Java and Python are two of the most popular computer languages in use today. Both are very mature and provide the tools and technology ecosystems to support developing solutions to the challenging problems that arise in the world of data science. Each has its idiosyncrasies. It’s important to understand how they compare tackling different problems, whether they shine or lack the required flexibility to handle the assigned tasks.  When one is preferable over the other or when they work in tandem complementing each other.

Python is a dynamically typed language, very straightforward to work with, and is certainly the language of choice to do complex computations if we don’t have to worry about intricate program flows. It provides excellent libraries (Pandas, NumPy, Matplotlib, ScyPy, PyTorch, TensorFlow, etc.) to support logical, mathematical, and scientific operations on data structures or arrays.