I am trying to understand how does standardizing the data help us? Can we also standardize a distribution which is not normal?
Standardization is a common technique to deal with many different problems with the data. The biggest one being: scale.
When our data is of completely different scale like this:
One variable takes values in the range: 500,000 to 1,000,000.
Another takes values in the range: 0.001 to 0.005
Then the two are not really comparable. This causes a great deal of problems when using machine learning models (seen later in the course).
A simple solution is to standardize all variables. Once they have the same magnitude/scale, models work much better (due to homoscedasticity, etc.).
Note that all these topics will be explored later in the course, so you need not worry about it.
Finally, there are mathematical transformations that can transform non-normal distributions to normal. A commonly used term is ‘normalization’, which is a type of ‘standardization’ (but the formula is different).
Everything will become clear around the end of the course, promise!