the IT industry for nearly 30 years, got a huge amount of value from this book, as much of the content provided some good reminders of things that I'd since forgot, including probability and statistical analysis.

In conclusion, I can strongly recommend this book provided that one reads the title in full as a manual for budding computer scientists, as well as an introduction to the Python programming language. View 2 comments. It's a good book, a bit too academic and too abstract explaining OOP If read while following the MITx lecture it's handy but still complicated for an Introduction, because it's full of computer science jargon that doesn't explain anything to novice.

If you are not already a programmer and want to learn python, read "Python the hard way" and the educational materials from the Google dev center. May 29, Loukas Arvanitis rated it it was amazing. Highly recommended. This is obviously one of the best written technical books. The teaching style is fun and straightforward. The material is rid of any redundancy so that a quick learning of the essential parts about computation was made possible. Definitely worth reading and re-reading.

View 1 comment. Jan 17, Steven rated it really liked it. An introduction to sotware engineering and data science for beginners, who are generally assumed to be non-specialist engineering or science students with a background in algebra and the scientific method. Data science is maybe taking it too far: really it's basic computational statistical analytics. Both halves of the book use Python.

It's a college textbook, used in the MIT subject 6. This edition of the book is based on Python 2. As in many MIT presentations, there's not a lot of hand-holding -- things move fast and you may have to supplement with other material to fill in leaps in the exposition. But the overall arc of the book provides a solid course of study for those just getting started with computer programming and computational data analysis, two skills no scientist or engineer should be without.

A practical introduction to computation from MIT Press. This book walks you through the vast majority of computational techniques that you need to know as a software engineer.

It covers a wide range of traditional topics such as computational complexity, basic data structures, Object-oriented programming and dynamic programming. Although it uses Python, this is not a python book!

I skimmed through the book picking up the concepts taught from the author's point of view. The interesting part here was, the author introduces a number of scientific methods and presents a real-world use-case before demonstrating the scientific method using Python. One interesting thing I had picked up from this book was Buffon's method for calculating Pi, which was extremely simple and serves good exercise to do in any programming language.

I think this book is aimed at science students, like I skimmed through the book picking up the concepts taught from the author's point of view. I think this book is aimed at science students, like high school or bachelor who have learned science the traditional way and want to pick up computation and python. Mar 04, Neal rated it really liked it. Very detailed. Could be shorter. Jan 29, TallabAbdelhakim rated it really liked it. Worth Your Time. Only read the first half of the book that corresponds to the lectures in the MITx 6.

Might come back in the future to browse through the second half. Jan 14, Gilvane Ferret rated it really liked it. Revised and Expanded Edition.

Good college level overview. Tiesiog malonu skaityti. Sep 23, Andrew Breza rated it really liked it. I'm a data scientist with extensive experience using the R programming language. I'm adding Python to my toolkit and this is exactly the right book for learning not only Python syntax but also how to use object oriented code to solve real problems.

Feb 09, Jovany Agathe rated it it was ok. It is terse and fast. If you require a more leisurely pace, John Zelle's Python Programming, an Introduction to Computer Science takes pages to cover what this book covers in pages, and then has a short chapter just touching on what this book covers more deeply in the remaining pages. Zelle's book is also a popular first year CS text, but obviously for a different audience.

Obviously, you will learn more with this book if you can take the pace. I boug i am very pleased with the book. I bought Zelle's book also and have used it for extra exercises and the occasional alternative explanation. May 01, Carl rated it it was amazing Recommends it for: People looking to learn Computer Science.

Read this book for the Mitx course on edx. It is a very good introduction to computer science and python programming. And the e 6. I would not have liked to have taken the course without this book. The only thing about this book was it was a little bit heavy on the academic side and mathematical side.

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The 10 Best John Guttag Python Book of 2020 (Beginners Guide)

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.

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Introduction to Computation and Programming Using Python, Second Edition

Toggle navigation. Guttag 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. 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.

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Introduction to Computation and Programming Using Python

Is this becoming challenging for you to buy the best john guttag python book? Are you looking for some kind of buying help? We know how it feels having thousands of products in front of you and you having to choose one out of the many available. We have experienced this when gathering information on the top john guttag python books that are trending this year. Many questions may be arising in your mind; you may be seeing appropriate answers to them before you decide to make a purchase — we will help! Information that you read and grasp should always be authentic and trustworthy and should be picked up from a reliable, authoritative source.

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This book introduces students with little or no prior programming experience to theart of computational problem solving using Python and various Python libraries, including PyLab. Itprovides students with skills that will enable them to make productive use of computationaltechniques, including some of the tools and techniques of "data science" for usingcomputation to model and interpret data. Students are introduced to Python and the basics of programming inthe context of such computational concepts and techniques as exhaustive enumeration, bisectionsearch, and efficient approximation algorithms. The book does not require knowledge of mathematicsbeyond high school algebra, but does assume that readers are comfortable with rigorous thinking andnot intimidated by mathematical concepts. Although it covers such traditional topics ascomputational complexity and simple algorithms, the book focuses on a wide range of topics not foundin 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 dynamicprogramming. Introduction to Computation and Programming UsingPython can serve as a stepping-stone to more advanced computer science courses, or as abasic grounding in computational problem solving for students in other disciplines.

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