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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Chapter 12: Using Python in Experimental and Data Analysis Problems

In this chapter, we will take a look at how Python can help us understand and analyze data using algorithms and libraries created specifically for data analysis and data science. We will first go through experimental data and then move on to algorithms that use two main libraries: NumPy and pandas.

In this chapter, we will cover the following topics:

  • Defining experimental data
  • Using data libraries in Python
  • Understanding data analysis with Python
  • Using additional libraries for plotting and analysis

By the end of this chapter, you will be able to define types of experiments, data gathering, and how computational thinking helps when designing models and solutions. You will also learn how to use data libraries, particularly, NumPy, pandas, and Matplotlib, to help in analyzing and displaying data. Finally, you'll be able to design algorithms that help with data analysis in order to learn...