Data science/big data exists at the crossroads of traditional analytics and large scale computation. As such, neither the traditional tools of analytics (R, Mathematica, Matlab) nor mainstreams languages (Java, C++, C#) supply its requirements precisely as they cannot simultaneously provide the required mathematical abstractions and real-word platform power. The Clojure language is unique in that it has the potential to provide the best of both worlds. The video talk below by Edmund Jackson explores why Clojure is a good choice for data science.
Clojure is a dynamic programming language that targets the Java Virtual Machine. It is designed to be a general-purpose language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language – it compiles directly to JVM bytecode, yet remains completely dynamic. Every feature supported by Clojure is supported at runtime. Clojure provides easy access to the Java frameworks, with optional type hints and type inference, to ensure that calls to Java can avoid reflection.
Clojure is a dialect of Lisp, and shares with Lisp the code-as-data philosophy and a powerful macro system. Clojure is predominantly a functional programming language, and features a rich set of immutable, persistent data structures.