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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Defining experimental data

We're finally at the data chapter of this book. We all have our biases and areas where we just love to live in. This is one of mine. There are many reasons why data is so important, but let's start with the fact that data, how we collect it, how we analyze it, and how we present it has a massive impact on our daily lives.

When writing algorithms to display information, we have a responsibility to share that data in the least biased way possible, making sure that our data is inclusive and representative of our communities and our people. I wanted to make sure I said that before we talk about the topic in as much depth as a chapter will allow me to. For me, this is how I fell in love with code and Python.

In this section, we're going to go over experimental data, defining what it is as well as key terms used when working with experimental data.

Now, let's get started. Experimental data is a term that gets its use from science and...