Technology

Types of SAS Applications

With the increase in the availability of data and the need to convert it into analyzed information, there is a need for software that can do it in a way that’s easy to comprehend and learn. If you are into data science and related fields or want to improve on the SAS programming language, then you should get enrolled in a good SAS course.

What is SAS?

Suppose you rely on share market graphs to buy, sell or hold onto your latest bought stock. A rational decision to the above-stated scenario can result in a profitable outcome.

Statistical Analysis System (SAS) is a software suite developed by SAS Institute. It is a tool that plays a crucial role in modern-day statistical analysis and decision-making, and hence you should consider taking a SAS course.

Why should you learn SAS?

Compared to other languages, SAS holds 70% of the market share. R has a 15% share, and Python for data analytics stands at 10%. Certain features are:

  • Presentable and user-friendly GUI.
  • Easy to learn compared to Python or R.
  • Concise code.
  • Always up to date.
  • Cross-platform.
  • Integration makes it flexible and available.

Why are the types of SAS applications?

SAS provides methods using which complex data can be processed to bring about meaningful information, which firms can further use to take rational and predictive decisions.

There are broadly five categories into which the applications of SAS can be divided:

 

  • Advanced Analytics

 

The term advanced analytics shelters a group of high-level methods and tools that can be used to forecast trends, events, and behaviors using available data. This gives an organization a chance to make decisions prior to the happening of some event, keeping in mind the “what-if” scenario.

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Certain components of advanced analytics include data mining and big data analytics.

 

  • Multivariate Analysis

 

When there is more than one factor affecting a single outcome, multivariate analysis comes into play. Consider a retailer who has to buy from a wholesaler who must consider factors like price, quantity, margin, etc. This method will detect and analyze various statistical variables to predict the output.

 

  • Business Intelligence

 

Strategies used for business information using historical, current, and predictive views of a firm group under business intelligence. The work includes data mining, event processing, reporting, benchmarking, etc.

Consider enrolling in this SAS course to understand more.

 

  • Predictive Analytics

 

Predictive analysis uses prior available data to predict the outcome of a certain scenario for making profitable decisions. Various statistical techniques can be used to draw inferences to make predictions.

Consider a company that has a product that has been selling successfully for years. The release of a new version of the product will require an analysis using previous changelogs of the same product.

 

  • Creating Safe Drugs & Clinical Research and Forecasting

 

SAS has helped take critical decisions in the field of medical diagnosis and prognosis. They require to be accurate to analyze if a patient is at risk or not. The tool has also helped in finding if there is any chance that the medical condition will improve.

Conclusion

With an exponential increase in the use of data science and its tools, it is evident that the future will rely a lot on data analytics. It is recommended that you take a SAS course to learn and enhance your skills and be ready for the industry.

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