Brunet info Solutions provides some statistical tools to handle the tonnes of data generated each day. Structuring and analyzing the data is the main task, followed by strategic decision making. It can be directly said that the cause and excess of data, led to the efficient generation of data analysis tools like SAS.
SAS is a command-driven statistical software suite widely used for statistical data analysis and visualization. SAS full form is Statistical Analysis Software. It allows you to use qualitative techniques and processes which help you to enhance employee productivity and business profits. SAS is also used for advanced analytics like business intelligence, crime investigation, and predictive analysis. SAS is pronounced as “SaaS.”
SAS Programming Language is defined as the science of data-driven decision making. Technology can bring us from raw data to structured readable data; it even predicts many solutions to a problem.
But it is ultimately the call of humans to make the final decision. It uses the repetitive and procedural exploration of past data to handle business decisions.
Let’s understand this with an example, have you ever wondered, why is a billing point at the mall, loaded with gum and candies on its sides.
This is not an unplanned move, rather a strategically made decision. Let’s see how, for instance when parents are at the billing counter with their kids, waiting for their turn. The gums and candies lure the kids and they demand of having it.
SAS Programming as a tool is very helpful for analytics. SAS is software which works in three simple processes. It gets data from various sources, cleans it and processes it. SAS programming language is one of the easiest procedural languages.
It has a simple syntax and various inbuilt libraries containing enormous features. For instance, it has features like plotting graphs, finding permutations and combinations and solving regression.
In the field of analytics, SAS major application lies in observing trends, decipher patterns and provide statistical inferences. In the domain of data management, it mainly contributes to a listing, characterizing, sorting and filtering data.
Its application in the multivariate analysis includes factor analysis, preference analysis and other various kinds of analysis. The crux of using SAS is to analyze data, on which decisions can be made strategically.