Now a day’s analytics market has grown at a double speed. Today, the analytics software market is having numerous players and ranging from billion-dollar companies to a single person operated shops and businesses offering sophisticated and custom-based solutions to various communities as per their requirements. The analytics field is continuously growing, new tools and technologies have started coming into the market, causing the business analysts to master them for better career options.
Today, the number of analytical tools are available in the market, but the major competition is observed between R and SAS.
Before diving into the main agenda of this article, let us first understand what R, SAS is?
R is the lingua franca of statistics. It is a procedural language that depends on a series of sequential subroutines. R is open source and cost-effective approach and the highly preferred programming language among many data scientists. With its built-in packages and library functions. R is the most preferable language in the case of data and plot visualization which are crucial in the context of data analysis.
Statistical Analysis System (SAS) is a premium software suite introduced to explore large datasets in a visually good-looking format and the undisputed market leader in the enterprise analytics space. It is having features of good GUI, an array of statistical functions, along with a full-fledged technical support.
1. Availability / Cost
2. Data handling and management
3. Graphical capabilities
4. Big Data Applications
5. Deep Learning Support
6. Learning curve
7. Career Opportunities
8. Customer service support and Community
R: R and Python both are open source tools and accessed for free of cost and having a number of open community forums and support development teams. Which makes R and Python widely popular among many startups and well-established company.
SAS: SAS is an expensive commercial software available in the market. However, it occupies the highest market share in Private Organizations. SAS has brought a University Edition that is free to access but it has some limitations.
R: R computes everything in RAM (Random access memory), which makes it difficult to run even a small task.
SAS: It is having a good user-friendly GUI for data management but falls short of the capabilities for data handling and managing.
They no longer have differentiation in data management. Hadoop and Spark Integrations are added and supporting Cloudera and Apache Pig.
R: It steals the limelight for making data visualization and graphics more appealing with packages like RGIS, Lattice, GGPlot, etc.
SAS: Working with large data sets SAS is preferable.
R: R integrates well with Hadoop, they lack the feasibility required for machine learning analytics.
SAS: SAS also integrates easily with Hadoop but it lacks in features in machine learning analytics.
R: R has recently added support for kerasR and keras packages. In R act as an interface to the original Python package and Keras
SAS: Deep learning in SAS is still in beginning phase, the SAS technical team are working on it.
R: It’s a low-level programming language designed to perform data analysis with more user-friendly behavior.
SAS: SAS may easy to learn but it does not have good work on programming era.
R: R is highly famous across startups and middle-level organizations and MNCs. Which shows a better opportunity.
SAS: SAS still the market leader in corporate jobs. Most of the big organization are still work on SAS.
R: R is not having customer service support but having many online communities. So, in R if you have any problem you will solver on your own or you take a help of online community people.
SAS: In the case of SAS, having two options like customer service support and online communities if anyone having any problem with installation they can reach out to the customer service as well as communities.
|Ease of learning||Good||Preferable|
|Data handling capacities||Preferable||Preferable|
|Advancement in tools||Preferable||Preferable|
|Customer Service Support & community||Good||More Preferable|
|Deep Learning Support||Good||Not applicable|
|Big communities who creates libraries||High adoption rate in major industries|
|Free to use||Flow based interface with drag and drop|
|Early Adopter in Explanatory and Predictive modeling||Official Support|
|Easy to connect to data sources, including no SQL and web scraping||Handling large Datasets|
|Can be slow with big Datasets||Relatively high cost|
|Steep learning Curve||For nonstandard options not in interface, we have to write the code|
|No official support||Slow adopting to new technology|
|No user interface||Different programs for visualisation|
To Sustain in analytic market mandatory to expertise in the high-level coding and programming. With their open community forums and regular updates R and Python trending all the business in the market.
If you are experienced in analytics, already spent time in the industry, you should try and diversify your expertise be learning a new tool.
If you want to an expert in the analytical field you should know any two-analytical technology.
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