This discussion is motivated by a simple question (that was posed to me by a theoretical computer scientist): “*What is the role of Statistics in the development of Theoretical Data Science*?” The answer lies in understanding the big picture.

**Theory of [Efficient] Computing:** A branch of Theoretical Computer Science that deals with how quickly one can solve (compute) a *given* algorithm. The critical task is to analyze algorithms carefully based on their performance characteristics to make it computationally efficient.

**Theory of Unified Algorithms:** An emerging branch of Theoretical Statistics that deals with how efficiently one can represent a large class of diverse algorithms using a *single* unified semantics. The critical task is to put together different “mini-algorithms” into a coherent master algorithm.

For overall development of Data Science, we need both ANALYSIS + SYNTHESIS. However, it is also important to bear in mind the distinction between the two.