My First Post

Code Challenges, and Optimizations: Part 1

At some point in the job search, we encounter technical interviews that aim to assess our level of understanding and/or depth of knowledge. One of the most common, or the most commonly practiced aspects of a technical interview is the code challenge.

Code challenges are often a simple one on one interaction with an interviewer and a series of problems/scenarios that you are required to solve. There are various resources for the structure of these challenges, and libraries of problem sets to practice with:

List of Resources:

A common strategy after reviewing and confirming the parameters of the problem is to pseudo-code a solution, and use this time to ask any questions or clarifications to discover edge cases. Once a path has been decided, your solution will benefit greatly from the framework provided in your pseudo-code/brainstorming session.

A secondary part of the code challenge is the optimization portion, where your interviewer may ask you to review your solution, and hink about ways to decrease time and space complexity. (Big O Notation) In Computer Science, Big O Notation refers to how quickly our functions run relative to our input, as input increases. In other words, Big O Notation helps us to classify how efficent our code is when faced with arbitrarily large inputs.

There are run-times that are constant, the “steps” required in our code is unchanging relative to input.

O(1) Time:

function printFirst(arr) {
    console.log(arr[0])
}

There are run-times that are linear, as in our code iterates linearly.

O(n) Time:

function printAll(items) {
    for (let item in items) {
        console.log(items)
    }
}

There are run-times referred to as quadratic time, where the run time is equivalent to the squared input. We’re nesting two loops, our inner loop runs n times each iteration of the outer loop (n x n or n²).

O(n²) Time:

function printPairs(items) {
    for (let item in items) {
        for (let secondItem in items) {
            console.log(item, secondItem)
        }
    }
}

Whether your solution is the most optimal or a brute force solution, be prepared to discuss how/why that is the case in terms of time complexity. Next week, we’ll discuss a quick problem with brute force solutions, and an optimized solution.

Code Challenges, and Optimizations: Part 1

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