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)
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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Analyzing algorithms

As mentioned previously in this chapter, when we design algorithms, they should meet the following characteristics:

  • They are clear and unambiguous.
  • They have inputs that are well defined.
  • They have outputs that are well defined.
  • They have finiteness.
  • They are feasible.
  • They are language-independent.

In addition to those characteristics, when we are looking at algorithms and analyzing them, we want to make sure we ask ourselves some questions:

  • Does the algorithm do what we want?
  • Does the output make sense?
  • Is there another way to get the same information in a clearer way?

There are many more questions we can ask ourselves when analyzing algorithms, but for now, let's take a look at some algorithmic solutions and analyze them based on the aforementioned characteristics and questions.

Algorithm analysis 1 – States and capitals

A student has created an algorithm that includes a list of US states...