Comment common sense (Score 1) 191
Comment Ok (Score 1) 303
Comment The Brains of Men and Women ARE Different (Score 1) 163
Comment It's their sport (Score 1) 181
Comment Re:Smart man (Score 1) 378
Comment That's just... (Score 1) 46
Comment More than curious, (Score 2) 188
Comment Re:Biting flies can't evolve? (Score 1) 190
Comment Solved (Score 1) 465
- 1. Ease of reading/programming/maintaining by non-professional programmers.
- 2. Accessability.
- 3. Well supported/documented libraries.
- 4. Quickness.
#1 reduced the field of choices (IMO) to
* Matlab/Octave
* R/S+
* SAS
* Perl
* Python
* Julia
As for #2 gives preference to Python, R, Julia, Perl, or Octave (Your situation may not be as limiting).
#3 led me to many searches that all indicated that R and Python have a rich set of libraries and lots of community support.
As for #4 From Julia's website http://julialang.org/ they show nice benchmark information that indicates that Python is pretty quick.
My conclusion was that I couldn't really go wrong between R or Python. However, I chose Python because it was quicker, I like the syntax better, I like the libraries better (NumPy, SciPy, Pandas, Matplotlib) and is seems to play nicer with everything else.
This is what worked for me and how I went about deciding.