What Are the Benefits of SAS and R Programming Training

If you are a senior data scientist or pro in predictive analytics, you would probably be using both R & SAS. But, what if you are a beginner or just thinking about to start your career in data science or machine learning or any other related field, then you must be wondering whether to use R or SAS? It has always been a topic of great debate among data scientists, researchers and analytics professionals.

In this article, we will discuss benefits of both SAS as well as R programming:

INTRODUCTION TO THE LANGUAGES:

R programming:

R is an open source programming language, free to access and pen to all to perform data analysis tasks. The R language is extensively used among data miners for generating statistical software and data analysis. The source code for R software environment is composed essentially in C, FORTRAN, and R. Language R is available for free under the GNU General Public License and is pre-compiled binary versions that are provided for different operating systems. While R has a command-line interface, there are some graphical front-ends also available. To learn R programming in detail, one can join best R programming training institute in Noida.

SAS:

The SAS language is a programming language which is used for statistical analysis, introduced by the project at the North Carolina State University. It can input data from general spreadsheets and several databases and output the results of statistical analysis in the form of graphs, tables and as HTML, RTF, and PDF documents. SAS is commercial purpose software. It is costly and still beyond the ability for most of the professionals. So, until and unless you are in the company which has in the past bought SAS, it might get hard to access one. To learn SAS in detail, one can join SAS training provider in Noida.

ADVANTAGES OF SAS:

• Simple to learn: SAS has a simple to learn syntax. It can be adapted effectively by one with no programming aptitudes. It can solve very complicated issues that could not be solved earlier.
• Potential to deal with extensive database: SAS has a powerful capacity to deal with extensive database effectively. You can add new possibilities, manage huge volumes of data and make the proper choices.
• Simple to debug: SAS is an extremely understandable language. It can be effectively debugged. Its log window apparently expresses the mistake which can be comprehended and revised. To learn SAS, join Industrial Training in SAS.
• Analyzed algorithms: The algorithm executed in the SAS program is completely analyzed and tested. Each adaptation of SAS is first examined in a controlled domain, before discharged. One can produce a time-sensitive and high-value determination by pulling in-memory analytics.
• SAS Customer assistance: SAS having a place with an organization has legitimate monitoring. It's an entire association. It has exceptionally unconstrained customer assistance. All issues are taken care of SAS customer assistance.

ADVANTAGES OF R:

• R is great for statistical analysis.
• R is also built around a command line, but many people work inside of environments like RStudio or R commander that include a data editor, debugging support, and a window to hold graphics as well. Python has tried to catch up with this with IDEs like Eclipse or Visual Studio.
• R language is considered as the best tool for data visualization. Visualized data can be better understood than raw numbers. R and visualization go hand-in-hand. It includes quite a few packages that correspond with this. Pythons visualizations are a little more convoluted, and there aren’t as many visualization libraries to choose.
• R programming creates excellent results of visualization which can be used in research papers. The results can be traced when required and can be reproduced to create a different result structure.
• R language provides a large community support with 1000 developers and draws talents of data scientists spread across the world. The community includes packages in various domains like finance, machine learning, web technologies, and pharmacy.

CONCLUSION

If you’re a data scientist, you’re as likely to be using R as Python, and almost never using SAS. If you want to do data mining or require difficult graphical outputs, R wins over SAS. However, by large businesses that cannot afford to have disruptions in their financial and marketing data analysis stalled and don’t care about the price tag.