Introduction

• Computational thinking involves strategies to solve complex problems and can be applied to both humans and computers.
• The four components of computational thinking are decomposition, pattern recognition, algorithms, and abstraction.
• Computational thinking is essential for problem-solving in programming and other fields.
• Ethical considerations and human oversight are crucial in automation to avoid biases and ensure transparency.

Exercise

Computational thinking in practice

• Computational thinking involves breaking down problems, recognizing patterns, and developing algorithms.
• While programming is instructing a computer to carry out tasks, computational thinking helps decide what those tasks will be.
• Computational thinking is used in various fields, from project management to epidemiology, and even in daily tasks.
• Structure diagrams are useful tools for visually breaking down and planning problem-solving steps.

Computational thinking in programming

• Decomposition is essential for breaking down problems into discrete parts in programming.
• Computers require precise instructions, and problems must be broken down accordingly.
• Linear code runs commands in a sequence, while branching code allows for different pathways based on conditions.
• Pattern recognition helps programmers adapt existing code for new problems.

Pseudocode

• Pseudocode is a valuable tool for organizing and planning coding solutions before actual programming.
• It helps to list each step of a process logically, making it easier to translate into code.
• Loops allow the execution of repetitive tasks until certain conditions are met.
• Variables in loops can change with each iteration, demonstrating the concept of abstraction.

Potential solutions to practice exercises