SAS vs. SQL: What’s the Difference?

Want to become a data analyst? Not sure which data analysis language to start with? This article will help you choose between SAS and SQL – two of the most popular tools.

Are you looking into a career in data analysis? You will probably encounter names like SQL and SAS in job advertisements and job descriptions. The tools may seem similar at first glance, but they actually serve different purposes. In this article, we will explore the key features of SAS and SQL as well as their differences. We will help you decide which one to learn for your next data analysis job!

If you are just starting out as a data analyst and would like to know what to learn next, take a look at our roadmap to becoming a data analyst. If you are confident in your skills, check out our article on building a data analyst portfolio.

And if you decide to take on SQL after reading this article, our SQL for Data Analysis track has more than 450 interactive exercises designed to prepare you for real-world data analysis tasks!

What Is SAS?

SAS (Statistical Analysis System) is a statistical software suite developed by the SAS Institute. It is used for analyzing data, making reports, and creating predictions. It includes its own programming language, also called SAS, that’s designed for statistical analysis.

The SAS programming language is widely used for advanced data analysis, statistical analysis, and Business Intelligence. It is also used for data visualization and prediction. Finally, it is commonly integrated with programming languages like Python and R as well as with popular database management systems.

What Is SQL?

SQL (Structured Query Language) is a programming language designed to manage databases. A database is a collection of organized data that is stored on a computer. A database can store customer details for an online store, transaction histories for a bank, or student records for a university – among many other things.

SQL is used to view the data in databases and to manage and update that data. It’s also used to create and change the structure of tables in a database. SQL has an English-like syntax which makes it very easy to understand and learn. It’s currently the most popular query language for processing large amounts of data, and is closely integrated with other programming languages through various libraries.

Finally, it’s worth noting that SQL comes in many dialects. These dialects are designed to work with specific database management systems (DBMSs); popular DBMSs include MySQL, Oracle, and SQLite. However, most differences between SQL dialects are minor and can be easily resolved with a look into that DBMS’ documentation.

If you are going to choose to learn SQL for your data analyst work, keep our SQL for Data Analysis Cheat Sheet around. It provides a quick reference to common SQL commands.

SQL vs. SAS: Which One Should You Choose?

What should you learn, SQL or SAS? It mostly depends on your goals and what you want to accomplish.

SQL is widely used in fields that heavily rely on the data itself, like tech, media, accounting, and software. Consider learning SQL if you want to be able to design databases and operate on large amounts of data.

SAS is used primarily in fields that rely on advanced data analysis, like healthcare, security, manufacturing, finance, and some government systems. You should learn SAS if you plan on using data to find complicated patterns and predict future outcomes.

Take a look at this case-by-case comparison between SQL and SAS:

SAS vs. SQL: The Conclusion

SQL and SAS may seem similar at first, but in this article we have uncovered the differences in approaches and use cases for these languages.

You should learn SQL if you want to work with data in industries like business, retail, or technology. It’s a powerful tool for organizing and analyzing data to help companies make better decisions. SQL is used to create reports, build dashboards, and answer important questions like which products are selling the most or how customer preferences are changing. It’s also a key skill if you want to work with tools that visualize data, such as Power BI or Tableau.

You should learn SAS if you’re interested in working in industries like healthcare, finance, or government, where analyzing data and making predictions are crucial. SAS is especially useful for tasks like finding patterns in data, predicting future trends, or creating reports in adherence with strict regulations. It’s a great tool for working with large, sensitive data, such as patient records or financial transactions, in industries that need to follow specific rules and standards.

Before we close, I’d like to encourage you to check out our SQL for Data Analysis track with 450+ interactive exercises. It will teach you what you need to know to successfully analyze data with SQL. Happy learning!