Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.
This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.
By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.

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Spis treści

Applied Computational Thinking with Python. Algorithm design for complex real-world problems - Second Edition eBook -- spis treści

• 1. Fundamentals of Computer Science
• 2. Elements of Computational Thinking
• 3. Understanding Algorithms and Algorithmic Thinking
• 4. Understanding Logical Reasoning
• 5. Errors
• 6. Exploring Problem Analysis
• 7. Designing Solutions and Solution Processes
• 8. Identifying Challenges within Solutions
• 9. Introduction to Python
• 10. Understanding Input and Output to Design a Solution Algorithm
• 11. Control Flow
• 12. Using Computational Thinkning and Python in Simples Challenges
• 13. Debugging
• 14. Using Python in Experimental and Data Analysis
• 15. Using Classification and Clusters Introduction to Machine Learning
• 16. Using Computational Thinking and Pythin in Statistical Analysis
• 17. Applied Computational Thinking Problems
• 18. Advanced Applied Computational Thinking Problems
• 19. Usage of Cloud Platforms