4 Reasons to Learn SAS for Analytics

4 reasons to learn SAS for analytics

Python, R, and SQL get most of the attention when it comes to languages a data analyst or data scientist should learn. But it would be a mistake to count out SAS - long a player and leader in analytics solutions. 

Let’s look at 4 reasons to learn SAS for analytics.

1 - Healthcare Analytics

SAS thrives in industries that are highly regulated.

It shouldn’t be much of a surprise then that SAS is extremely popular for healthcare analytics. 

SAS is extensively used in clinical trial data analysis in the pharmaceutical and clinical research companies. They have embedded artificial intelligence (AI), image analytics and machine learning that allow in-stream analysis of data from the Internet of Medical Things (IoMT). 

For both research and real-time decision making, SAS has a great suite of tools for healthcare.

One of the main arguments against using SAS is that it’s not open source like Python or R. Open source languages carry a lot of appeal in the democratization of data. That is - anyone can learn to use them and implement business solutions with them limited/no cost.

However, in the case of healthcare, this actually gives SAS an advantage.

They’ve spent decades and millions of dollars investing in building a very stable, accurate, and reliable infrastructure. Connect with this collaboration with the healthcare sector from the 1970s until now and they’ve been able to build very robust tools and solutions.

2 - Finance Analytics

Similar to healthcare, the financial industry also faces heavy regulation which plays to SAS’s strengths.

Banking and insurance analytics are dominated by SAS. As with healthcare, data is extremely sensitive.

While SAS dominates banking, insurance, and healthcare, it doesn’t mean they’re the ONLY programming language being used for data analysis in these industries. Python, R, and other languages also have their place, but they aren’t as widely used for data analytics and data science. 

The reasons to learn SAS for analytics extends beyond specific industries though, so let’s get into some other reasons: 

3 - Varied levels of programming

I talk about SAS like it’s a single thing, just the language, but SAS offers a massive selection of tools. Most organizations that utilize SAS are using a variety of these solutions. 

Some analysts that use SAS almost exclusively code their solutions from scratch in the same way you would other languages.

SAS also has tools with great GUIs. That is, graphical user interfaces, which are easy to use for a more drag and drop approach.

If you’re not sure what a GUI interface is, think of Tableau or Power BI where your main interaction is dragging/clicking to make selections vs a non-GUI interface where you need to type out your commands. 

Depending on the tool, you can also use a mix of both and easily set up interactive reports or dashboards for other users. 

4 - Next Step from SQL

If you know SQL and are looking to add an additional language, SAS is a good next move.

Using the procedure PROC SQL within SAS lets you use SQL statements. So if you know SQL then you can already do some basic things within SAS. 

This can make for an easier transition to learning SAS since you’ll at least be able to do some familiar things without completely starting from scratch.

How to Become a Business Analyst

If you’re thinking about getting into analytics or just want to build your skills to complement your current job, check out my guide How to Become a Data Analyst.

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