What is algorithms?

An algorithm is a step‑by‑step set of instructions that tells a computer (or a person) how to solve a problem or complete a task. Think of it like a recipe: it lists the exact actions to take, in order, to get from the ingredients (input) to the finished dish (output).

Let's break it down

  • Input: The data you start with (e.g., a list of numbers).
  • Steps: The individual actions the algorithm performs (e.g., compare two numbers, swap them).
  • Output: The result after all steps are finished (e.g., the list sorted from smallest to largest).
  • Termination: The algorithm must finish after a finite number of steps; it can’t run forever.
  • Correctness: When it stops, the output should be the right answer for the given input.

Why does it matter?

Algorithms are the engine behind every software feature you use. They determine how fast a program runs, how much memory it needs, and whether it can handle large amounts of data. Good algorithms make apps responsive, secure, and scalable, while poor ones can cause lag, crashes, or excessive costs.

Where is it used?

  • Search engines (finding the best results among billions of pages).
  • Social media feeds (deciding which posts to show you).
  • Navigation apps (calculating the fastest route).
  • E‑commerce (recommending products you might like).
  • Healthcare (analyzing medical images or predicting disease risk).
  • Everyday apps (sorting contacts, encrypting messages, compressing files).

Good things about it

  • Efficiency: Well‑designed algorithms can solve big problems quickly.
  • Predictability: They give consistent results for the same input.
  • Reusability: The same algorithm can be used in many different programs.
  • Scalability: Good algorithms handle growth in data size without breaking.
  • Foundation for innovation: New technologies often start with a clever algorithm.

Not-so-good things

  • Complexity: Some algorithms are hard to understand and debug.
  • Resource heavy: Poorly chosen algorithms can waste CPU time or memory.
  • Bias risk: Algorithms that process human data can unintentionally reflect societal biases.
  • Over‑optimization: Focusing too much on speed can make code harder to maintain.
  • Security concerns: Flaws in algorithms (e.g., weak encryption) can expose data.