SAS vs. R for Artificial Intelligence

Written by Coursera Staff • Updated on

Learn more about SAS, the statistical software system, as well as the programming language R for artificial intelligence.

[Featured Image] Two programmers sit at a laptop and discuss whether to use SAS or R for their next project.

Artificial intelligence (AI) is already impacting today's world, and its importance will continue growing as the technology evolves. Although some may have concerns about AI replacing the need for humans to perform specific jobs, new work opportunities that don’t currently exist will also emerge.

By equipping yourself with the necessary skills, you can look forward to being part of this new and exciting field. Several industries are currently benefiting from artificial intelligence, including business intelligence, health care, manufacturing, education, finance, retail, and more. 

If pursuing a career in artificial intelligence interests you, developing your skills with the SAS statistical software system and programming languages such as R can help you find your next job opportunity. 

In this article, you will learn what SAS and R are, the pros and cons of each, and which is right for you.

What is SAS?

SAS, short for statistical analysis system, is a collection of several different integrated software products for analyzing data, performing statistical analysis, and much more. SAS is what’s known as a fourth-generation programming language. Fourth-generation programming languages differ from other programming languages, such as C, by requiring fewer commands to perform tasks, making them relatively simpler to use.

Some of the well-known companies across different fields that utilize SAS are Nestlé, Office Depot, the World Wildlife Fund, Lockheed Martin, and the Orlando Magic. According to SAS, a massive 90 percent of Fortune 100 companies or their affiliates have used SAS over the past five years [1].

SAS for AI

SAS offers software solutions specifically for artificial intelligence. For machine learning and deep learning applications, SAS has tools to help you recognize the variables in your models that are impactful, creating visualizations to better learn about the insights found in your data. With SAS, you can also create synthetic data to use for training deep learning models. 

Another area of artificial intelligence SAS provides is natural language processing. By incorporating deep learning, machine learning, and natural language processing, SAS allows you to find valuable information from text data by automatically detecting relationships and presenting the findings in interactive reports and visualizations. 

Some of the other useful AI solutions you can experience with SAS include computer vision and predictive forecasting. Industry applications of SAS AI solutions include banking, life sciences, insurance, retail, health care, education, and manufacturing, along with others. 

Pros and cons of SAS

SAS comes with plenty of benefits. Not only does SAS likely have software solutions for you regardless of the industry you work in, but SAS also provides an environment where your data is safe. Additionally, SAS users benefit from its ease of use in comparison to common programming languages, with the help of a user-friendly interface.

SAS does have some downsides as well. Depending on the budget of your organization, SAS may not be affordable, whereas R is a free, open source option. Additionally, SAS doesn’t offer the same level of customizability as R as far as data visualization tools go.

What is R?

This programming language specializes in statistical functions and data analysis. R is also popular for its ability to create visualizations, with specific tools designed to help you do so. You can access R on operating systems such as macOS, Linux, and Microsoft Windows as an open-source programming language. Another way of using R is through the integrated development environment RStudio. 

The industries where you can find professionals programming with R include manufacturing, health care, retail, finance, and education. Some of the positions where R programming is a common skill are data scientists, data analysts, and statisticians. However, the popularity of R as a tool for artificial intelligence is growing, with specific features designed to help develop applications in this field.

R for AI

R has packages and libraries that you can use in order to create AI applications in areas like natural language processing, computer vision, deep learning, and machine learning models, namely predictive modeling. For natural language processing, you can use R to classify, summarize, and cluster text. When working on computer vision projects, R is effective in analyzing images to detect specific objects and classify them as well. 

Some of the packages you can use in R for artificial intelligence tasks include Tidyverse for graphing data, iml for analyzing machine learning models, H2O for developing machine learning algorithms, and vip for creating visualizations. 

Pros and cons of R

R has several features that make it an excellent option for your next AI project. As an open-source programming language, you can use R for free. Additionally, the packages in R are helpful during the development process, and you can use R for many different tasks, including creating unique visualizations. 

However, one of the main challenges with R is the learning process. If you have limited experience with the language, you will need to gain familiarity before using R for AI applications. Furthermore, when performing highly complex AI tasks that are computationally heavy, R may not be the best option. 

Developing skills in SAS vs. R

You can demonstrate your skills in SAS to employers by earning an SAS Certified Professional: Artificial Intelligence and Machine Learning certification. To earn this certification, you first must complete three exams. These exams cover machine learning, forecasting, and optimization, along with computer vision and natural language processing.

To develop your programming skills in R, you have several options, including bootcamps and online courses, some of which can be found on Coursera. R bootcamps cover key concepts, such as creating visualizations and statistical modeling, and they will help you learn more about using R libraries. Bootcamps are an effective way to gain the necessary skills for a career quickly, generally within three to six months. 

Getting started with Coursera

Whether you have an interest in SAS versus R, you can find highly-rated courses to learn more on Coursera. For example, the SAS course Getting Started with SAS Programming is an introductory course that teaches more about preparing and analyzing data with SAS before moving on to more advanced SAS courses in areas such as artificial intelligence and machine learning.

Similarly, Data Science: Foundations Using R Specialization offered by Johns Hopkins University can help you learn more about using R to clean and analyze data and create visualizations. This course also covers how to obtain data and can help you gain skills relating to machine learning.

Article sources

  1. SAS. “See Who’s Working Smarter with SAS,” Accessed March 19, 2024. 

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