Solving FizzBuzz with JavaScript and Understanding Big O Notation

Hey LinkedIn community,

Today, I want to take a moment to discuss a classic programming problem that often comes up in interviews and serves as a great introduction to algorithm complexity – FizzBuzz. We'll dive into solving this problem using JavaScript and explore its time complexity using Big O notation.

The FizzBuzz Problem:

FizzBuzz is a simple task: Write a program that prints numbers from 1 to 100. But for multiples of 3, print "Fizz" instead of the number, and for multiples of 5, print "Buzz." For numbers that are multiples of both 3 and 5, print "FizzBuzz."

Solving FizzBuzz in JavaScript:

We can solve FizzBuzz in JavaScript using a loop that iterates from 1 to 100 and checking each number to determine whether it's a multiple of 3, 5, or both. Here's a JavaScript solution to this problem:

for (let i = 1; i <= 100; i++) {
  if (i % 3 === 0 && i % 5 === 0) {
    console.log("FizzBuzz");
  } else if (i % 3 === 0) {
    console.log("Fizz");
  } else if (i % 5 === 0) {
    console.log("Buzz");
  } else {
    console.log(i);
  }
}        

This code is straightforward and solves the FizzBuzz problem efficiently for a small range like 1 to 100. But how do we analyze its time complexity using Big O notation?

Big O Notation: Understanding Efficiency:

Big O notation is a mathematical notation used to describe the upper bound of an algorithm's time complexity. In our FizzBuzz solution, we have a loop that iterates from 1 to 100. The time complexity of this code is O(n), where 'n' is the range of numbers (100 in this case).

O(n) means that the algorithm's execution time grows linearly with the input size. In this case, the input size is the range of numbers, and as it increases, the time taken by the algorithm increases linearly.

Conclusion:

Solving the FizzBuzz problem is a fundamental exercise in programming. It allows you to practice conditional statements, loops, and basic algorithmic thinking. Additionally, understanding Big O notation is crucial for assessing the efficiency of your code as it scales to handle larger inputs.

I hope this blog post helps you both solve FizzBuzz in JavaScript and understand the concept of time complexity using Big O notation. Remember, these are foundational skills that can serve you well in interviews and real-world programming scenarios.

Happy coding, and if you have any questions or want to discuss any other topics, feel free to reach out!

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