As a beginner, learning SAS or Python is a personal choice. There are pros and cons of selecting one over the other. This article aims to help you make that choice. As a Data Scientist, the two main considerations are:
- Cost
- Capabilties
- Present Application
- Future Application
| – | Python | SAS |
| Cost | – Open Source – Free for commercial use |
– Free for Students/Learners Closed Software – Expensive for commercial use |
| Capabilties | – Bleeding edge capabilities – May be unstable due to open community contributions |
– Stable due to being developed in a controlled environment – Less Prone to bugs |
| Present App | Preferred by start-ups, SMEs due to the low cost and AI/Machine Learning Capabilities, requiring a small but capable team | Mostly adopted by big corporations and MNCs, where stability and security are essential with many moving parts |
| Future App | The industry has already shifted towards open-source technology and preferred in data-science | SAS will remain the status quo for statistical analysis and business intelligence |
As a Data Scientist dealing with bleeding edge software, I am of the opinion that Python would be of greater relevance to the future of AI. It is also relatively easy to learn due to its simple syntax.
However, using Python requires quite a bit of willpower for an unguided beginner and the tools cannot be compared to the ease of handling the SAS GUI, thereby creating a steeper learning curve. Given enough time and space, it is beneficial to learn both but realistically you would need to stick to one.
For Python, an integrated development environment (IDE) such as Spyder will be used. Installation of Spyder and its dependencies will require a lot more work than logging into SAS studio. For SAS, SAS studio (cloud hosted) will be used for majority of the work.