I found this interesting article about data analysis languages on KDnuggetes.
Julia is described as follows by ActiveWizards:
Julia is a high-level, high-performance dynamic programming language for numerical computing. Sophisticated compiler, numerical accuracy, distributed parallel execution, and an extensive mathematical function library make Julia popular for data science. Its Base library is mostly written in Julia itself.
- Julia is free, so you don’t need a license.
- Julia is compiled but not interpreted. Consequently, it wins in speed.
- Julia can be used not only for numerical analysis. It can be used as a general-purpose programming.
- Julia code can be combined with other language libraries written in Python, C, and Fortran. Moreover, we can interface with Python code by PyCall library and share data between Python and Julia.
- Julia can provide metaprogramming. Its programs can produce other Julia programs and moreover modify their own code.
- Julia is not properly developed. Due to its recent entry, there is a need for improvements. Julia’s tools are not as fluid and reliable as they wished to be.
- Julia has a limited number of packages because it is young and their community is pretty small. Unlike R and Python, Julia doesn’t have such a variety of packages.
- Julia can’t identify issues. Julia is far behind from Python and R in terms of identifying issues and debugging tools. But soon more tools were expected to be developed for users.