Database Reference
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rating models into production systems, etc. One option for the Data Scientist is to learn
several different software packages that each specialize in one or two of these things, but
don't do them all well, plus learn a programming language to tie them together. (Or do a
lot of manual work.) An alternative is to use a general-purpose, high-level programming
language that provides libraries to do all these things. Python is an excellent choice for this.
It has a diverse range of open source libraries for just about everything the Data Scientist
will do. It is available everywhere; high performance python interpreters exist for running
your code on almost any operating system or architecture. Python and most of its libraries
are both open source and free. Contrast this with common software packages that are avail-
able in a course via an academic license, yet are extremely expensive to license and use in
industry.”
So, which is better for the Data Scientist? R or Python?
This depends on the emphasis of the particular Data Scientist's work and skills.
In general, R can be more valuable to those whose range of tasks and talents relate
more to mathematical and statistical missions than to programming, while Python is the
better choice the closer one's tasks and talents are to programming.
One of the primary strengths of R is the widespread support the language has in the
statistical community writ large. Myriad robust pre-existing packages of code have been
contrived to handle thousands of precise statistical tasks. What is more, R's array-orien-
ted syntax makes the transition from math to implementation extremely easy, especially
for Data Scientists with limited programming skills. Given this, as one might guess, it is
a fairly simple thing within R to move vectors and matrices and functions from paper to
code.
On the other hand, Python is very good for accomplishing tasks common to coding for
anything other than mathematics. Also, in general, Python seems (to me at least) to be syn-
tactically much cleaner than R.
