The world of data science is evolving rapidly, and professionals must constantly upskill to stay relevant. Among the many tools available, SAS (Statistical Analysis System) remains a powerful yet somewhat controversial choice. With Python and R dominating the industry, many wonder: Should you learn SAS for data science?
If you’re considering a career in data analytics or looking to enhance your data science service skills, understanding SAS’s role can help you make an informed decision. This article explores the advantages, limitations, and industry relevance of SAS in today’s data-driven world.
What is SAS?
SAS (Statistical Analysis System) is a software suite developed for advanced analytics, business intelligence, data management, and predictive modeling. It has been a leader in the analytics space for decades, used primarily by large enterprises and government organizations.
Unlike open-source languages like Python or R, SAS is a proprietary tool, meaning users need a paid license to access its full functionalities. Despite this, many organizations still rely on SAS for their data science service needs due to its robustness and reliability.
Why is SAS Used in Data Science?
SAS has been a mainstay in data analytics and data science for several reasons:
1. Strong Enterprise Adoption
Many Fortune 500 companies, banks, and healthcare institutions still rely on SAS for data science services. Organizations that require highly secure and validated statistical models often prefer SAS over open-source alternatives.
2. Powerful Data Handling Capabilities
SAS excels in managing large datasets, making it ideal for industries that work with vast amounts of structured data, such as finance, healthcare, and government agencies.
3. Excellent Support & Documentation
Since SAS is a paid software, it comes with dedicated customer support, regular updates, and extensive documentation, which can be beneficial for organizations that need stability and reliability.
4. Advanced Statistical Modeling
SAS is known for its advanced statistical and predictive modeling capabilities, making it a preferred choice for risk assessment, fraud detection, and regulatory compliance.
Advantages of Learning SAS for Data Science
If you’re considering whether to invest time in learning SAS, here are some key advantages:
1. High Demand in Certain Industries
Many financial institutions, pharmaceutical companies, and government organizations still require professionals skilled in SAS for their data science services.
2. Better Job Opportunities in Regulated Sectors
Industries like healthcare and banking have stringent regulatory requirements. Since SAS is widely used for compliance-related analytics, knowing SAS can open doors in these sectors.
3. Stability & Reliability
SAS provides a controlled environment with fewer risks of unexpected bugs compared to open-source alternatives like Python. This makes it a safer choice for critical applications.
4. Strong Career Growth for SAS Specialists
SAS professionals, especially those in financial analytics, fraud detection, and regulatory reporting, can earn competitive salaries due to the niche demand for their skills.
Disadvantages of Learning SAS for Data Science
While SAS has its benefits, it also comes with some challenges that might make you reconsider:
1. High Licensing Costs
Unlike Python and R, SAS is not free. The licensing cost can be a significant barrier, especially for individual learners and startups.
2. Limited Community Support
While SAS has excellent customer support, it lacks the vast open-source community that Python and R offer. This means fewer forums, third-party libraries, and open-source contributions.
3. Less Popular in Startups & Tech Firms
Most modern startups and tech companies prefer Python or R for their data science services due to their flexibility and cost-effectiveness. SAS is rarely used outside of large enterprises.
4. Slower Innovation Compared to Open-Source Alternatives
Python and R are constantly evolving, with new libraries and frameworks being developed at a rapid pace. SAS, being a proprietary software, often lags in adopting cutting-edge innovations.
SAS vs. Python vs. R: Which One Should You Learn?
| Feature | SAS | Python | R |
| Cost | Expensive (paid license required) | Free & open-source | Free & open-source |
| Ease of Learning | Moderate (GUI + scripting) | Easy to moderate | Moderate to difficult |
| Industry Use | Enterprise, banking, healthcare | Tech firms, startups, research | Academia, research, statistics |
| Community Support | Limited | Large & active | Large but niche |
| Flexibility | Limited (proprietary) | Highly flexible | Flexible but mostly statistical |
If you aim for a career in enterprise analytics, SAS could be a valuable skill. However, for broader opportunities in data science services, Python or R might be better choices.Should You Learn SAS for Data Science in 2025?
The decision to learn SAS depends on your career goals:
- If you want to work in finance, healthcare, or government analytics, SAS is still a relevant and valuable skill.
- If you’re targeting startups, tech companies, or research roles, Python or R would be more beneficial.
- If you already know Python or R, learning SAS can enhance your skillset and make you more versatile in the job market.
FAQs
Is SAS better than Python for data science?
SAS is better for enterprise-level analytics in regulated industries, but Python is more versatile and widely used in modern data science roles.
Do companies still use SAS for data science?
Yes, many large corporations, especially in banking, healthcare, and government, still use SAS for their analytics needs.
Can I get a job with just SAS knowledge?
While some jobs specifically require SAS expertise, knowing Python or R will significantly expand your job opportunities.
Is SAS difficult to learn?
SAS is easier to learn compared to Python or R due to its GUI-based approach, but its scripting language can take time to master.
What is the future of SAS in data science?
SAS is expected to remain relevant in regulated industries, but Python and R will likely continue dominating general data science applications.
Should I learn SAS if I already know Python?
If you aim to work in industries that use SAS, learning it can be beneficial. Otherwise, focusing on Python is usually sufficient.
Conclusion
SAS remains a powerful tool in the data science industry, particularly for enterprise analytics and regulated sectors. However, with the increasing dominance of open-source languages like Python and R, its relevance is becoming more niche.
If your career path aligns with industries that require SAS, learning it can be a smart investment. But if you’re looking for flexibility, job opportunities, and cutting-edge innovation, Python and R are the better choices for data science services.
