PDF Python for Software Design: How to Think Like a Computer Scientist

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Contents:
  1. Allen B. Downey
  2. Think Python: How to Think Like a Computer Scientist
  3. Kiitos että kirjoitit arvostelun {{ 'vertaa' | capitalize }}.{{ 'fi' }}
  4. Find out what else you can do with Python
  5. Intermediate Programming in Python - Online Course

Return to Book Page. Downey Goodreads Author.

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The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science.

Like mathematicians, computer scientists use formal languages to denote ideas specifically computations. Like engineers, they design things, assembling components into systems and evaluating tr The goal of this book is to teach you to think like a computer scientist. Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives.

How To Think Like A Programmer

Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions. The single most important skill for a computer scientist is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. On another level, you will use programming as a means to an end. As we go along, that end will become clearer.

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Allen B. Downey

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Think Python: How to Think Like a Computer Scientist

All Languages. This item has been added to your basket View basket Checkout. View other formats and editions. A no-nonsense introduction to software design using the Python programming language. Written for people with no programming experience, this book starts with the most basic concepts and gradually adds new material.


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Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. The focus is on the programming process, with special emphasis on debugging.

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The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practise each new concept. Exercise solutions and code examples are available from thinkpython. The presentation is neat and clean, I might even say cheerful. And I learned a lot, not least of all where higher level languages are going, and the terminology used to express that I liked the pace of presentation.

Find out what else you can do with Python

I liked the constant stirring of topics: a new feature, a hint on debugging, a few words on programming style, some thoughts on programming principles, then on to the next new feature. It really is a nice mix. I hope that instructors in computational science will learn some pedagogical lessons from it. Repeatedly, the book showed code that was simply readable. The feature, its rationale, its uses, and debugging hints are together for collective reference like an object? And ideas are repeated as they naturally reappear.

Is that how computer scientists think? I don't know. But if that's how they teach, they're doing a fine job. When trying to teach the more difficult ideas of floating point errors, control of step size, mesh refinement, and parallel programming, computational scientists could learn something from Python for Software Design. Dan Nagle, Scientific Programming " The author provides details of Python in a cogent fashion, enabling a novice in programming to cover the material with relative ease.

Downey succeeds in fulfilling his four goals, stated in the preface.

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Intermediate Programming in Python - Online Course

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