Julia is a new programming language that takes advantage of recent innovations in compiler technology to offer the functionality, ease-of-use, and intuitive syntax of dynamic programming languages at the speed of C. One if its goals is to eliminate the so-called two language problem—when the users code in a high-level language, such as R and Python, but performance-critical parts have to be rewritten in C or C++. Julia feels like a dynamic language and offers all the productivity features associated with these. But at the same time, it eliminates the performance trade-offs, proving to be productive enough for prototyping and exploratory coding, and efficient enough for performance-critical applications.
Its built-in package manager provides access to over 2,000 third-party libraries that seamlessly extend the language with powerful new features—and we've learned how to take advantage of these. And if that is not enough, Julia has the ability to call functions written in other languages, such as C, Fortran, Python, or Java, to name just a few.
Julia is free and open source (MIT licensed) and can be deployed on all the major operating systems, including Windows, the main Linux distributions, and macOS. It also comes with some very good IDE and editor options.
Now that we have successfully set up our development environment, it's time to dive deeper into Julia's syntax. In the next chapter, we'll take a look at some of the basic building blocks of the language—defining variables and constants, manipulating and using Strings and numeric types, and working with Arrays. As a testament to Julia's productivity, that's all we'll need (together with some extra packages that we'll add) in order to perform powerful exploratory data analysis on the Iris flowers dataset. Meet you in the next chapter!
