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
Other Books You May Enjoy

Problem 1 – Using Python to analyze historical speeches

History is quite fascinating, and there are many reasons why we would look at writing algorithms to evaluate historical data and contexts.

For this problem, we want to analyze some historical text. In particular, we are going to take a look at Abraham Lincoln's second inaugural speech. Our goal is to find some frequencies of words. There are many reasons why we'd want to perform some straightforward text analysis, especially for historical texts. We may want to compare them, understand underlying themes, and so on.

For our algorithm, we are going to use a fairly simple design using the nltk package. Because the installation of some of the components is a bit different to what we've done so far, we'll provide some information in case your packages have not been installed.

In the Python shell, if you are in the active console, create a new file and import nltk after installing the main package...