The short answer probably is that all observable processes, which we may want to analyze with statistical tools, are stochastic processes, that is, they contain some element of randomness. The course will probably teach you the mathematics behind these stochastic processes, e. g. distribution functions, which will allow you to grasp the function of your statistical tools.
I think you can compare it with an automobile: As you can drive your car without understanding the engineering behind it, and without theoretical knowledge about the dynamics of your car on the road, you can apply statistical tools to your data without understanding how these tools work, as long as you understand the output. This will probably be good enough if you want to do basic statistics with well behaved data. But if you really want to get the most out of your car, to see where it's limits are, you need knowledge about the engineering, the dynamics of your car on roads and in curves and so on. And if you want to get the most out of your data with the help of your statistical tools, you need to understand how data generation can be modeled, how tests are devised and what the assumptions behind your tests are to be able to see where those assumptions are not valid.