Sure! Time and space complexity are measures used to analyze the efficiency and resource requirements of an algorithm.

Time Complexity:

Time complexity refers to the amount of time an algorithm takes to run as the input size increases. It helps us understand how the algorithm's performance scales with larger inputs. Time complexity is often expressed using Big O notation.

For example, let's consider a simple algorithm that finds the maximum element in an array of size n. In this case, the time complexity is O(n) because, in the worst case, the algorithm needs to compare each element of the array to find the maximum.

Space Complexity:

Space complexity refers to the amount of memory an algorithm requires to run as the input size increases. It helps us analyze the memory usage of an algorithm. Space complexity is also often expressed using Big O notation.

Let's take another example of an algorithm that calculates the Fibonacci sequence up to a given number n. If we use a recursive approach, the space complexity would be O(n) because the recursive calls consume memory on the call stack. Each recursive call adds to the space required.

In summary, time complexity measures the time taken by an algorithm, while space complexity measures the memory required by an algorithm. Both are important factors to consider when analyzing the efficiency and resource usage of algorithms.