What is the difference between oversampling minority data and simulating minority data?
So after some research, here is my answer.
Bootstrapping is for either 1D array or 2D (also referred to as pairs bootstrap) of any columns of data (only columns) while oversampling is for the minority class in the dataset (rows + columns). 
Bootstrapping is less popular for binary classification with 100+ features as it does not maintain the underlying relationship between the features while oversampling (depending on the technique) does maintain the underlying relationship between the features in the data. Moreover, Bootstrap works best for continuous data in a single column in case of 1D or 2d, as then you can produce a range of data for the whole column regardless of minority or majority class as if the measurements where taken n numbers of times (n is is any integer 1+)
Thus, in this question scenario, oversampling is preferred as it maintains the underlying structure of the data of the minority class.
Oversampled data as in those created by the SMOTHE and ADASYN method. 
Simulated data as in those created by statistics, using extreme mean values, bootstrapping and measuring performance with the p-value.
The application is to increase the minority class of underrepresented data using python.