Frequently Asked Questions

Start with sorting and searching algorithms - they're fundamental building blocks that appear everywhere. From there, move into basic data structures like arrays, linked lists, and hash tables. Once you're comfortable with these, tackle dynamic programming and graph algorithms. This progression gives you a solid foundation that supports more advanced topics like machine learning algorithms or distributed systems.

Extremely important, especially as you work with larger datasets. Big O notation helps you predict how your code will perform as input size grows. I've seen too many developers write algorithms that work fine during testing but crash in production because they didn't consider scalability. Understanding time and space complexity helps you make informed decisions about which approach to use in different situations.

Greedy algorithms make locally optimal choices at each step, hoping to find a global optimum. They're fast but don't always guarantee the best solution. Dynamic programming breaks problems into subproblems, stores results, and builds up to the optimal solution. It's more thorough but requires more memory and computational resources. Choose greedy for speed when approximate solutions work; use dynamic programming when you need guaranteed optimal results.

Graph algorithms are everywhere once you start looking for them. Social networks use them for friend recommendations and news feed algorithms. GPS navigation relies on shortest-path algorithms. Web crawlers use graph traversal to index pages. Even project management tools use graph algorithms to handle task dependencies. Understanding graphs opens up solutions to problems you might not initially recognize as graph-related.