Introduction to Computational Thinking

Problem Solving, Algorithms, Data Structures, and More
1. Auflage 2021
ISBN: 978-1-4842-7076-9
Verlag: Apress

Buch, Englisch, 657 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 1243 g

Learn approaches of computational thinking and the art of designing algorithms. Most of the algorithms you will see in this book are used in almost all software that runs on your computer.

Learning how to program can be very rewarding. It is a special feeling to seeing a computer translate your thoughts into actions and see it solve your problems for you. To get to that point, however, you must learn to think about computations in a new way—you must learn computational thinking.
This book begins by discussing models of the world and how to formalize problems. This leads onto a definition of computational thinking and putting computational thinking in a broader context. The practical coding in the book is carried out in Python; you’ll get an introduction to Python programming, including how to set up your development environment.

What You Will Learn
- Think in a computational way
- Acquire general techniques for problem solving
- Seegeneral and concrete algorithmic techniques
- Program solutions that are both computationally efficient and maintainable

Who This Book Is For

Those new to programming and computer science who are interested in learning how to program algorithms and working with other computational aspects of programming.

Autoren/Hrsg.

Mailund, Thomas

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmierung: Methoden und Allgemeines
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Inhaltsverzeichnis

1: Introduction.- 2: Introducing Python Programming.- 3: Introduction to Algorithms.- 4: Algorithmic Efficiency.- 5: Searching and Sorting.- 6: Functions.- 7: Inner Functions.- 8: Recursion.- 9: Divide and Conquer and Dynamic Programming.- 10: Hidden Markov Models.- 11: Data Structures, Objects and Classes.- 12: Class Hierarchies and Inheritance.- 13: Sequences.- 14: Sets.- 15: Red-black Search Trees.- 16: Stacks and Queues.- 17: Priority Queues.- 18: Conclusions.

Über Autor(innen)

Thomas Mailund, PhD is an associate professor in bioinformatics at Aarhus University, Denmark. He has a background in math and computer science, including experience programming and teaching in the C, Python and R programming languages. For the last decade, his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.