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 the basics of Python programming. We looked at some of the built-in functions, worked with dictionaries and lists, used variables and functions, learned about files, data, and iteration, and learned about classes and OOP.

As we mentioned in this chapter and when solving previous problems, Python provides multiple ways for us to solve the same problems. One example of that is provided in the Problem 2 - Organizing information section of this chapter, where we used the eval() and sum() functions in two different algorithms to produce the same result. As we continue to learn about Python and how to write our algorithms, choosing which functions, variables, arguments, and more to use will start to become second nature. Some of the more challenging concepts and content in this chapter have to do with data analysis, such as the survey we used when introducing data in Python in the Data in Python section of this chapter, and classes. It's important...