A comparison with other languages for the data scientist
Because speed is one of the ultimate targets of Julia, a benchmark comparison with other languages is displayed prominently on the Julia website (http://julialang.org/). It shows that Julia's rivals C and Fortran, often stay within a factor of two of fully optimized C code, and leave the traditional dynamic language category far behind. One of Julia's explicit goals is to have sufficiently good performance that you never have to drop down into C. This is in contrast to the following environments, where (even for NumPy) you often have to work with C to get enough performance when moving to production. So, a new era of technical computing can be envisioned, where libraries can be developed in a high-level language instead of in C or FORTRAN. Julia is especially good at running MATLAB and R-style programs. Let's compare them somewhat more in detail.
MATLAB
Julia is instantly familiar to MATLAB users; its syntax strongly resembles that of MATLAB, but Julia aims to be a much more general purpose language than MATLAB. The names of most functions in Julia correspond to the MATLAB/Octave names, and not the R names. Under the covers, however, the way the computations are done, things are extremely different. Julia also has equally powerful capabilities in linear algebra, the field where MATLAB is traditionally applied. However, using Julia won't give you the same license fee headaches. Moreover, the benchmarks show that it is from 10 to 1,000 times faster depending on the type of operation, also when compared to Octave (the open source version of MATLAB). Julia provides an interface to the MATLAB language with the package MATLAB.jl (https://github.com/lindahua/MATLAB.jl).
R
R was until now the chosen development language in the statistics domain. Julia proves to be as usable as R in this domain, but again with a performance increase of a factor of 10 to 1,000. Doing statistics in MATLAB is frustrating, as is doing linear algebra in R, but Julia fits both the purposes. Julia has a much richer type system than the vector-based types of R. Some statistics experts such as Douglas Bates heavily support and promote Julia as well. Julia provides an interface to the R language with the package Rif.jl (https://github.com/lgautier/Rif.jl).
Python
Again, Julia has a performance head start of a factor of 10 to 30 times as compared to Python. However, Julia compiles the code that reads like Python into machine code that performs like C. Furthermore, if necessary you can call Python functions from within Julia using the PyCall package (https://github.com/stevengj/PyCall.jl).
Because of the huge number of existing libraries in all these languages, any practical data scientist can and will need to mix the Julia code with R or Python when the problem at hand demands it.
Julia can also be applied to data analysis and big data, because these often involve predictive analysis, modeling problems that can often be reduced to linear algebra algorithms, or graph analysis techniques, all things Julia is good at tackling.
In the field of High Performance Computing (HPC), a language such as Julia has long been lacking. With Julia, domain experts can experiment and quickly and easily express a problem in such a way that they can use modern HPC hardware as easily as a desktop PC. In other words, a language that gets users started quickly without the need to understand the details of the underlying machine architecture is very welcome in this area.