Why Data Scientists Should Learn Algorithms and Data Structures?

The first useful concept you will encounter is algorithmic complexity and Big-Oh notation. It is a method that allows understanding how well your code scales with the data. This concept is important to data scientists due to the need to process an ever-increasing amount of information produced daily. By getting rid of less important details, you will be able to reason about the performance of an algorithm regardless if it is written in Python or C and executed on a laptop or NASA's supercomputer. In a sense, it defines a basic vocabulary for the design and analysis of algorithms while suppressing architecture and language-dependent details -- these are considered a constant factor, not relevant to the big picture.