SAS-STAT Software

Among the various other statistical software I have used, I have found SAS to be quite user friendly. It actually was the first statistical software I used before I tried R, SPSS and other programming tools such as Matlab. Other than being user friendly, it was easy to generate a bunch of graphs to show what the data was doing using and editing a simple piece of code

It does take a little bit of time to familiarize yourself with the software but that is true for most software anyway

I use the software to accomplish 80% of my duties so I may be a bit biased.

The software is extremely flexible allowing you solve many problems using its predefined processes and functions. It can be used to solve many types of reporting problems. It has built in solutions to many common and uncommon statistical and business problems.

It has a variety of interfaces that are available to clients, depending on what a user needs they might choose to use different tools that are available to them. The whole suite is actually quite large and most users will only ever touch the parts of it that helps them accomplish their goals. The SAS eco-system is extensive, mature and well-developed. We rely on it for many mission critical solutions.

Often times the various prebuilt processes have their own syntax and they are not consistent throughout the application. The software package is not cheap.

can handle large scale data very easily. Industrial scale software to run big data

installation takes a long time and consumes a lot of space on your PC.

Doing the same thing in Python takes less effort.

What I love about SAS are syntax consistency and standardized functions. SAS as an institute has been doing a great job at maintaining the software and providing great experiences to its users. Whenever I face an error, the message often tells me what to fix exactly whereas other programming languages like R doesn't - it's extremely annoying and time-consuming.

One thing SAS lacks - perhaps it's just because of the nature of the software - is its visualization capability. I'd do a lot of statistical analysis in SAS, but have to find an alternative way to create figures to convey the results as the graphics look like they're from 90s.

The functionality and ease of SAS is unparalleled for conducting a variety of advanced analyses, especially in GLM and multilevel (HLM) frameworks. I find when writing code for the same analyses with the free software R that for more complex analyses, the R code is always much longer. Additionally, SAS documentation is very thorough and it is always easy to find the answers you are looking for.

The graphics. SAS graphics are so basic and look so dated that I often use a different program to generate plots, even though they are easier to generate in SAS. The graphics really need to be updated. Also, the SAS capability for structural equation models (SEMs) is limited, and I usually use Mplus for those analyses.