Introduction
The SAS Institute for Data Management created SAS, an analytical program used for data analytics. SAS was first made available in 1972. (almost 50 years ago). On August 18, 2020, SAS is finished with its stable release. It is possible to write this in C. SAS offers support for several different operating systems, including Windows, the IBM mainframe, Unix/Linux, and OpenVFM Alpha.
What Is SAS In Data Analytics?
Data mining, data administration, data alteration, and data retrieval from several sources are all important tasks that SAS software is capable of performing with data. SAS may give data through SAS Language in a graphical point-and-click manner for non-technical users. SAS has DATA steps and PROC steps, each of which consists of a set of statements to carry out the necessary data operations. These steps are used for data analysis, data modification, and data retrieval.
Data steps include executable instructions for applying software actions to data as well as certain declarative clauses for reading data sets and modifying the appearance of data. The DATA stage consists of two steps. Compilation and execution come first.
In the compilation process, declaration statements are used to check for syntax mistakes. The execution phase involves carrying out the executable stages in order. The data is organized in tabular form as a result of the DATA phase, where rows are referred to as “observants” and columns as “variables.” Every piece of data also has a descriptor and a value.
PROC statements, also known as process statements, are used in PROC stages. PROC performs data analysis, data modification, and graphical, and statistical representation of data. There are over 300 named procedures, and each one performs its own statistical and programming job. Additionally, these PROC statements sort data and display results, among other data operations.
SAS can be used without any programming knowledge. ODS is used to provide the SAS data to the user in HTML, PDF, Excel, RTF, and other forms (Output Delivery System). SAS either automatically executes data operations or generates code to manipulate the data.
SAS’s fundamental elements are:
• Base SAS: The most utilized SAS component. It is a key component that houses the data management tool and the data analysis programming.
• SAS/ GRAPH: This component aids in the creation of graphs and presents the data in a suitable style for easier understanding.
• SAS/SAT: This software implements regression, multivariate, psychometric, and mixed model statistical analysis.
• SAS/OR: It does data-driven operational research.
• SAS/ETS: This component handles time series analysis and econometrics.
• SAS/IML: Based on the data, it generates an interactive matrix.
• SAS/AF: The application facility is provided by this component.
• SAS/QC is a part that offers quality control.
• SAS/ Insight: This aids in data mining; a component by the name of SAS/Enterprise miner also does the same function.
• SAS/PH: This program analyses clinical trial data.
There are primarily 5 different types of SAS software:
PC SAS or Windows SAS
• SAS Enterprise Guide (abbreviated as SAS EG)
• SAS Enterprise Miner, often known as SAS EM, for predictive analysis
SAS Stats and SAS Mean:
Because Linux SAS lacks a graphical user interface, users must create programs for every query. As a result of this complexity, the majority of users choose Windows SAS, which includes five sections and a variety of user-friendly utilities. It’s them,
• Log Window: This is similar to an execution window in that it displays program execution coupled with a log window that displays program errors. While the program is running, it is crucial to look at the log window to ensure appropriate program comprehension.
• Editor Window: This function acts as a notepad for entering query codes.
• Output Window: This window displays the program’s output.
• Result Window: The outputs from running specific programs are collected in the result window. When a certain session is operating, the output from that session is saved in the output window. When switching to another session, the output window displays the output from the current window. By clicking on the output result, this Result Window functions as a directory of all the programs that have already been run. The Result Window will be empty and free of any output results if we close the app and reopen it.
• Explore Window: All of the various windows are displayed here so that you can select one that best suits the program.
SAS uses libraries to store related programs. SAS offers a capability that allows users to construct different libraries.
There are two different kinds of libraries:
- Temporary Library:
Sometimes called the Work Library. It can be found in the SAS software’s Explore window. The Work library by default stores all currently running apps. There is a requirement to assign a permanent library to the program because the work library only lasts as long as the session does if the user hasn’t assigned any permanent libraries to the programs and ends the session after examining the programs again.
- Permanent Library:
A program that has been produced and saved in these libraries will be accessible for as long as the user wants the program to be present and the session is active. By utilizing the utilities and entering text in the editor window, this SAS library can be produced.
Programming SAS:
As previously said, this SAS contains three necessary processes, including the output step.
Data Step, Processing Step, and Output Step
Data Step: In this step, user-provided data is entered into a SAS Dataset to compute data values and assign relevant variables to them.
By processing, comparing, and verifying the data, it verifies the data and creates new datasets.
Syntax:
DATA data_set_name; #Give a name to the dataset
INPUT var1,var2,var3; #Declare variables in the dataset.
NEW_VAR; #Define new variables.
LABEL; #Give variables a label
DATALINES; #Provide data
RUN;
The key noteworthy action in the data stage is to provide the program with a specific RUN statement to continue running.
PROC Step:
It carries out user-defined actions to analyze the data or functions to generate findings and reports to the user.
Syntax:
#The proc’s name; PROC procedure name options;
In the output step, conditional output statements are used to show the user the results or reports.
OPTIONS; RUN; PROC PRINT DATA = data set;
SAS has the following benefits:
• It is simple to manage huge databases;
• It can access any type of data;
• It can be thorough and readily debugged;
• It provides the data in statistical form, which is helpful to non-programmers.
SAS can comprehensively test or analyze algorithms, and even non-programmers can use its simple syntax.
SAS has a few drawbacks:
• Its licensing fees are quite expensive for individuals or businesses.
• Its use is not open source.
• SAS text mining is challenging.
SAS alternatives:
SAS is not widely used in data analytics. They are Python and R.
R:
R is an open-source program that is simple to learn and is similar to SAS in that it offers robust statistical capabilities.
Python:
To do statistical operations, use Python. The MatPlotlib, Scipy, and Numpy libraries are all available for Python. It is simple to analyze the data using these libraries, and in this case, the user can also utilize the libraries to build his model.
Conclusion:
Hence this article provides you the information about the SAS overview, its working, steps, types of SAS, and alternatives of SAS. Hope you like this article the brief about the SAS.
