<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Computer Science on Pocket Dev</title><link>/tags/computer-science/</link><description>Recent content in Computer Science on Pocket Dev</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 05 Feb 2025 13:56:00 +0000</lastBuildDate><atom:link href="/tags/computer-science/index.xml" rel="self" type="application/rss+xml"/><item><title>Big O Notation Explained: A Simple Guide to Algorithm Complexity</title><link>/posts/big-o-notation-explained-algorithm-complexity/</link><pubDate>Wed, 05 Feb 2025 13:56:00 +0000</pubDate><guid>/posts/big-o-notation-explained-algorithm-complexity/</guid><description>&lt;h1 id="understanding-big-o-notation-a-simple-comparison"&gt;&lt;strong&gt;Understanding Big O Notation: A Simple Comparison&lt;/strong&gt;&lt;/h1&gt;
&lt;p&gt;This post is part of my &lt;strong&gt;data structures and algorithms refresher series&lt;/strong&gt;, where I revisit essential concepts that every developer should understand. One of the most important is &lt;strong&gt;Big O notation&lt;/strong&gt;—a way to measure how an algorithm&amp;rsquo;s efficiency scales as input size increases.&lt;/p&gt;</description></item></channel></rss>