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This is not a book for the ones who want to learn how to write lines of code or learn python's syntax, this is rigorous material therefore it's highly likely that the reader spends considerable mental energy and some time through this book, however, the reader gathers all that is needed to understand computational systems and write programs as a computational scientist would do.

David

For people who want to learn how to think and solve problems

Avaliado nos Estados Unidos em 11 de agosto de 2022

This book goes very well with the free MITx courses available online. The book covers the same material as the online lectures. So you could do the online lectures without the book. However, the book does go into a little more detail and is a more convenient medium for reviewing concepts than re-watching a lot of video. The focus is not on learning syntax. The focus is on learning how to break down problems and create solutions. So it is ok for people who want to start learning Python. However, it is even better for people who want to learn how to become good problem solvers. You won't master Python by reading this book but it is a fantastic place to start learning some fundamental concepts of computer science

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Nicholas Oatridge

Definitive text for learning computer science through Python

Avaliado no Reino Unido em 7 de janeiro de 2021

When the socialite Kylie Jenner asked on Twitter "Can you guys please recommend books that made you cry?", the response from one follower was "Data Structures and Algorithms in Java (2nd Edition)".

It is true books about coding are rarely easy going. However there are some that, through the clarity of thought and precision of expression, are satisfying to struggle with. "The C Programming Language (2nd Edition)" by Kernighan and Ritchie from 1988 springs to mind, the book that brought us "Hello, World!".

For that most accessible of computer languages, Python, there is a wealth of excellent books published to introduce the language. However in its 3rd Edition, "Introduction to Computation and Programmimg Using Python" by John Vogel Guttag takes some beating.

The book was initially developed from material used on a single semester course at MIT, using Python to introduce Computer Science. It has since been expanded considerably. Although it works well as a text for formal education, it can also be used alongside MIT's hugely successful and highly recommended (free) MOOCs, 6.00.1x and 6.00.2x or as a primer for somebody wanting to learn or improve their Python with a view to using it in a scientific or social science setting. In particular this is an excellent primer for those wanting to work in the field of data science or machine learning, especially if their formal exposure to algorithms, probability and statistical inference is limited. The latest version includes a chapter on the pandas library, supplementing material in the previous edition that touched on numpy and scipy, and it covers plotting (using matplotlib) more extensively than in the 2nd Edition.

This is not a dry tome. Throughout the book, Guttag's sense of humour and erudition shines through. His asides cover everything from Babbage to baseball, from Ptolemy to Turing. Each chapter summarises the terms introduced in the chapter and there is an excellent Python 3.8 quick reference guide at the end of the book. As would be expected, the book is copiously indexed and cross-referenced, accompanying code is available to download and most of the material can be supplemented with videos available on YouTube.

The book covers subjects such as object-oriented programming, dynamic programming and algorithmic complexity and introduces some of the most important algorithms in the field of computer science. The book falls short of discussing other important machine learning libraries, such as sklearn or tensorflow, does not address Python's support for functional programming and does not cover important commercial tools such as database management systems or graphical user interfaces. However Guttag covers a lot of well-paced ground in the book's 637 pages, by the end of which you will have become competent in using Python to perform systematic problem solving, data analysis and computational modelling to address real world challenges.

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Bernardo y Estefania

Sobre la calidad del envío y material, no el contenido

Avaliado no México em 24 de março de 2024

Esta opinión no es sobre el contenido del libro, que es muy bueno, sino la calidad del material usado en el libro y el envío.

El libro es de pasta blanda. Cuando llegó, llegó muy maltratado. Este es problema de Amazon seguramente, y no del libro. Don embargo, dado el costo del libro, esperaba una mucho mejor calidad. Como se ve en las imágenes, la calidad es bastante mala.

TSR

Great book but

Avaliado nos Estados Unidos em 20 de abril de 2021

It's an excellent read and teaches you almost everything you need to know about computation by way of algorithms, the nature of efficiency of their usage. It's a computer science baptism in fire and water. My only gripe with this book is that it's pretty thin on exercises if that's what you are after. I found Sedgewick's original book Programming in Python - An intermediate approach, to be a very solid resource with a chockful of exercises BUT that book is version outdated (Python 2.x) and their new book uses Java. But I wouldn't be without this book if Python is your thing. Also, Wes McKinney's book is a great one to have for all the tricks you need.

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