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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Problem 2 – Using Python in biological data analysis

For this particular problem, we'll be using the Breast_cancer_data.csv file, which can be found on Kaggle (https://www.kaggle.com/nsaravana/breast-cancer?select=breast-cancer.csv). The file has also been uploaded to the book's GitHub repository.

When looking at data, sometimes we want to make comparisons with the data we currently have, or we want to use it for predictions in machine learning. In this case, we're going to look at how we can present another type of plot, the scatterplot, using two specific values of columns in our dataset.

Let's imagine you received this data and already determined that your mean perimeter and mean textures are better predictors than the other values in the columns. Your goal now is to create an algorithm that will analyze the values for those two columns by comparing them using a scatterplot. Our goal is only to get that scatterplot. For additional analysis and machine...