I just found this explanation for the different statistical data types, but I'm still not sure what the difference between ordinal categorical and numerical discrete variables is.
The example for ordinal data given in this link was the rating of a restaurant in stars. But they define numerical discrete data as follows:
Discrete data represent items that can be counted; they take on possible values that can be listed out. The list of possible values may be fixed (also called finite); or it may go from 0, 1, 2, on to infinity (making it countably infinite). For example, the number of heads in 100 coin flips takes on values from 0 through 100 (finite case), but the number of flips needed to get 100 heads takes on values from 100 (the fastest scenario) on up to infinity (if you never get to that 100th heads). Its possible values are listed as 100, 101, 102, 103, . . . (representing the countably infinite case).
In my opinion the star rating of a restaurant perfectly fits into this definition. Its a finite number of stars and i can count them. Why is this ordinal data? Is this example just wrong? If yes: could anybody provide me another example for ordinal data?
I want to understand this because i want to convert this data to numerical features. I can convert nominal categorical data to onehot vectors, but I'm not sure what's the right way to convert categorical ordinal features.