Faisalabad Pakistan, 5 Sep 2021, ZEXPRWIRE, The data scientists use several tools such as R, Python, SQL for analyzing, cleaning, and accessing data along with the constructive predictive models. However, some other employers need industry-specific analytical tools such as SAS. These industries include healthcare sectors, including the pharmaceutical stream, and require the proficiency offered by SAS.
What is SAS?
SAS – an acronym for statistical analytics software is an essential tool for analyzing statistical data. SAS aims at retrieving, reporting, and analyzing statistical information. SAS performs as a powerful tool to run SQL queries and also automates the queries through macros. It is to be noted that every statement in the SAS environment must end with a semicolon to avoid any error.
SAS also offers descriptive visualization with the help of graphs and reporting of time series, data mining, machine learning, and many more. The two types of statements supported by SAS are procedures and data steps. SAS comprises several essential elements and an array of user options: discovery, visualization, analytics, business solutions, graphics and reporting, management, and data access. The users can get a free demo of the software or even a university version to download for free. Thus, SAS is an excellent alternative to the open-source programming language.
History of SAS
SAS has its roots in North Carolina State University from the year 1960 to 1970. The software was created basically to analyze agricultural data. Though its evolution was slower than other tools such as Python and R, once picked up, it gave tremendous results and revenue. SAS provides users with a lot of attributes and products more than 200 including:
- Decision Management Solutions
- Customer Intelligence Solutions
- Business Rules manager
- Analytics for IoT
- Asset Performance Analytics
How is SAS different from R and Python?
SAS performs with its programming language that is similar to SQL. It also deploys a graphical user interface. The GUI for R and Python is primarily optional, but for SAS, it is built-in. Even if the capabilities of SAS are not equal to R and Python, they are similar to them. Open source programming languages such as R and Python are less costly and have several benefits associated. They also incorporate data techniques such as deep learning, AI, and machine learning easily and quickly. SAS is also similar to open-source languages and possesses an extended latency period for development.
SAS’s unique and most favorable feature is a dedicated support team always available to solve all the user queries. R and Python have community support mechanisms such as Reddit, Stack Overflow, Quora, and many more. However, the quality and tone of the responses are better with SAS. With innovative business functioning, the requirement of SAS has rapidly increased.
The step by step guide for SAS programming is mentioned below:
- Import SAS data
Data from the excel file can be imported to the SAS environment through the PROC import function. SAS also has built-in libraries where the data set is stored and accessed as and when required. Temporary data lasts for only the current SAS session and gets deleted at the end of the session. Permanent data gets stored for life long and can be accessed as required.
- Statistical descriptive Analysis
There are numerous functions such as missing value PROC Mean and many more to analyze, retrieve and report the data. The missing value function is used for identifying the missing data. PROC Mean function is used to find the average or mean of any given data. PROC Freq is another unique function that lets the user calculate the frequency distribution of the desired data in the data set. This function helps in finding the distribution of values with the help of tables. Co-relation between two variables X and Y are calculated with the PROC CORR function.
- Graphs and Visualization
There are various graphic functions available in SAS that let the users report the data in a particular way. These include bar charts, histograms, scatter plots, box plots, and others. Bar charts are used to represent the data categorically. The distribution of continuous values is expressed through an appropriately prepared histogram. When the length values are skewed, then it depicts that the information is not distributed evenly. When two variables are plotted through a graph, their relationship is represented through a scatter plot. The strong correlation between the two variables is depicted through a strong positive upward trend. The continuous variable is depicted categorically through a graph in a particular way called a Box Plot. The whisker and the box chart show minimum and maximum values and interquartile range and outliers.
Where can I learn SAS?
SAS is not as popular as its statistical analytics cousin R and Python. The software development is done with Python. It is not merely a statistical analysis tool but has broader application possibilities. R is mainly designed for implementing advanced and basic statistical techniques. R enthusiasts have also started using a programming language for taking a plunge in software engineering. Though there are very few resources for learning SAS, there is ample knowledge of it.
SAS can be learned from the following sources:
YouTube offers the best online analytics courses and tutorial channels from the SAS institute. The learners can gain a lot of knowledge from these SAS pools directly and at their convenience and leisure. YouTube is an ocean of knowledge, and you gain from learning all over the world. Some of the channels are paid but at a reasonable price.
There are some free e-learning data courses also provided by SAS. These include Analytics, business intelligence, data management, SAS programming, and SAS Hadoop. These courses comprise all the aspects of SAS and programming languages as required.
- Great Learning
You can learn Data Science from Great Learning, which provides a wide variety of courses related to SAS. This platform offers Data Science certificates from renowned universities in the world and global experts. The courses are designed accordingly so that they will benefit fresh graduates as well as working professionals.
Disrupting innovation and prediction is a confounding variable while determining how the industry gets evolved. And therefore, data science tools are rightly termed as the evolutionary trajectory of technology. SAS has been building a concept spanning more than 40 years, and consequently, it is assumed it would not disintegrate into defunct technology. Data scientists have to be quick in learning and keep themselves updated with the latest shiny tools. These tools are essential for making data science learning efficient and more manageable. Learning SAS has been highly encouraged to make a career in data science. If a particular job requires some specific qualification that can also be adhered to with SAS learning.