Book Image

Applied Computational Thinking with Python

By : Sofía De Jesús, Dayrene Martinez
Book Image

Applied Computational Thinking with Python

By: Sofía De Jesús, Dayrene Martinez

Overview of this book

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.
Table of Contents (21 chapters)
Section 1: Introduction to Computational Thinking
Section 2:Applying Python and Computational Thinking
Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
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In this chapter, we discussed errors in algorithm design and how to debug solutions. We also learned how to compare solutions and refine and redesign solutions when needed. After reading this chapter, you should know more about syntax errors in algorithms and how to use the debugger using the breakpoint() command in Python 3.7 and above. The built-in debugger provides you with four courses of action: c = continue, q = quit, n = next line, and s = step.

Using the debugger allows us to identify pieces of code where we may have made mistakes. We can add this line to any place in our code to determine the issue.

We also looked at algorithms that provide the same output but using different code. By comparing algorithm solutions, we can identify which of them are more useful, what better suits our problem or situation, and why we should use one over the other. Remember that algorithms are lists of instructions. Knowing which instructions to use given the broader use of the...