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کتاب Data Structures and Algorithms in Python

کتاب Data Structures and Algorithms in Python

Based on the authors’ market leading data structures booksin Java and C++, this book offers a comprehensive, definitiveintroduction to data structures in Python by authoritative authors.

Data Structures and Algorithms in Python is the firstauthoritative object-oriented book available for Python datastructures. Designed to provide a comprehensive introduction todata structures and algorithms, including their design, analysis,and implementation, the text will maintain the same generalstructure as Data Structures and Algorithms in Java andData Structures and Algorithms in C++.

  • Begins by discussing Python’s conceptually simple syntax,which allows for a greater focus on concepts.
  • Employs a consistent object-oriented viewpoint throughout thetext.
  • Presents each data structure using ADTs and their respectiveimplementations and introduces important design patterns as a meansto organize those implementations into classes, methods, andobjects.
  • Provides a thorough discussion on the analysis and design offundamental data structures.
  • Includes many helpful Python code examples, with source codeprovided on the website.
  • Uses illustrations to present data structures and algorithms,as well as their analysis, in a clear, visual manner.
  • Provides hundreds of exercises that promote creativity, helpreaders learn how to think like programmers, and reinforceimportant concepts.
  • Contains many Python-code and pseudo-code fragments, andhundreds of exercises, which are divided into roughly 40%reinforcement exercises, 40% creativity exercises, and 20%programming projects.

 

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Computational Thinking: First Algorithms, Then Code

Computational Thinking: First Algorithms, Then Code

This book offers a gentle motivation and introduction to computational thinking, in particular to algorithms and how they can be coded to solve significant, topical problems from domains such as finance, cryptography, Web search, and data compression.

The book is suitable for undergraduate students in computer science, engineering, and applied mathematics, university students in other fields, high-school students with an interest in STEM subjects, and professionals who want an insight into algorithmic solutions and the related mindset.

While the authors assume only basic mathematical knowledge, they uphold the scientific rigor that is indispensable for transforming general ideas into executable algorithms.

A supporting website contains examples and Python code for implementing the algorithms in the book.

 

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