Funso Oyedele

I've recently decided to go back to the basics and strengthen my understanding of Computer Science fundamentals. Over the next few weeks, I'll be sharing what I learn about Data Structures & Algorithms (DSA), from Arrays and Linked Lists to Trees, Graphs, and beyond.

4. O(n log n) — Linearithmic Time A little more work than O(n), but much better than O(n²). Most efficient sorting algorithms live here. Examples: • Merge Sort • Quick Sort (average case) This is why large applications can sort millions of records quickly.

3. O(n) — Linear Time You check items one by one. Example: Looking for "John" in your contacts. Worst case? You scan every contact. 10 contacts → 10 checks 1,000 contacts → 1,000 checks for (const item of arr) {}

This is why Binary Search is so fast. Even with 1,000,000 items, you only need about 20 checks.

2. O(log n) — Logarithmic Time You keep cutting the problem in half. Imagine guessing a number between 1 and 1,000. You don't start from 1. You ask: "Is it greater than 500?" Then 750. Then 625. Each guess removes half the possibilities.

Example: You have a book with 10 pages or 10 million pages. Someone asks: "What's on page 1?" You open page 1 immediately. 1 step. Always. arr[0]

Common Types of Big O (From Fastest to Slowest) Understanding these will help you quickly identify whether your code will scale well or become a bottleneck as data grows. 1. O(1) — Constant Time No matter how much data you have, the work stays the same.

@euniceajim Bravo👏🏿

I'm 30. I'm black. I'm African. I'm a woman. I manage an early-stage VC fund focused on Africa. This is my experience.

@x_jinal Just me😂

GitHub thinks I'm a senior engineer. I think I'm lucky.

@vivoplt I missed copying codes from Stack overflow🥺

There was a time when we used to copy paste code from a website.

It asks: ✅ "What happens when we go from 10 users to 10 million users?" That's why companies like Google, Netflix, and Amazon care so much about it. Big O is basically a future-proofing score for your code.

Big O ignores the small details (3 and 5) and focuses on what matters most as the data grows: O(n) Because when the list gets huge, the n is what makes the biggest difference. Think of it this way Big O doesn't ask: ❌ "How fast is it on my laptop?"

🔹Big O Notation Big O is simply a way of measuring how much harder a program has to work as the amount of data increases. Imagine you have: i. 10 names to search through ii. 1,000 names iii. 1,000,000 names Big O helps us answer: "Will this program still be fast when the data

@EOEboh Good then It seems the test wasn't that complex for such a short time to be given. Kudos!

@funso_dev I was given 26 mins.. enough time to reason for me

Just did an interview round where I was given three different code blocks to review. Review it, find the hidden bugs and suggest a solution or fix. Tech interviews are slowly changing I guess

@Priyansh_31Dec Talking to people is one of the most underrated ways to learn. You'd be surprised how much you can learn from a simple conversation with the right person.

Just talk to people, doesn’t matter who they are, what they do, whether they’re doing better than you or worse, where they live, what’s their background, etc, etc.

@darasoba Not sure I'd call it wasted time. Different people have different learning paths. AI is a great accelerator, but having a solid grasp of the fundamentals is never a bad investment.

I just saw someone learning HTML/CSS the old W3schools way, and I don’t know how to tell him he’s wasting precious time. Send help!

@TTrimoreau Notion

Name a startup that became successful without spending millions on marketing 👇

Understanding arrays is the foundation for mastering more advanced data structures like Linked Lists, Stacks, Queues, Trees, and Graphs.

Why? Because elements after the deleted item must be moved. ⏱ Time Complexity: O(n) Quick Recap ✅ Read → O(1) ✅ Search → O(n) ✅ Insert → O(n) ✅ Delete → O(n)

Array Deletion Suppose we have: [10, 20, 30, 40, 50] Delete the element at index 2 (30). Before: Index: 0 1 2 3 4 Value:10 20 30 40 50 After: Index: 0 1 2 3 Value:10 20 40 50 Notice how 40 and 50 shifted left to fill the gap.