Julia is an open source, high-level, high-performance dynamic programming language for technical computing.
See comparison between Julia and Go.
Ranked in these QuestionsQuestion Ranking
Pro Great standard REPL
Out of the box Julia has a very good Read-Eval-Print-Loop, which both completes functions and types, as well as completion based on history of previous statements. It integrates well with the shell and has an excellent online help system.
Pro Strong dynamic typing
Dynamic and high level, but does not isolate the user from properly thinking about types. Can do explicit type signatures which is great for teaching structured thinking.
Pro Nice regular syntax
Julia code is easy to read and avoid a lot of unnecessary special symbols and fluff. It uses newline to end statements and "end" to end blocks so there is no need for lots of semicolons and curly braces. It is regular in that unless it is a variable assignment, function name always comes first. No need to be confused about whether something is a method on an object or a free function.
Unlike Python and Ruby, since you can annotate the types a function operates on, you can overload function names, so that you can use the same function name for many data types. So you can keep simple descriptive function names and not have to invent artificial function names to separate them from the type they operate on.
Pro Almost as fast as C
Julia runs almost as fast as (and in fact in some cases faster than) C code.
Pro Written in itself
The Julia language is written in itself to a much larger extent than most other languages, so a budding programmer can read through the depths of the standard library and learn exactly how things work all the way down to the low-level bit-twiddling details, which can be englightening.
Pro Amazing learning curve
Julia requires no boilerplate code – a beginner needs to write only the parts they care about. This combined with the REPL provides the best learning experience available.
Pro Powerful n-dimensional arrays
Julia has built in n-dimensional arrays similar in functionality as Python's numpy.
Pro Function and operator broadcasting
You can perform operations on scalars, for example 2^2 or [1, 2, 3].^2.
Pro Function overloading
You can have multiple functions with the same name, but doing different things depending on function arguments and argument types.
Pro High-level code
Julia provides a high level of abstraction, making platform-independent programming trivial and easing the learning curve.
Con Young language with limited support
Julia was released in 2012. Due to its short existence, there is a limited amount of support for the language. Very few libraries exist as of yet, and the community is still quite small (though growing quickly).
Con 1-based array and column major
This design probably comes from Matlab, but makes it unnatural to interface C and C++ and python.