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 were able to explore more topics in computational thinking, especially in dealing with data and deep learning, using the Python programming language. We learned how to create pairplots in order to determine the relationship between variables in a dataset. We also learned how to produce various types of plots to visually represent our datasets. We also learned how to create electric field lines using Python. In short, we applied what we'd learned throughout the previous chapters and extended our knowledge while working in applied problems.

And that's really what this book sought to do: Show a wide variety of Python applications while looking at real problems in context. Did we cover everything Python can do? That's fairly impossible, as Python capabilities continue to grow because of its ease of use, how easy it is to learn, and how many applications continue to be added because of its open source nature. Hopefully, you got to work with...