By using SAS one can easily transform data in better format to interpret some result and make decision. SAS is very good tool for data transformation.

SAS is developed by SAS institute which is useful for analytics like statistical analysis, predictive analysis, business intelligence. It is a very good tool for data processing in which data stored, processed and analysed to make it useful for analysis. It is done in linear and tabular forms which is useful for better data understanding. It is useful for decision making and to make data easily understandable by others.

It is quite difficult to understand functionalities of SAS. It can be accessed by professional who knows how to handle software.

Good. I feel like a learn something new everyday especially when handling data request. Doing the SAS training for certification were also incredibly helpful in expanding my understanding of what all I could do in SAS.

1) That the rules are relatively consistent making it easier to learn more and more technical jobs once you've got the basics 2) SAS will make you check you work and has useful error messages and alerts 3)that shear scope of procedures and step options available for data analysis. It offers users numerous back up options if they are not sure how to get one particular procedure to work

1) There is a definite learning curve with SAS and it takes time and patience to practice at it. It's worth it but other systems are slightly more intuitive. 2) SAS is not the best when it comes to data visualization tools like bar charts.

It is reliable and a can handle medium-large datasets without breaking a sweat. The flow of the SAS scripts is usually straightforward and readable for basic data manipulations. It allows SQL to be embedded which is what I take advantage of in 90% if the code that I write. The documentation online is throrough across the board, especially compared to the scattered nature of documentation for R or Python.

Rigid structure of the code and requires a lot of bad practices such as copying and pasting code blocks. This leads to thousands of lines being necessary when I could write only hundreds in another language. There may be more efficiencies I could take advantage of, but as an intermediate user I do not have time to learn the intricacies of this tool that is so different than everything else out there. Also, the high price is causing my organization to shift away from this software, but the pricing structure makes it hard to lower our usage without scrapping it all together.

I am developing statistical models for financial industry using SAS. In addition, I am doing portfolio analyses and also model monitoring tasks within the tool

SAS Enterprise Guide is the tool a data scientist will be able to perform all data preparation and analysis tasks with both hard coding or drag and drop functionalities. Even some forms of models can easily be developed within the tool. On the other hand, SAS Miner is also very useful for statistical model build. The advantage of all these SAS tools is the visibility of the data from all these tools as they are integrated with each other

As data science word is evolving in a very fast pace, SAS should also align itself to this world. SAS sometimes stays behind the popular algorithms which could be done with R and Python. I am sure they will also be covered soon within the tools.

SAS is great and has great documentation and SAS also provides a lot of great books. I have not no problem with its difficulties because I know every good thing has costs.

SAS-STAT provides a lot of statistical procedures but from the point of view of an experienced Statistician, this is not the main advantage of SAS with respect to its comparators including R and Stata. SAS is recognized as the gold standard statistical software for biostatistics and statistical programmer job titles in the pharmaceutical industry where the cost of software is not important with respect to the overall cost of drug development. So the main advantage of SAS is its prestige that facilitates some of the challenges in submitting drug development applications to governmental agencies.

Working with SAS is not easy for most statistics graduates where they are well familiar with R. On the other hand, Stata software is also complete and is menu-driven so it is easy to use. Hence, the only reason that you may need to chose SAS is just its prestige.