Computational Thinking : First Algorithms, Then Code / by Paolo Ferragina, Fabrizio Luccio.
Material type:
TextPublisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st edDescription: VIII, 174 p. Includes IndexISBN: 9783319979403Subject(s): Algorithms | Computer Thinking | Software engineering | Algorithm Analysis and Problem Complexity | Software Engineering/Programming and Operating SystemsDDC classification: 004 Q80 Summary: 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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ASECL | Computer Science | 004 Q80 (Browse shelf(Opens below)) | Available | 4256 |
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| 004 Q70 Computational Thinking : A Beginner's Guide to Problem-Solving and Programming / | 004 Q70 Computational Thinking : A Beginner's Guide to Problem-Solving and Programming / | 004 Q80 Computational Thinking : First Algorithms, Then Code / | 004 Q80 Computational Thinking : First Algorithms, Then Code / | 004.421.2: 519.2 Q20 Statistical Algorithms / | 004.421.2: 519.2 Q20 Statistical Algorithms / | 004.438 PYT Q30 Python : The Complete Reference / |
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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