Preface

Applied Computational Thinking with Python provides a hands-on approach to implementation and associated methodologies that will have you up and running and productive in no time. Developers working with Python will be able to put their knowledge to work with this practical guide, using the computational thinking method for problem-solving.

In this second edition, we have added more information on debugging problems, updated and added to the chapters on machine learning, data, and artificial intelligence, and created additional problems for you to solve. Technology advances at a swift pace, and with artificial intelligence, things will continue to move at even faster rates. We hope this book will allow you to tackle problems even as technology changes.

This book will help 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 practice solving problems in multiple areas, including economics, mathematics, and artificial intelligence.

The 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.

We’ll find out how to use decomposition to solve problems through visual representation. We’ll also employ pattern generalization and abstraction to design solutions and build the analytical skills required to assess algorithmic solutions. We’ll also use computational thinking with Python for statistical analyses.

We will understand the input and output requirements in terms of designing algorithmic solutions, and use computational thinking to solve data processing problems. We’ll identify errors in logical processing to refine your solution design and apply computational thinking in various domains, such as cryptography, economics, and machine learning.

You’ll then learn about the various techniques involved in problem analysis, logical reasoning, algorithm design, classification and clusters, data analysis, and modeling, allowing you to understand how computational thinking elements can be used together with these aspects to design solutions.

We’ll also develop logical reasoning and problem-solving skills that will help you tackle complex problems. We’ll also explore core computer science concepts and important computational thinking elements, using practical examples, and discover how to identify the algorithmic solution that is best suited to your problem.

Finally, you will discover how to identify pitfalls in the solution design process, as well as 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 apply computational thinking techniques to software development.