SAS for Data Science – Learn how SAS benefits Data Scientists
Data Science has emerged out as the most successful technology in the world today. There are various tools that a data scientist must use in order to analyze data. While there are much popular open-source tools like R and Python, there are also other software tools like SAS that facilitate data science functionalities in a closed-source way. In this article, we will explore how data scientists use SAS for their data science operations.
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What is SAS?
SAS abbreviated for Statistical Analysis System is a software developed by SAS Institute that is mostly used for statistical modeling. In statistical modeling, SAS is used for business intelligence, data management, predictive analytics, and multivariate analytics. SAS was initially released in 1976 after its development at the North Carolina State University. It was conceived as a rival platform to IBM’s SPSS and quickly grew into becoming one of the best software tools for statistical modeling.
For a long time, SAS has been the undisputed champion of the analytics market. With SAS, you can use a wide range of analytical software tools that will allow you to mine data, update, extract and manage data from various sources. You can then apply statistical analysis after gathering and processing the data.
SAS allows you to access the data in any format. That is, the format can be in the form of SAS tables or Excel Worksheets. With SAS, you can manipulate and manage your data to obtain the essentials. You can also create subsets of data to merge it with other data and create more columns. SAS also allows you to apply statistical techniques to analyze data. These statistical techniques range from descriptive statistics to inferential statistics. Furthermore, with SAS, you can create reports and make it sharable with other users.
A SAS program is a sequence of steps that you submit to SAS for execution. Each step in the program performs a specific task. Only two kinds of steps make up SAS programs: DATA steps and PROC steps. There can be either DATA step or PROC step, or a combination of both the steps. These steps depend on the type of task that you need to carry out.
With a DATA step, you can manage and manipulate your data whereas, with the PROC step, you can analyze your data.
SAS provides you with a programming environment called SAS Studio. There is also a free to use SAS version called SAS University that is for non-commercial and academic usage. Furthermore, SAS allows you to connect with SAS server that processes commands on a cloud level.
There are more than 200 components of SAS, some of the important components are –
- Base SAS – Basic version of SAS for data management.
- SAS/STAT – SAS used for statistical analysis.
- SAS/INSIGHT – SAS used for data-mining.
- SAS EBI – SAS for business intelligence applications.
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Applications of SAS
Here are some of the top applications of SAS –
1. Business Intelligence
Business Intelligence refers to the analysis of the information related to business information. SAS allows various business forecasting tools, services and predictive technologies for you to develop insights about the program. Furthermore, the insights generated from business intelligence help in decision making by the companies.
2. Predictive Analytics
Predictive analytics is the prediction of future events using historical data. With predictive analytics, one can draw several inferences through statistical techniques. An example of predictive analytics is in the field of sales, where SAS allows you to develop predictions for future based on the past sales record.
3. Multivariate Analysis
Multivariate analysis is an extension of simple analysis that makes use of several variables. For example, a sales company may look into various factors affecting the sales like location, customer salary, age etc. SAS allows you to have a robust analysis of multiple variables like these.
4. SAS for improving Services
SAS allows companies to improve and optimize their services. One such example where SAS played an important role in optimizing the services was in the case of Lufthansa Airlines. They used SAS Cost and Profitability Management for improving the services of the ground handlers. This solution provided them with the ability to estimate costs and turnover which further facilitated them to improve their services in this field.
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Importance of SAS for Data Science
It has been the primary choice for analytical processing long before the word “Data Science” was coined. SAS was used by Statisticians who wanted to build powerful statistical models to analyze and build predictive models on top of processed data. Coming to the 21st century, there has been an exponential boom in data. With this, there is an increment in a number of software tools and programming languages that specialize in churning large amount of data.
While many of these tools maybe open-source, a closed-source tool like SAS still hasn’t lost its charm. One main reason for this is the fact that SAS provides you with the stability and security that no other software company provides. SAS provides you with the support and maintenance of their software products. But still, with the emerging technologies of Big Data and artificial intelligence, does SAS still hold its ground?
In order to harness the breakthrough in Artificial Intelligence and Big Data, SAS has launched its own products and tools that specialize in this role. These products specialize in AI & Machine Learning, Customer Intelligence, Risk Management, Fraud & Security Intelligence etc.
Furthermore, SAS provides you with SAS Viya which a comprehensive cloud platform for business analysts, data scientists and executives to collaborate and work on results. Similarly, Honda uses SAS to save costs on service repairs. It also uses the software suite to improve the warranty claims and for forecasting the usage of parts and services.
In these ways, SAS has been growing its share of Artificial Intelligence and Data Science in the market. Furthermore, bigger industries are using SAS for its data security and stability. Due to these reasons, SAS is a very much important tool for Data Science. With the growth in the data science community, there is a shift in the usage of SAS for more open-source tools. However, while these open-source tools may provide you with the knowledge and hands-on approach, you will still require the knowledge of SAS for working in top-tier business intelligence firms.
In the end, we conclude that SAS has been around for many years. Being a closed-source language, it has not been popular in the contemporary scenario. However, SAS is highly stable, reliant and secure. Also, it has been pursuing rapid developments in Artificial Intelligence and Data Science, competing with several other technologies. SAS is still a preferred language by many data scientists and is high in demand in major industries.
Hope the article was helpful to you. If you have any queries, ask in the comment section below. I recommend you to check the prerequisites for Data Science, it will help you to move ahead in Data Science easily.