Julia is a high level, high-performance dynamic language with a focus on data science and scientific computing applications. It is both easy to write, with syntax that can pass off as pseudocode, and fast, like a statically compiled language like C. This combination of ease of use and high performance makes Julia uniquely powerful for a data science team:

Everyone can write Julia: There is no prototyping in a high level language for flexibility, then re-implementing the same in C / C++ for performance, and finally writing wrappers to make it usable from the high-level language. Julia eliminates this dichotomy by virtue of being both easy and fast.

Powerful language features: Features like multiple-dispatch, Just-in-time compilation, lisp-like macros, low-level code introspection allows developers and data scientists to move quickly, creating powerful, yet performant abstractions.

In this workshop, we will introduce the Julia language and its ecosystem of packages, and show how people are using Julia in production environments across a wide range of domains.

Why Julia? Is Julia fast? Why is Julia fast? Basic syntax Multiple-dispatch Working with arrays Working with a dataset - queries and plots Metaprogramming basics Overview of important Julia organizations and packages Who is using Julia and why? We will be using Jupyter notebooks with the Julia back-end for the tutorial. Attendees can join along and try the same at juliabox.com without requiring any installation. However, we do recommend locally installing Julia via http://julialang.org/downloads/, and IJulia - the Jupyter backend using Pkg.add("IJulia").

Further reading:

Julia website Why we created Julia Some insights from a Julia developer The Julia slack Julia Computing