The amount of period it takes for an algorithm to execute, proportional to the quantity of data that it is given as input.

This statement is describing the concept of time complexity in computer science. Time complexity is a computational concept that deals with the computation time of an algorithm. It is directly proportional to the size of the input data. Here's a step-by-step explanation:

1. The "amount of period it takes for an algorithm to execute" refers to the time complexity of an algorithm. This is a measure of the amount of time an algorithm takes to run, as a function of the size of the input to the program.

2. The phrase "proportional to the quantity of data that it is given as input" means that as the size of the input data increases, the time it takes for the algorithm to execute also increases. This relationship is typically expressed in Big O notation, which describes the upper bound of the time complexity in the worst-case scenario.

3. For example, if an algorithm has a time complexity of O(n), this means that if the size of the input data is n, the algorithm will take approximately n units of time to execute. If the size of the input data doubles, the time it takes for the algorithm to execute will also double.

4. Therefore, the statement is saying that the time it takes for an algorithm to execute is directly related to the size of the input data. The larger the input data, the longer the algorithm will take to execute.