Computer Science
Algorithms
Computational Thinking
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Computational Thinking
- "Computational Thinking" is a key aspect of computer science and deals with solving problems, designing systems, and understanding human behaviour.
- It comprises of four main techniques: decomposition, pattern recognition, abstraction, and algorithms.
- Decomposition involves breaking down a complex problem or system into smaller, more manageable parts.
- Pattern Recognition is the identification of similarities or patterns that can be used to solve problems more easily.
- Abstraction focuses on the important information only, ignoring irrelevant details to simplify the problem and solution.
- Algorithms are step-by-step instructions created to solve problems and accomplish a task.
- All four techniques are interchangeable and often used together to solve complex problems.
- Understanding these techniques helps in designing effective algorithms.
- These techniques have applications not only in computer science but also in other fields such as biology, business and physics.
- To be competent in 'computational thinking', the ability to analyse a problem, recognise patterns, abstract from the details and write an algorithm should be possible.
- The quality of an algorithm can be measured by its efficiency (how quickly it solves the problem) and correctness (the accuracy of its solutions).
- Valuing Computational Thinking in computer science means placing importance on problem solving rather than learning specific software tools or programming languages.
- Challenges in Computational Thinking encourages thinking about the different ways that problems can be solved and help improve the development of algorithms.
- Larger problems often require algorithms to work together. This can involve using decomposed parts of the problem, recognising patterns between them, and using these to create a solution.
