Statistics with Julia [pdf]
Julia is everything python could have been, and much more. I'm stuck with python right now as a lot of people in the data science/ML community are, but it's becoming increasingly viable to use Julia for "real" work. The Python-Julia interop story is pretty strong as well, which allows you to (somewhat) easily convert pandas/pytorch/sklearn code into Julia using Python wrappers. Julia has some unconventional things in it but they are all growing on me:<p>1. Indices by default start with 1. This honestly makes a ton of sense and off by one errors are less likely to happen. You have nice symmetry between the length of a collection and the last element, and in general just have to do less "+ 1" or "- 1" things in your code.<p>2. Native syntax for creation of matrices. Nicer and easier to use than ndarray in Python.<p>3. Easy one-line mathematical function definitions: f(x) = 2*x. Also being able to omit the multiplication sign (f(x) = 2x) is super nice and makes things more readable.<p>4. Real and powerful macros ala lisp.<p>5. Optional static typing. Sometimes when doing data science work static typing can get in your way (more so than for other kinds of programs), but it's useful to use most of the time.<p>6. A simple and easy to understand polymorphism system. Might not be structured enough for big programs, but more than suitable for Julia's niche.<p>Really the only thing I don't like about the language is the begin/end block syntax, but I've mentioned that before on HN and don't need to get into it again.