To make your code better using Big O notation, it's important to understand how fast it runs and how much space it uses. Here are some simple strategies to help you:

1. Check How Efficient Your Algorithms Are

• Know the Complexity: Find out the worst-case, average-case, and best-case scenarios for your algorithms. For example, a simple search through a list has a complexity of $O(n)$, while a faster method called binary search has a complexity of $O(\log n)$.
• Pick Faster Algorithms: Choose algorithms that work more efficiently. For instance, using Merge Sort ($O(n \log n)$) is much quicker than Bubble Sort ($O(n^2)$) when dealing with large amounts of data.

2. Use Data Structures Wisely

• Select the Right Data Structure: The kind of data structure you use can change how quickly you can perform different tasks. For instance, hash tables can look up information in an average time of $O(1)$, but lists usually take $O(n)$ time.
• Dynamic vs Static: Use dynamic data structures, like linked lists, when you need to change things often. If you are reading more and changing less, go with static structures, like arrays.

3. Improve Your Loops and Recursive Functions

• Reduce Nested Loops: Try to limit the number of loops, especially if they slow things down with $O(n^2)$ complexity. Instead, see if you can use one loop with the right conditions.
• Use Tail Recursion: If possible, turn recursive functions into tail-recursive versions. This can help your code run better.

4. Profile Your Code

• Use Profiling Tools: Gather information about how long your code takes to run and how much memory it uses. This will help you find areas where your code might be slow. Check how performance changes as you increase the input size to see the effects of your complexity findings.

By following these tips, you can make sure your code runs smoothly and handles larger inputs better, which leads to better performance in real-life situations.