Scala :The Bridge Language
The Fragmented World of Languages Lots of changes in Data Science have happened in its every dimension: applications, algorithms and techniques, software and ,of course, languages. Languages tell a fascinating story because it is a reflection of the nature and the state-of-mind of the practitioners. Not so surprising, they have changed a lot over the years. When I first started getting interested in Data Science, SAS and Matlab were still sure bets. A few years in (2013 or so), R became the Lingua Franca around me: easy to code and understand, the vectorized calculations backed by the DataFrame API made it very practical for non-CS practitioners (read statisticians, engineering generalists like myself) to use. It did away with a lot of the lower level considerations and ended up making a simpler interface, predictably at the expenses of the CS crowd, loathing such abstractions. Today, I think we are at another junction where the ball is moving in the opposite direction: the CS