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Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. This all-new Data Structures and Algorithms in Python is designed to provide an introduction to data structures and algorithms, including their design, analysis, and implementation. The authors take advantage of the beauty and simplicity of Python to present executable source code that is clear and concise. Furthermore, a consistent object-oriented viewpoint is retained throughout the book, including the use of inheritance, both to maximize code reuse and to draw attention to the clear similarities and differences of various abstract data types and algorithmic approaches. The cover art is based on an indigenous Australian painting style portraying what is known as Dreamtime.
In data science, algorithms and data structures make up the function and storage of data collecting. While coding and applied mathematical knowledge are helpful when learning these structures, there are actually plenty of books for complete beginners. Many of the books focus on a specific structure intended to facilitate learning, using working examples and code to support the theory behind the subject. This article will discuss a range of algorithm and structures books that can satisfy skill levels from beginner to intermediate, to advanced users. Authors: Thomas H.
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It seems that you're in Germany. We have a dedicated site for Germany. Authors: Lee , Kent D. This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use.
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This clearly structured and easy to read textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by motivating examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. The text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python. Students of computer science will find this clear and concise textbook to be invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels.