What is difference between factor analysis and principal component analysis



I am studying about dimension reduction and while studying about I came across two methods
1-factor analysis
while studying it I learn that they are almost same but I am not able understand what is exact difference between them


If Your main aim is only to reduce observed data - use PCA. If Your
aim is to reason about latent factors - use factor analysis.

This short tutorial will give you a very good understanding of factor analysis:

On the other hand, principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

Hope this helps!