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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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Learning to decompose problems

When we decompose problems, we're identifying what we need the algorithm to provide us with. The end user will need to see something seamless. Look at the flowchart in Figure 5.1; this is a basic decision-making flowchart to help us design our algorithm.

Let's make another assumption first, that is, if the user enters more than 10, the price will be lower. We're only going to do less than 10 or more than or equal to 10 in this case. However, if you needed to subdivide this further, you can add more cases, such as the following:

  • Less than or equal to 10
  • More than 10 and less than or equal to 50
  • More than or equal to 50

You can have as many cases as you need. For the purposes of this algorithm, we're going to keep it to two cases, since we also have to include personalization costs and we don't want to create an overly complicated algorithm.

The following diagram shows you the flowchart for the algorithm...

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Applied Computational Thinking with Python
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