Machine Learning (ML) plays a crucial role in Data Science applications majorly in areas like finding unseen patterns, deriving information, making decisions and much more. Despite many surveys revolving around ML applications exist, their applicability in Data Science domain has not been much explored so far. Understanding this gap, an attempt has been made in this paper to provide an exhaustive survey on literature involving both ML and Data Science. The survey has been classified into various traditional sub-domains like cyber fraud, healthcare etc., and new-age ones like virtual try-ons and behaviorism. It is believed that this survey would help the Data Science Application Designers to apply ML so as to gain maximum benefits in terms of applicability knowledge, advantages as well as their limitations.