When choosing a programming environment, one has to consider what particular career aspirations they want to pursue and whether they can afford such an option and the demands of the industry. In this blog, we are going to look at the respective pros and cons of SAS and Python.
SAS: Analytics Tool for the Enterprise
SAS (Statistical Analysis System) is proprietary software created by the SAS Institute for advanced analytics, business intelligence, and data management. It is commonly used in fields such as banking, health, and pharmaceuticals, where information is sensitive and laws of the land are paramount.
Strong Statistical Capabilities: SAS can accomplish any complex statistical analysis.
Easy to Use: SAS has a pretty good GUI and does not intimidate the typical end-users without programming knowledge.
Enterprise Support: With support, professional services, and thorough documentation available from SAS, applications that depend on it are considered critical.
Price: Being proprietary, SAS charges for its use. This would be such a heavy investment for an individual and a small entity.
Flexibility: It is less flexible than open-source ones, particularly in integrating newer technologies.
Bhrighu’s SAS certification equips professionals for analytics careers in regulated industries such as healthcare and finance
Python is an open-source general-purpose computing language with the utmost emphasis on readability, which allows it great flexibility, especially as a scripting language for automating tasks and is heavily used in data analysis, ML, web-building, and system administration.
Accessibility: It is free to use Python; it is an excellent choice, especially if you consider persons or institutions under budgetary concerns.
Great Environment: With libraries such as Pandas, NumPy, and Matplotlib, Python offers an extremely powerful environment for data manipulation, analysis, and visualisation.
Community Support: A huge and supportive community has kept Python alive all these years, assisting learners with heaps of resources to learn.
Learning Curve: Though it is good for those who start with it, the language does require learning programming concepts, which may prove a little tough for someone with a non-technical background.
Lower Specialisation Support: From an enterprise support standpoint, Python relies on community forums and third parties for support.
Beginners and Generalists: Python is a huge starting point for the naive and for those who seek versatility across domains. It's open source and backed by a worldwide community with the biggest libraries practically in every area of domain application.
Industry-Specific Roles: If you are inclined toward jobs that use SAS in domains like healthcare, finance, then the SAS career-oriented pathway is the best option for you.
Extra Edge: Both can be good. The flexibility of Python coupled with the special abilities of SAS, creates an ultimate skill set for data analysis.
One can take a structured SAS course at Bhrighu to improve their skills. To begin, you can look into joining a SAS programming course.
In conclusion, both are quite powerful, and the selection should essentially stem from objective and industry focus, and mode-of-learning considerations. However, according to the KDnuggets annual survey, Python ranks as the most preferred tool for data scientists. On the other hand, you can go for SAS in terms of service industries, providing tools for the said sectors. However, knowing both will only give that person extra versatility and marketability.
Supriyo Khan
Supriyo Khan
Supriyo Khan
Supriyo Khan
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