A Quick Introduction to SAS for Data Science and Its Importance in 2022 

SAS is one of the most well-known data science and analytics software tools. They are primarily known for their specialized statistical analysis and business intelligence software used by corporations worldwide to analyze large data sets. The SAS company claimed that “SAS For Data Science” reflects its commitment to providing tools for data analysis, managing big data and analytics, and visualization. 

Big data seems like a simple term. It’s just the phrase we apply to a concept that humans have dealt with since the beginning of time: large sets of information that are difficult to understand at once. The complexity arises when you try to manage, store and manipulate that information as technology changes. This article covers what SAS is and how SAS tools can be used to collect and organize massive sets of data to answer complex questions. 

What is SAS for data science? 

SAS (Statistical Analysis System) is a data management software package developed by SAS Institute. It is one of the world’s most widely used statistical analysis tools. This powerful software offers a wide range of capabilities that allow you to work with data quickly and easily from any location. The software’s built-in tools provide easy access to data, allowing you to analyze data in a matter of minutes. Its user-friendly interface makes it easy for anyone with minimal computer experience to get started using SAS for data science applications quickly. 

SAS for Data Science is a comprehensive data science platform that helps you develop, manage, and deploy your analytics solutions. It’s designed to help you build more effective business solutions by providing you with a single integrated environment for managing your data science projects and integrating data from across your organization. 

The key features of the SAS platform include: 

  • Data management: including importing and exporting data, creating tables, and performing simple statistical operations 
  • Analytics: including exploratory analysis, forecasting, regression modeling, and neural networks with decision trees 
  • Visualization: including dashboards using QlikView and Tableau; data mining using SAS Enterprise Miner; and web-based analytics using SAS Visual Analytics Platform (VAP) 
  • Integration: including connectivity to other products such as SAS HANA or Microsoft Azure

Learning SAS for data science can be a tough road. Despite the abundance of information available, they all appear to contradict one another. You can check out Learnbay’s data science course, which is designed in collaboration with IBM. This course will teach you how to use SAS for data science. At Learnbay, you will also learn about other programming languages, such as Python or R so that you can be prepared for whichever language is most needed by your organization when it comes time to use them. 

What makes SAS unique from other programming languages? 

  • The main difference between SAS for Data Science and other software tools used for data management is that SAS for data science allows users to create their own custom analytic reports using self-service BI tools like KPI mapping, dashboards, charts, and graphs using SAS Enterprise Guide (SEG). 
  • Another feature of SAS for data science is integrating external databases with existing ones without installing additional software on your local computer system, making it ideal for enterprise-level organizations. 

Applications of SAS in data science: 

SAS applications are becoming increasingly important in decision-making and statistical analysis. Here are a few examples of how SAS tools are used. 

  1. Predictive Analytics: 

Predictive analytics is simply the study of past events to make accurate predictions about the future. Using SAS, you can make predictions about future sales based on preliminary sales data, which is an example of predictive analytics. Several conclusions can be drawn using statistical approaches in predictive analytics. 

  1. Multivariate Analysis: 

Multiple variables can be analyzed in a robust manner using SAS. It’s an extension of fundamental analysis that incorporates many variables into the equation. For example, a sales organization may look into characteristics such as the location of the client, the customer’s wage, and the customer’s age. 

  1. Business Intelligence (BI): 

Analyzing information connected to a company’s operations and activities is known as Business Intelligence (BI). SAS provides various tools, services, and prediction technologies to help you learn about the program. 

In addition, the insights gained through business intelligence help firms make better decisions.

  1. SAS for Improving Services: 

SAS helps companies enhance and optimize their services. Lufthansa Airlines was an excellent example of a company where SAS significantly impacted service optimization. The ground handlers’ services were improved with the help of SAS Cost and Profitability Management. 

  1. Safe Drug Development, Clinical Research, and Predictions 

Medical diagnosis and prognosis are two of the most critical aspects of the field. They should always be proper and precise in what they say and do. SAS has had a significant impact on the medical industry. It’s a part of clinical decision-making operations. SAS is used to identify if patients are at risk of danger. Doctors also apply it to identify patients with poor predictions for improvement. 

Key Takeaway: 

The main features of the SAS Software are: 

  • A robust programming environment that enables you to write code in any language you choose 
  • A set of pre-built functions called “data sets” make it easy to analyze large amounts of data 
  • A system for managing datasets called “data management” allows you to find, manage and work with your data using standard commands like SQL 
  • Tools for working with large datasets (such as Hadoop) 

By now, you have seen the data science field is expanding, and SAS is well-positioned to take advantage of this growth. While it’s tempting to say that you need to learn SAS in order to compete with data scientists, most companies have already accepted this reality. SAS is a big data information management leader, providing access to real-time business intelligence for decision-makers across industries. While plenty of open source tools are available, SAS remains popular due to its ease of use with the SAS programming language. 

Kickstart Your Journey With Learnbay 

For beginner to intermediate data scientists, it’s a great tool to learn. SAS has a bit of a learning curve, but it is easy to understand for anyone familiar with any programming language. It’s not an easy program to learn, but it’s definitely worth the effort. 

To master SAS, you can check out a data science course in Dubai offered by Learnbay. Learnbay’s data science and AI training equips learners with the domain-focused practical skills to navigate the software’s interface, conduct analytics, and perform statistical analyses. Learners will also be equipped with strategies for effectively using SAS to manage complex datasets and work on various real-time and capstone data science projects. Learnbay also provides mock interviews and placement assistance programs to help students prepare for challenging interviews with top hiring managers.

Guest article written by: Siddharth is an enthusiastic author who writes blogs on the latest technology. He aims to make young people understand the changing world. He wants everyone to be updated so that they can win the race of life. Knowing the importance of the Data Science Course and its demand, his blogs are concentrated mostly on data science and artificial intelligence.

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