Python and Java get the most attention when it comes to the languages a data analyst or data scientist should learn, but it’s a mistake. SAS is a player and leader in analytics software for so long. In this article, we will be looking for reasons to learn SAS for analytics today.
Reason 1: If you are interested in health analytics. SAS thrives in highly regulated industries. It is therefore not surprising that SAS is very popular for health analysis, especially when considering clinical settings. SAS is widely used for the analysis of clinical trial data in pharmaceutical and clinical research. Organizations have integrated AI, machine learning, and image analytics that enable streaming data analysis from the Internet of Medical Things, or IOMT, for both research and real-time analysis. SAS has several different tools suitable for special medical and health. One of the main arguments people bring up again is SAS, it’s not open source like Python or others. Open-source languages and tools have great appeal for democratizing data. This makes the data easily accessible to everyone. Anyone can learn to use them and implement them for business solutions at a relatively low cost. Depending on the app, even for free. However, in the case of healthcare, we have the advantage that they are not open source, that they have so many resources that they are dedicated to developing their healthcare modules, their tools for healthcare, and making sure the tools are doing a good job of dealing with healthcare and medicines and they offer overseas support that caters to 2 continents that operate in a highly regulated industry that has massive repercussions when they get it wrong, not only for their own business but also in terms of how it could affect others. And in terms of any government sanctions, they might face. SAS has spent decades and millions of dollars investing in the infrastructure, stability, and interconnection of its systems. Combine that with consulting and working with healthcare companies over the last 40-50 years and you have a pretty robust healthcare analytics solution.
The second reason to learn SAS is if you are going into finance, especially banking or insurance. I think it’s important to highlight the 2 areas in which SAS excels. And like healthcare, the financial industry is highly regulated, and there’s usually a lot of money. A lot of money to invest in these analytics solutions. It is therefore not surprising that SAS also plays a prominent role in the field of bank and insurance financing. Just like in healthcare, they process large volumes. When it comes to very heavy data analytics, SAS is currently the dominant player in these industries. The reasons to learn SAS go beyond these two industries. So, let’s look at other reasons why they are independent of the industry. This brings us to our third reason to learn SAS, which allows for very high-level programming.
They have dozens of different tools and apps suitable for different settings. The different business needs and tools also vary with the amount of programming you need to do. Most organizations use a variety of these tools for users, some analysts use them exclusively for coding from scratch, as you would if you were working in Python. Analysts also use tools that are more low-code slash, code-savvy, and tools like SAS Enterprise Guide or SAS Viya that allow you to achieve the same result with very little coding.
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