[helmholtz]

I won't hear of it. Matlab is great. Superb at manipulating matrices, great for getting started with differential equations, world-class plotting library, and massively forgiving. All the things a computational engineer like me needs.

I would never use it for producing software meant for distribution, but people mainly hate on it because it's 'cool', without realising that it excels at what it does. I fucking love matlab.

[spywaregorilla]

https://diffeq.sciml.ai/stable/tutorials/ode_example/

Diffeq are one of those things that Julia is a particularly good choice for that benefits strongly from its types and what not. Give it a shot if you do a lot of this.

Julia syntax resembles matlab more than python or R.

[helmholtz]

Chris Rackauckas' work on it was truly brilliant. I believe he also posts on HN sometimes. Anyway, I've had tonnes of fun playing around with differential equations in Julia!

[glial]

Thanks, I'll give it a shot. Matlab's syntax for matrices is, naturally, great.

[freemint]

If you will do a lot of matrix stuff you might want to listen to https://youtu.be/C2RO34b_oPM taking Vector Transpose Seriously

[maybelsyrup]

I know little about Matlab, but I know a lot about SAS, where it's the default analytical environment in public health practice and research. SAS is trash, and a ripoff to boot. R has made good inroads, giving SAS a tiny bit of pressure, but frankly it needs more. Bring on Julia! And python and anything else.

[xiaodai]

that's why SAS developed Viya so you can run Python and R code.

[swordsmith]

+1 on this. Matlab toolboxes are much more well tested, has thorough documentation and work much better out of the box, especially for controls and signal processing. Working with scipy can be a pain sometimes with incorrect or unstable results.