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.

SAS 9.3 gives you tremendous power to clean, manipulate, and analyze large datasets. I've been a SAS user for a while and prior to that I had only worked with SPSS. SAS opened the door for me to think about my data differently. I've used SAS now on criminal sentencing data, healthcare claims data, and operational case management data. Whether you need to create an automated report, write algorithms to mine data, or prep a dataset for another software like Tableau, SAS is a fantstic tool. Additionally, SAS has a litany of trainings available for purchase to help you get the most of your data.

SAS is very costly. It's important to keep in mind that in addition to being a product, SAS is also a programming language that as of writing is not patented. If you are on a budget there is a company out of the UK that sells a software that runs Base SAS programming language for a fraction of a price, great for outfitting a startup team. SAS Graph module leaves something to be desired, there are better softwares that focus on just visualization.

Use it just for the sheer power. Quite expensive licenses and there are certainly better and faster packages available. Its just that migrating system from a foolproof and proven system just seems too much of work. Hope they up their game plan soon or soon, it might be time to make the plunge.

Very powerful. You want it and you can do it. Quite intuitive programming. Has multiple ways to integrate with other packages for data exchange. Has a reasonably versatile scalablity.

Its aged. Much slower than some of the latest products in the market. Syntax while not complex can hardly be described intuitive Interface seems at least a couple of decades old.

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.

Used SAS to do a bunch of design processes and quick analysis on a large relational database. I did get certified in SAS, but even I will admit that I still hardly feel comfortable coding on my own, so I still rely on the GUIs.

There are a lot of different SAS softwares to fill a variety of uses. If you have a problem, then SAS has almost certainly already thought of it. Some of their GUIs are very user-friendly too.

Learning how to code in SAS is a real pain if you've ever learned any other coding language. It's just way different than anything else.

Well tested software that can do a lot of statistical analysis quite fast.

If what you want to do is supported in a proc then it can be very easy to to get your work done. The documentation is *very* good.

If what you want isn't supported you *might* be able to find a macro online. But the macro language is very archaic feeling especially compared to R or Python. The graphics used to be terrible but they have gotten quite a bit better over the years. It's also incredibly expensive especially when you compare it to R (free and open source).

Great, but you pay for it...

This is the perfect tool to handle database. Powerfull and fast architecture to deal with your query. The fact it can be coded in SQL is also a nice plus. Great set of add-on can really help produce awesome and clear reports.

Not so user-friendly coding language, some particulary are a non-sense, in my opinion. Some add-on are expensive for what they add to the product. Price is off the chart.

There are tons of examples and answered questions available with a simple Google search. You can generally figure out how to accomplish your task by doing a little research.

The interface could use some updating...it seems old and dated. Need to intriduce new functionality in order to keep up with R and Python, which I prefer using.

It can be considered to be relatively expensive. It is more appropriate for those who are statistically or mathematically oriented.

It is very easy to use and comprehensive. It is applicable to every aspect of analytics, including predictive. Its results are valid, reliable and respected every where.

I have used SAS for multiple research projects. It is simple and easy to use. Very similar to other tools such as STATA and SPSS. However, it is more easily accessible as compared to others.

There are many glitches when using it to analyze larger data sets.

SAS-STAT offers industry best statistical interferences, reports and all other items can be generated using regulatory guidelines.

No pre-built report templates, UI is pretty old, still has to write code for reports.

Coding allows for personalized data analysis and ease of use

Can be difficult to troubleshoot issues as helpmmenus are unclear and frequently link to pages that are not related to core issue

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.

I love that it has all the components to run and collect data

I don't like that the user interface looks outdated.

Analysis functionality is very good to do all different data analysis, like modeling, prediction, or basic function.

Visualization can be improved, like all the charts. Because of price, usually used in big companies.

Statistical results obtained from proc are very comprehensive. Help files are also very useful. Easy to do SQL on SAS.

It is fairly hard to tailor the output, especially the plots.

SAS makes it capable to produce incredible data and analysis for the study of disease, healthcare and public health in general.

It is definitely a difficult software to utilize as far as coding is concerned. I had a difficult time keeping up with the course.

not many when there are other, free packages available.

The outputs are really nice in SAS - it runs several useful tests and plots just with the regress command. It is an easy way to see right away what is going on with the data.

The online support community is not helpful and very disorganized compared with other stats packages. Additionally, the software itself has a lot of bugs and I found myself needing to quit and restart the program just for the exact same code to work! Getting it to read data can be a pain as well. I no longer use for these reasons.

Used it for most research, including dissertation. Easy to learn, not too complicated with coding.

It’s a bit of a headache to learn all the macros and such.

Overall it can be useful once you find your data. However there are other statistical software programs I’m more inclined to use that are more user friendly.

SAS appears user friendly and can be great for analyzing a large dataset.

I spend a large amount of time in SAS trying to figure out how to import data. This should be the most accessible and intuitive part of data software. However SAS does not make it easy.

It's best if you are from science background

As of now I don't see any flaws will update if they comes up in future