Now that much less mature companies are realizing the value of 'analytics' (I hate that word) SAS's cost doesn't really make sense.
I don't actually hate SAS and I'm very productive using it but at the same time I do feel that not knowing R limits my opportunities if I ever want to change jobs and work for an outside organisation.
We don't strictly use SAS for analytics. A big part of SAS we use is the "BI" side I don't know if that acronym is still in vogue but I'm talking about ad-hoc querying reporting etc. The kind of stuff one step above what you'd use a spreadsheet for if that makes sense.
I think where SAS excels is they have made it very easy for non experts to be productive with it. Kind of similar to MATLAB in engineering world if people are familiar with that.
A lot of non statiscians and non programmers use it inside my work (my background is engineering). Accountants, Managers, mechanical engineers etc are all pretty capable of using Enterprise Guide to run adhoc queries and generate reports and the like. The only other similar tool I'm aware of is IBM's Cognos. We used to use both packages (as well as Microsoft Access) but about 10 years ago the business agreed to standardize around SAS. I've heard there is a similar tool in the R world to Enterprise Guide (R-Studio I think???) but I'm not all that familiar with it I've heard it behaves more like an IDE rather then a drag and drop way to construct queries, graphs/reports etc.
If anyone has made the transition from SAS (or Cognos) to R (especially for a large org) I'd be keen to hear what tools you'd recommend and how the business found it?
My shop is going the opposite direction, from R/excel to SAS+Cognos. I'm not happy about that decision, but the pay is good and the problems are still interesting.
This does not have to involve machine learning, and "pattern recognition" is an academic term (and might be confusing for laymen).
Business people seem determined to invent new jargon when our current vocabulary is sufficient.
- descriptive analysis (clustering, summary statistics, etc.)
- predictive analysis / forecasting
- optimization
- automation
I suppose that last one involves engineering as well.
It's... kinda working.
The issue is what is "data science" really? In what respect is it different from traditional statistics and data analysis and not just a new buzzphrase?
Probably many jobs using SAS could be considered "data science" but don't use the specific buzz words and phrases that the author specifies in his methodology to identify "data science" jobs. Thus, the headline that "R Passes SAS" could be inaccurate, except in the sense that R is more popular among statistics and data analysis jobs that use "data science" buzzwords and phrases.