If you are thinking about taking Julia, the hot new mathematical, statistical, and data-oriented programming language, for a test drive, you might need a little bit of help. In this blog we round up some great posts discussing various aspects of Julia to get you up and running faster.
If only you could always read through the intentions and thoughts of the creators of a language! With Julia you can. Jump over to here to get the perspectives of four of the original developers, Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman.
We are power Matlab users. Some of us are Lisp hackers. Some are Pythonistas, others Rubyists, still others Perl hackers. There are those of us who used Mathematica before we could grow facial hair. There are those who still can’t grow facial hair. We’ve generated more R plots than any sane person should. C is our desert island programming language.
We love all of these languages; they are wonderful and powerful. For the work we do — scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing — each one is perfect for some aspects of the work and terrible for others. Each one is a trade-off.
We are greedy: we want more.
An IDE for Julia
If you are looking for an IDE for Julia, check out the Julia Studio. Even better, Forio, the makers of this IDE, offer a nice series of beginner, intermediate, and advanced tutorials to help you get up and running.
By far the most comprehensive and best source of help and information on Julia are the ever growing Julia Docs which includes a Manual for the language (with a useful getting started guide), details of the Standard Library, and an overview of available packages. Not to be missed are the two sections detailing noteworthy differences between Matlab and R.
Avi Bryant provides a very nice overview and comparison of Matlab, R, Julia, and Python. Definitely recommended reading if you are considering a new data analysis language.
This post is from mid-2012 so a lot has changed with Julia. However, it is an extensive look at the language from an experienced R developer.
There are many aspects of Julia that are quite intriguing to an R programmer. I am interested in programming languages for “Computing with Data”, in John Chambers’ term, or “Technical Computing”, as the authors of Julia classify it. I believe that learning a programming language is somewhat like learning a natural language in that you need to live with it and use it for a while before you feel comfortable with it and with the culture surrounding it. Read more …
Continuing on this theme of statistics and Julia, John Myles White provides a great view of using Julia for statistics which he updated in December of last year.
A quick look at Julia from the perspective of a Matlab programmer and pretty insightful as well.
Julia is a new language for numerical computing. It is fast (comparable to C), its syntax is easy to pick up if you already know Matlab, supports parallelism and distributed computing, has a neat and powerful typing system, can call C and Fortran code, and includes a pretty web interface. It also has excellent online documentation. Crucially, and contrary to SciPy, it indexes from 1 instead of 0. Read more …
Finally, we leave you, good reader, with a contrarian view point.
Latest posts by Sean Murphy (see all)
- Flask Mega Meta Tutorial for Data Scientists - February 16, 2014
- Expanding the Online Presence of Data Community DC – W3DC’s Strategic Plan for 2014 - January 6, 2014
- A Tutorial for Deploying a Django Application that Uses Numpy and Scipy to Google Compute Engine Using Apache2 and modwsgi - December 17, 2013