I'm starting my Data Structure and Algorithm course on Udemy. It's a pretty beginner course which would, hopefully, set me up for understanding the Leetcode problems. 
Learning about the concept of Big O
<h3 id="heading-1-on-o-of-n-iteration">1. <code>O(n) - O of n. Iteration</code></h3>
<ul>
<li>Drop the constant(when you have two single for loops)</li>
</ul>
<pre><code>function logItems(n){
for (let i = 0; i&lt; n; i ++){
console.log(i)
for (let j = 0; j&lt; n; j ++){
console.log(j)
</code></pre><h3 id="heading-2-on2-o-of-n-squared-aka-loop-within-a-loop">2. <code>O(n^2) - O of n squared. aka loop within a loop</code></h3>
<ul>
<li>Drop Non-Dominants = when you have O(n) and O(n^2) together, you drop O(n).</li>
</ul>
<h3 id="heading-3-o1-o-of-1-aka-constant-time">3. <code>O(1) - O of 1 aka constant time.</code></h3>
<ul>
<li>The most efficient Big O.</li>
</ul>
<h3 id="heading-4-olog-n-o-of-log-n-aka-divide-and-conquer">4. `O(log n) - O of log n. aka Divide and Conquer</h3>
<img src="https://cdn.hashnode.com/res/hashnode/image/upload/v1659660331288/o9efkjKH-.PNG" alt="BigO.PNG" class="image--center mx-auto" />
#conclusion
Big O is one of the fundamental tools for software engineers to analyze the cost of an algorithm. You can find more info on Big O by following this Link(<a href="https://www.bigocheatsheet.com/" class="autolinkedURL autolinkedURL-url" target="_blank">bigocheatsheet.com</a>)

I'm starting my Data Structure and Algorithm course on Udemy. It's a pretty beginner course which would, hopefully, set me up for understanding the Leetcode problems. 
Learning about the concept of *Big O*

### 1. ` O(n) - O of n. Iteration`
- Drop the constant(when you have two single for loops)

```
function logItems(n){
for (let i = 0; i< n; i ++){
console.log(i)
for (let j = 0; j< n; j ++){
console.log(j)
```

### 2. `O(n^2) - O of n squared. aka loop within a loop`
- Drop Non-Dominants = when you have O(n) and O(n^2) together, you drop O(n).

### 3. `O(1) - O of 1 aka constant time.` 
- The most efficient Big O.

### 4. `O(log n) - O of log n. aka Divide and Conquer 



![BigO.PNG](https://cdn.hashnode.com/res/hashnode/image/upload/v1659660331288/o9efkjKH-.PNG align="center")




#conclusion

Big O is one of the fundamental tools for software engineers to analyze the cost of an algorithm. You can find more info on Big O by following this Link(https://www.bigocheatsheet.com/)

What the heck is Big O!?

Learning about Big O

1. O(n) - O of n. Iteration

2. O(n^2) - O of n squared. aka loop within a loop

3. O(1) - O of 1 aka constant time.

4. `O(log n) - O of log n. aka Divide and Conquer

Worked as a finance guy(loan manager) for 6 years only to decide to become a software engineer after discovering a passion that I've never found as a banker.


HODL BAEK

HODL BAEK

Learning about Big O

What the heck is Big O!?

Table of contents

1. `O(n) - O of n. Iteration`

2. `O(n^2) - O of n squared. aka loop within a loop`

3. `O(1) - O of 1 aka constant time.`

4. `O(log n) - O of log n. aka Divide and Conquer