3 languages changing data science

Julia

The Julia language, first released in 2012, was created specifically for data scientists. Its creators wanted to have a language as easy to work with as Python, but as fast as C or Fortran, and without having to work in more than one language at a time for the best results.

Julia works its magic by being “just-in-time” compiled, or JITed, to machine-native code, by way of the LLVM compiler system. Julia code has the simplicity of Python’s syntax, so it’s straightforward to write and supports quick results. You can let the compiler infer types at first, then supply type annotations for better performance later on.

Julia’s package collections contain libraries for most any common data science or analytics work—common math functions (like linear algebra or matrix theory), AI, statistics, and tools for working with parallel computing or GPU-powered computing. Many of the packages are written natively in Julia, but some wrap-in well-known third-party libraries such as TensorFlow. And if you have existing C or Fortran code in a shared library, you can call it directly from Julia with minimal overhead.