Introduction to SAS

What is SAS?

SAS (Statistical Analysis System) is a leader in business analytics.

SAS is software that transforms data into insight which can give a fresh perspective on business.

Unlike other BI tools available in the market, it takes an extensive programming approach to data transformation and analysis that makes it stand out from the crowd and finally it gives much finer control over data manipulation.

It is specifically used for :

• Data entry, retrieval, and management
• Report writing and graphics design
• Statistical and mathematical analysis
• Business forecasting and decision support
• Operations research and project management
• Applications development

Why SAS?

Features intrinsic to SAS which makes it a market leader are :

• Data Security/Authenticity: SAS is a closed source so it is completely secured. Data security prevents it from manipulation.
• Handling volumes of Data: SAS has a strong ability to handle large databases very easily.
• Easy to debug: SAS is a very comprehensible language. The process of debugging is easy. We can understand and correct the error that the log window clearly states.
• SAS GUI: SAS is one such language that has made statistical computing easier for non-programming users. It has an amazing Graphical User Interface (GUI) which provides various tools like graphs, plots, and a highly versatile library.

Why SAS is a preferred skill?

Comparing with R and Python

https://mindmajix.com/blogs/images/python-vs-sas-vs-r.png

• Data managing capabilities: In the case of data handling and management, SAS is better, smooth and safe. R comes with a significant disadvantage that it only works on RAM, which is a big problem since little exercises will also take time to run based on your machine’s RAM. Therefore, manipulating data is more natural when it comes to packages such as Plyr and DPlyr. We also face the same in the case of Python. When extensions like Numpy and Pandas compared to others, fundamental analysis and data handling work are similar to Python.
• Simplified coding: SAS is the most comfortable language across all three to learn and can be picked up by anyone without prior programming experience. The ability to parse SQL codes, integrated with macros and other native flavors makes SAS discovering a child’s play for individuals with primary SQL knowledge. Its learning curve is low-to-medium.

Applications of SAS

The image below shows a few applications :

• Multivariate Analysis: For instance, consider a person who wishes to buy stock in bulk. So he or she would weigh various factors like price, quantity, quality, etc. The multivariate analysis does the same thing. It detects and analyses various statistical variables of an outcome at the same time. It uses various studies which depict the effect of variable factors on single result. It includes analysis of factor analysis, bivariate analysis, and multiple regressions.
• Business Intelligence (BI): It refers to strategies and technologies used by any enterprise for data analysis of business information. It provides insights regarding predictive, current and historical views of business working. The analysis of data helps the senior board with scope for decision making. These technologies include reporting, data mining, process mining, complex event processing, benchmarking, etc.
• Predictive Analysis: As the name suggests it uses already available data to predict the future. It uses various statistical techniques to draw inferences. For instance, in a company, the trend in sales of product ‘A’ has been constant over the years. So, it suggests non-changing demand for the product. But for product ‘B’ with changing demand every month, it analyses all the factors causing the variation, hidden inferences in the text, the customer thought process, etc. Here predictive model exploits patterns found in historical data to identify risk.

Components of SAS

Let us take a look at a few important SAS components:

• Base SAS: It is the most widely used component. It has a data management facility. You can do data analysis using Base SAS.
• SAS/GRAPH: With the use SAS/Graph you can represent data as graphs. This makes data visualization easy.
• SAS/STAT: It lets you perform Statistical analysis, such as Variance, Regression, Multivariate, Survival, and Psychometric analysis.
• SAS/ETS: It is suited for Time Series Analysis.

Disadvantages of SAS

Now, let us look at some of the disadvantages of SAS:

• SAS is an expensive tool.
• Algorithms used in SAS procedures are not open to the public
• Less number of graphical capabilities available.
• Compared to its competitors, SAS is still an old software.
• Software like R provides many of the same features free of cost.
• 500 lines of SAS code can be equivalent to 100 lines of R code.
• For doing Time Series Forecasting we need to purchase SAS ETS Module.

I hope you enjoyed reading my post and got an idea of what SAS is. This was my new medium story, if you find any technical or grammatical mistake then you can comment the same and don’t forget to clap.