An algorithm is an unambiguous, step-by-step set of instructions to solve a problem or perform a task.
It's like a recipe, but for computers. Just like a recipe guides you to make a delicious dish, an algorithm guides a computer to accomplish a specific task.
Algorithms provide a systematic way of solving problems and are the building blocks of many applications we use daily.
What is an algorithm?
Examples of everyday algorithms
Although in this course we will be focusing on Algorithms for Computers, we actually use algorithms all the time even when we aren't using computers
Recipe instructions for baking a cake
Instructions for solving a Rubik's Cube
First evacuation procedures
Can you think of any more algorithms we use on a day to day basis?
Which of the following is an example of a human algorithm in a classroom?
What is the purpose of an algorithm?
Algorithm
Thinking Algorithmically
Thinking algorithmically is a systematic and structured approach to problem-solving and decision-making. It involves breaking down complex tasks or issues into smaller, well-defined steps or procedures, much like a recipe for cooking or a set of instructions for assembling a piece of furniture.
This methodical approach enables individuals to solve problems, make decisions, and complete tasks in an organized and efficient manner.
Algorithmic thinking is rooted in the principles of computer science and mathematics, where algorithms are used to solve a wide range of problems. However, its applicability extends well beyond the realm of technology.
What does it mean to think algorithmically?
Advantages of thinking algorithmically
Efficiency
Thinking algorithmically allows for efficient problem-solving. It helps break down complex tasks into smaller, manageable steps, making it easier to find solutions and complete tasks in a systematic and time-efficient way.
Consistency
Algorithmic thinking promotes consistency in problem-solving. When you approach problems using a well-defined algorithm or methodology, you're more likely to achieve consistent and reliable results, reducing errors and variations.
Scalability
Algorithmic thinking can be scaled to handle larger or more complex problems. It provides a framework for addressing increasingly intricate challenges without a proportional increase in cognitive load or resources.
One advantage of algorithmic thinking is that it can help improve -solving skills.
Disadvantages of thinking algorithmically
Rigidity
Overreliance on algorithmic thinking can lead to rigidity. In some situations, a strict algorithmic approach may not be suitable, and more creative or adaptive thinking is required. Rigid adherence to algorithms can stifle innovation and problem-solving flexibility.
Oversimplification
Algorithms, by their nature, simplify complex problems. While this can be an advantage in many cases, it can also lead to oversimplification and missing out on nuanced or context-specific solutions. Not all problems can be neatly reduced to algorithms.
Bias
Algorithmic thinking may perpetuate biases if algorithms are based on biased data or if the algorithm's design itself introduces bias. This can result in biased decisions or solutions that fail to account for diverse perspectives and situations.
A disadvantage of algorithmic thinking is that it may oversimplify problems.
