SAS vs Python
Last updated on Jan 25, 2024
What is SAS?
SAS stands for Statistical Analytics System. It is a software system developed to accommodate complex analytics, data techniques and other mathematics, but is mostly used by big companies, especially in the banking, health and insurance sectors. SAS is not open-source, this is not free but it is not affordable either, and this is the greatest deterrent to business owners and start-ups that would have been able to do so.At present SAS is expanding its platform to include emerging technologies like AI and machine learning tools as well. Moreover, it also provides services related to custom intelligence, risk management and identifying, big data functionalities, etc.
Why SAS?
Since SAS has been developed primarily for industrial and commercial purposes, this may not be the greatest option for beginners or solo data analysts to discover except if their main objective is to think about working in an industrial environment and to have new skills to be more competitive in the current industry. For all those who wish to learn SAS computing for free, a free version of SAS known as SAS University is available for educational purposes only and not for industrial applications.
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Features of SAS:
The exciting features of the SAS are:
- SaS is not a free platform or even an open source.
- It integrates the functionalities or capabilities of AI and machining learning techniques.
- SAS comes with high data security and stability.
- Moreover SAS provides excellent customer service, technical support and maintenance services as well.
- As it is compatible with cloud platforms, commands can be easily processed in the cloud.
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What is Python?
Python is an open-source object-oriented programming language which has become exceptionally successful with data analysts and software engineers. Python is recommended as it endorses, among many others, organized, object-oriented and operational programming and incorporates current infrastructure.Python comes with libraries to support a variety of data manipulation functions, including data integration, information extraction, business intelligence, visual analytics, and artificial intelligence. The libraries of the python are: pandas, Numpy, tensorflow, matplotlib, etc.
Why Python?
The simple truth that Python is perhaps the most popular language between many software developers and project managers helps make it simple to master, interpret, and then use. Python provides a sleek comprehensible syntax that makes it more convenient for newbies because they don't go into a lot of programming. This provides people an opportunity to plan mostly on learning the other operations of data science.
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Features of Python:
The attractive features of python are:
- Python is easy and simple to learn programming language as it requires menial coding.
- It comes with more number of libraries
- It comes with extensive support for many other operating systems like Mac platforms, Linus and Windows.
- Python is a highly scalable, interpreted and fastest programming language.
- Moreover, python comes with great features such as visualization, data analytics, and data manipulation functions as well.
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Comparison between SAS vs Python:
Now let us compare the SAS and python in detail.
- Learning curve:
Python:Python, on the other hand, is quick to understand thanks to its simple function. However, instead of an interactive GUI like the one in SAS, Python has an IPython notebook that allows students to access code.
SAS:For individuals who are really experienced with SQL, mastering the fundamental SAS language is possible due to a growing Emphasis. Prior to actually writing code, an adult should first acquaint himself/herself with the SAS GUI interface. There is no need to have previous knowledge to learn SAS.
- Cost effectiveness:
Python: Python becoming an open source platform and it is very much free to download it. However they won't provide any tech support or guarantee documents for the users. It is mostly preferred by the small and medium sized organizations due to its flexibility and transparency of the systems.
SAS:SAS is a licensed option and is more expensive as well. This SAS platform is equipped with mutli[le features which can be used only after the purchasing and upgrades. Most of the big IT companies rely on it.
- Data Science capabilities:
Python:In the field of data science, Python language succeeds in the analysis of complex data. Libraries also including Scikit Learn, Pandas, and NumPy, and Matplotlib for visual representation, end up making it an alternative for beginners who want to undertake a career in data science.
SAS:SAS also typically includes data science abilities, such as simultaneous data analysis, access to and strategic planning of datasets through an interconnected SQL database system.
- Libraries and tools supported:
Python:Python includes many other libraries for web design, software development, data science and visualization, desktop GUI programming, as well as machine learning and AI frameworks. Python is therefore a great option for exploiting and envisioning huge amounts of data.
SAS:SAS provides a variety of built-in business intelligence, data storage, graphical and computational tools that make it a better platform for manipulating data, especially on stand-alone data centres or devices. Although SAS could be used to determine outcomes very well, it is not as great as Python in terms of data visual representation as it cannot create special statistics.
- Market demand:
Python:Python is a powerful device that is not restricted to data analytics and software engineering functionality, creating a broader market for individuals with Python tech skills.
SAS:For a long time, SAS held the largest market share, and in particular the organizational market. However, the economy is continuously shifting toward these open-source technologies, which is why Python has grown exceptionally in prominence.
- Application advancements:
Python:Due to its open nature of Python, the introduction of innovative features and methodologies is fast compared to SAS. Although there are opportunities for sustainable development since they're not well-tested due to their accessible ability to contribute.
SAS:SAS is introducing a new edition in the type of software releases or rollouts. As it is granted a license, all functionalities and updates are well tested. It's much less likely to be an error especially in comparison to Python.
- Graphical capabilities:
Python:Python has a fierce challenge with graphics bundles such as VisPy, Matplotlib. But, compared to SAS, it's still complex.
SAS:SAS includes system graphical capabilities. But this is extremely practical. Making any customization is a difficult task to achieve. We need to comprehend the SAS Graph package rigorously to configure it.
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- Preference by industry:
Python:Python is recommended by start-ups, small and medium-sized technology companies since it provides advanced features for handling large unorganized data sets at no cost. It even has AI and machine learning abilities.
SAS:SAS is mostly embraced by large corporations whose major worry is high stability, better security and devoted customer support, not the expense of the application.
- Updates:
Python:Python is continuously replaced with the latest features from the community, making the latest developments quicker than SAS.
SAS:SAS will only be amended when a new version is rolled out.
Conclusion:
The technology is changing towards transmission. Second, tools like Python are flexible and most recommended for data science. SAS is much more appropriate to statistical analysis and business intelligence. For this reason, it would have been more beneficial for a beginner interested in exploring data science to understand Python. But adding SAS to their knowledge base would give newbies more possibilities.
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