The former is a collection of tools, which allows us to learn from the given data and to extract patterns and interactions between series and values. The latter describes the possible set of actions in relation to the data itself: collection, manipulation, preparation, and visualization. It is important to flag a few differences between Data Science and the classical Econometrics we study at University.
Unlike Econometrics, which concentrates on solving non-linearity bias problems in a typical linear framework, Data Science is about improving our ability to work with non-linear relationships in the system. Another difference is that Econometrics concentrates on methods such as robustness, while machine learning algorithms became popular for their outstanding predictive performance 4. 3.