Speaker 1: Python, R, and SQL get most of the attention when it comes to languages that a data analyst or data scientist should learn. But it'd be a mistake to count out SAS, long a player and a leader in analytics software. Hi, I'm Jen, an analyst with 15 years of experience. Today we'll look at four reasons to learn SAS for analytics. Reason one, you're interested in healthcare analytics. SAS thrives in industries that are highly regulated. It shouldn't be a surprise then that SAS is very popular for healthcare analytics, especially when we look at clinical settings. SAS is used extensively for clinical trial data analysis within pharmaceutical and clinical research organizations. They have embedded AI, machine learning, and image analysis that allow for in-stream data analysis from the Internet of Medical Things, or IOMT. For both research and real-time analysis, SAS has a suite of different tools that really cater to medical and healthcare specialties. One of the main arguments that people make against SAS is that it's not open-source like Python or R. Open-source languages and tools carry a lot of appeal for the democratization of data, that is, making data easily accessible to anyone. Anyone can learn to use them and implement them for business solutions at relatively low or even no cost, depending on the application. However, in the case of healthcare, this actually gives SAS an advantage, the fact that they're not open-source, that they have so many resources that they've dedicated to developing their healthcare modules, their healthcare tools, and ensuring the tools that they have work well dealing with health and medical issues, and they provide a broad support that really appeals to companies that are in a highly regulated industry that have massive repercussions if they get things wrong, not just for their own business, but also in terms of how that might affect others and in terms of government penalties they may face for failing to comply with different regulations, even if it's just from honest mistakes. SAS has spent decades and millions of dollars investing in the infrastructure, stability, and interconnectedness of their systems. Combine this with consulting with and working with companies in healthcare fields for the past 40 to 50 years, and you end up with a solution that is quite robust when it comes to healthcare analytics. The second reason to learn SAS is if you're going into finance, especially banking or insurance. Not all of the four reasons are going to be different fields that you're going to go into, but I think it's important to highlight the two areas where SAS really excels. And like healthcare, the financial industry is highly regulated, and there tends to be a lot of money involved, a lot of money to be invested in these analytic solutions. So it's no surprise that SAS also is a dominant player when it comes to banking and insurance. Finance, just like with healthcare, we're dealing with large volumes of data that's highly regulated and highly sensitive. So it's not too much of a surprise that we see SAS also dominating here in the financial industry. While SAS dominates the market in healthcare, in banking, in insurance, it certainly doesn't mean they're the only players. There's still plenty of room and use of Python, R, SQL, and all of the different tools that are out there that allow for no-code or low-code analytics. But when it comes to the really heavy data analytics work, SAS really is the dominant player in these industries right now. The reasons to learn SAS extend beyond just these two particular industries, though. So let's look at two reasons that are independent of industry. That brings us to our third reason to learn SAS, and that's that it allows for varied levels of programming. I talk about SAS like it's just one thing, the language. But SAS is so much more than just the SAS programming language. They have dozens of different tools and applications that are tailored to different settings and different business needs. And these tools also vary in how much programming that you need to do. Most organizations that utilize SAS use a variety of these tools. Some analysts use SAS exclusively to code from scratch, just like you would if you were working in Python, for instance. But other analysts, or sometimes even those same analysts, are also using tools that are more on the low-code slash no-code end. Tools like SAS Enterprise Guide or SAS Viya, which let you do a very minimal amount of coding to get to the same end result. SAS also has multiple tools that have great GUIs, or graphical user interfaces. If you're not sure what a GUI is, think of Tableau or Power BI, where your main way of interacting in the application is more click or drag and drop versus typing like you would be coding in a language like Python. Here it's more friendly to the person that doesn't know the coding language, but it still does require you to understand how data interacts with other sorts of data because you still need to know how to make your connections, what analysis to do, and whatnot. It's meant to be a little more intuitive for the person that doesn't know a coding language. However, these tools also let you add code snippets. If you're using a tool like SAS Enterprise Guide, which is on the surface more of a GUI setup where it leans more low-code, you can still add in portions of code to do the analysis, or you could do it completely from code within that interface, but it enables you to do some things a little faster than what writing the code might be based on how people over time have used it and different changes they've made, functions they've added in, capabilities so you can do things more drag and drop, and it generates the code behind the scenes to support that activity that you've called for. The fourth reason to learn SAS is it's a good next step up from SQL. If you know SQL and you want to add an additional language, SAS is a great step. SAS even has a procedure called PROC SQL, which lets you make SQL statements in your code. So if you know SQL, then you can already do some basic things within SAS, like importing certain pieces of data, selecting columns that you want to have in your output, filtering it out. Those things you'll find very easy to do as basic intros to SAS, and then the coding or the part of the tool that you'll still need to learn is the more analytics framework for it. Knowing SQL can make for an easier transition to learning SAS, since instead of starting completely from scratch, you can use those basics you know and then build off of it learning how to do the heavier analytics work within the SAS applications within the SAS language. Those are four reasons to learn SAS for analytics. I'm curious if you're using SAS or if you'd consider using it, learning it, after watching this video. Let me know down in the comments. If you're thinking about getting into an analytics career or just looking to build your analytics skills to supplement your current job and make it a little easier when you're working with data, check out my guide, How to Become a Data Analyst. I'll link that down in the description, along with a variety of other resources that are helpful for anyone that is new to analytics or newer to analytics. Thanks so much for watching. I hope to see you next week.