Use the computational thinking philosophy to solve complex problems by designing appropriate algorithms to produce optimal results across various domains
Key Features
- Develop logical reasoning and problem-solving skills that will help you tackle complex problems
- Explore core computer science concepts and important computational thinking elements using practical examples
- Find out how to identify the best-suited algorithmic solution for your problem
Book Description
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.
What you will learn
- Find out how to use decomposition to solve problems through visual representation
- Employ pattern generalization and abstraction to design solutions
- Build analytical skills to assess algorithmic solutions
- Use computational thinking with Python for statistical analysis
- Understand the input and output needs for designing algorithmic solutions
- Use computational thinking to solve data processing problems
- Identify errors in logical processing to refine your solution design
- Apply computational thinking in domains, such as cryptography, and machine learning
Who this book is for
This book is for students, developers, and professionals looking to develop problem-solving skills and tactics involved in writing or debugging software programs and applications. Familiarity with Python programming is required.
Table of Contents
- Fundamentals of Computer Science
- Elements of Computational Thinking
- Understanding Algorithms and Algorithmic Thinking
- Understanding Logical Reasoning
- Errors
- Exploring Problem Analysis
- Designing Solutions and Solution Processes
- Identifying Challenges within Solutions
- Introduction to Python
- Understanding Input and Output to Design a Solution Algorithm
- Control Flow
- Using Computational Thinkning and Python in Simples Challenges
- Debugging
- Using Python in Experimental and Data Analysis
- Using Classification and Clusters Introduction to Machine Learning
- Using Computational Thinking and Pythin in Statistical Analysis
- Applied Computational Thinking Problems
- Advanced Applied Computational Thinking Problems
- Usage of Cloud Platforms
Sofía De Jesús is a computational thinking teacher with a degree from the University of Puerto Rico with a focus on math. She has a master's degree from the University of Dayton. Her experience in education and development spans two decades. She has worked with clients to develop solutions in multiple programming languages. As a teacher, Sofía helps students incorporate the philosophy of computational thinking in courses like game design, circuits, Python, web design, and robotics. She likes to play video games and spend time with her 11 year old Yorkie, King Kong. Sofía also enjoys creating materials, small furniture, and jewelry using CNC machines and laser cutters. She enjoys spending as much time in Puerto Rico as work and life permits.
Dayrene Martinez is a data engineer specializing in AI at one of the Big Four consulting firms. She holds a bachelor's degree in electrical engineering. Her expertise includes optimizing neural network models, ETL and AWS cloud computing. Previously, she was a systems engineer in the defense industry, developing neural networks for aerospace vehicle decision-making. Dayrene is a dedicated and passionate engineer who serves as a keynote speaker and mentor inspiring high school students, college students, and career changers in engineering and tech by sharing her experiences and insights, making a positive impact on the next generation of talent in the field.
