B1gred

5 ms·

1.
by B1gred 11y ago
I am a statistician working in the data sciences. I see lots of programmers show interest but do not have the depth of knowledge in mathematics and statistics. They can apply libraries and do 80% fine but do not have the educational backgro
2.
by B1gred 11y ago
It can mean anything, and that is why it is hard. Many old school statisticians can not work with anything other than csv, excel spreadsheets or basic sql queries. Munging is the conversion to a nice format that can then be used for analysi
3.
by B1gred 11y ago
It is slow. And it is ok. Very few times will R ever beat any other language. Usually it is not off by much, but especially if coded by a novice using for loops vs apply functions can make is 100 -1000 x slower. Another example is the immut
4.
by B1gred 11y ago
Depends on coworkers. When in school, my professors couldn't do anything if it wasn't in a nice csv file. But, us data scientist / statistical programmers are well versed in digesting data in almost any form, especially a dat
5.
by B1gred 11y ago
I program in R 80% of my day. I have experiences in all the major alternatives but keep returning to R. It has one huge flaw, being slow but otherwise is fantastic to work with and has a vibrant community. The bigger issue is that while R i
6.
by B1gred 11y ago
Python has a steeper learning curve and is not as curtailed to simple data analysis. Many use Rstudio (an ide) and use the import data, and other tools to make then skill entry even lower. Also, mathematicians and statisticians think functi