Frequentists put the probability on the data, believing the parameter is fixed, while Bayesians put the probability on the parameter and believe the data is fixed

In the frequentist world, they believe that the parameter exists as a fixed value and the data is drawn by sampling from an infinite pool. The more data, the closer we should get to that true parameter value.

Bayesian approach is different. It only assumes the data we have observed and estimates a probability distribution over the parameter, expressing how strongly we believe in each possible value. Moreover, the parameter may well be fixed - it still gets a probability distribution, which measures our uncertainty about its value, not any variation in it.