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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Chapter 16: Advanced Applied Computational Thinking Problems

In this chapter, we will continue providing examples in multiple areas for applications of the Python programming language and computational thinking. We will be exploring multiple areas, such as geometric tessellations, creating models of housing data, creating electric fields, analyzing genetic data, analyzing stocks, creating a convolutional neural network (CNN), and more. We will use what we've learned so far in relation to computational thinking and the Python programming language to do the following:

  • Create tessellations
  • Analyze biological data
  • Analyze data for specific populations
  • Create models of housing data
  • Create electric field lines
  • Analyze generic data
  • Analyze stocks
  • Create a convolutional neural network (CNN)

After reading this chapter, you'll have learned how to perform various different analyses in working with data, creating tables and graphs that help...