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Applied Computational Thinking with Python

Applied Computational Thinking with Python

By : Sofía De Jesús, Dayrene Martinez
4.6 (11)
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Applied Computational Thinking with Python

Applied Computational Thinking with Python

4.6 (11)
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)
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1
Section 1: Introduction to Computational Thinking
9
Section 2:Applying Python and Computational Thinking
14
Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
20
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Introducing Python

Python is one of the fastest-growing programming languages due to its ease of use. One of the draws of Python is that we can write the same programs we do with languages such as C, C++, and Java, but with fewer lines of code and simpler language and syntax. Another big draw of Python is that it is extensible, which means we can add capabilities and functionalities to it.

While not all functionalities are inherently built in, we can use libraries to add what we need. Those libraries are available to download and use. For example, if we wanted to work with data and data science, there are a few libraries we can download, such as Pandas, NumPy, Matplotlib, SciPy, Scikit Learn, and more. But before we get into those libraries, let's look at how the Python language works and learn its basics.

Python has some built-in reference functions. The following table shows the functions in alphabetical order:

Table 8.1 – Python built-in...

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Applied Computational Thinking with Python
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