SAS vs R vs Python – If you are going to choose analytics profession then the major question that arises in your mind is “Which is the best tool for the job ?”
It has been a battle for years and it is always hard to decide between the programming languages best suited for data analysis.
Traditionally this question was raised against SAS vs R but now python has joined this discussion. So which is better between sas vs r vs python.
Few years back it was difficult to trace the career path in these tools. But fortunately this turned out to be a blessing in disguise.
But now analytics professional before deciding what technique they should apply, they are into the process of searching for the best tool to perform that task.
There is a heavy competition between SAS vs R vs Python. But the honest answer is that each tool is unique in its own way. There is no universal winner in this context. Each tool has its own strength and weakness.
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It is important for a analytic professional to know the strengths and weaknesses of each tool to decide which is best to use for their profession.
SAS vs R vs Python Infographics
Now let’s take a look at what are the tools about and what it is used for.
Here is a short description about the 3 tools
SAS is the integrated system of software solutions and it is the leader in the data analytics field. This software has a lot of features like good GUI and others to provide awesome technical support. SAS helps you to do the following tasks
- Data Entry, retrieval and management
- Report writing and graphics design
- Statistical and mathematical analysis
- Business Forecasting and decision support
- Operations Research and Project management
- Applications development
SAS is used by reputed companies like Barclays, Nestle, HSBC, Volvo and BNB Paribas.
R is a programming language for statistical computing and graphics which was created in the year 1995 by Ross Ihaka and Robert Gentleman. It offers a wide range of statistical and graphical techniques. It is a open source route which is highly extensible. It is a simple and effective programming language. It is more than just a statistics system. It does the following work
- Easily manipulates packages
- Manipulates strings
- Works with regular and irregular time series
- Visualize data
- Machine learning
R is used by top rated companies like Bank of America, bing, Ford, Uber and Foursquare.
Python is a object oriented programming language that has a clear syntax and readability. It was created in 1991 by Guido Van Rossem. It is easy to learn and will help you work more quickly and effectively. It has become more popular in a short period of time because of its simplicity.
Python is used by famous companies like ABN-AMRO, Quora, Google and reddit.
Reasons for comparison
Industries are growing dynamically. As the field grows there are a lot of technological advancements in each language.
If you are new to the data analytics field then you might be learning a new one because of your interest or most of the times driven by what your organization works with. You might challenges and frustrations because of upgrades in the tools and software programs.
Comparison of the languages is a worthy consideration now. Any comparison which was done before few years will not be relevant to the current situation. Comparisons will also help in choosing the best among the three.
These languages are compared on the following factors in this article. You may not purchase a tool based on the following comparisons but it will definitely be useful for you to choose one which suits your career.
Open Source vs Closed system
SAS is a closed source and does not support transparent functionalities. Whereas R and Python is the open source counterpart of SAS and contains detailed transparency of all its functionalities and algorithms.
SAS is more time consuming as it takes a long process to know the functionality.
SAS is also counter-productive.
SAS is one of the most expensive software in the world. Millions of dollars need to be invested in getting SAS license. Therefore it can be used only by large scale companies.
There are only few companies who use SAS. If you are a SAS professional then you need to choose a workplace where they use SAS. If you join in a company where they don’t use SAS then your career will be redirected to a new path.
R is a open source software which can be downloaded for free by anyone.
Python on the other hand is also a free open source software and can be downloaded by anyone.
SAS is easy to learn specially for people who already know SQL. Also SAS has a stable GUI interface. Tutorials of SAS is available in various sites and it has a comprehensive documentation.
Python is very easy to learn in the data analytics world. Python does not have a widespread GUI interface but Python notebooks has become popular. They provide you with the features of documentation and tutorial.
R is a low level programming language and so longer codes are required even for shorter procedures. You need have a deeper insight of coding in R.
SAS requires you to buy new products to get to know about the advanced features of SAS. It does not offer you an option to download any feature and use it instantly. SAS also has a strict licensing limitations.
Whereas in R and Python you are allowed to access or upgrade to the advanced features like parallel processing, multicore packages, etc to help you do repetitive operations.
Data handling capabilities
All the three languages are equally good in data handling and they also have an option for parallel computations. There is no much difference between the three in this factor. There might be few innovations made to each of these language to improve their standard.
With reference to this factor, R has the best graphical capabilities when compared with the other two.
SAS has basic graphical capabilities but it is only functional. Customization on plots is difficult and it needs a in depth knowledge to know about the SAS Graph package
Python has the option of using native libraries (matplotlib)or derived libraries which allows to call for R functions.
R has excellent graphical capabilities among the three. They have advanced packages for graphical capabilities.
Advancements in tool
All the three languages have the basic and most required functions but the latest technologies and functions matters a lot if your work expects it.
R and Python are open source in nature so they get enhanced to the latest technologies and features more quickly than the other two languages. Development of new techniques is very fast in R.
SAS on the other hand takes time to update to latest features and capabilities as it works in controlled environment.
There is one main advantage of SAS being working in a controlled environment. They are well tested and so the chances of errors are very less.
But Python and R works in a open source and gets updated to the latest technologies very fast but they are more open to errors.
R and Python have more job openings in the recent past and it is also expected to increase in the future.
R and Python are used by companies who look for cost efficiency. They are the best option for start up companies.
SAS is used widely by big organizations and corporate companies.
A recent study have proved that Python jobs for data analytics will also increase in the same way as R.
Support for Visualization
Visualization is an fundamental part of data science. The main visualization platform of SAS is called SAS Visual Analytics. This is too costly to use.
R and Python has a lot of visualization tools for free. It does not require you to sign a contract and pay for each and every activity like in SAS.
Customer Support and Community
Based on customer support and service SAS is the best when compared with the other two languages. SAS has a dedicated customer support and service and a community. If you have any technical problems you can contact the support centre directly.
R has a big online community but no customer support centre. You will get help from them but not instantly.
Python too does not have a customer support centre. It provides help to its customers but not to the level of SAS.
The trend of job market is moving fast towards open source technologies. R, Hadoop, Python are all the major examples of this. SAS is also one among such technology but it is the only paid product. People prefer R and Python instead of SAS because it does not provide any extra benefits over the free products. Only a few companies go for SAS these days for certain reasons.
R and Python comes for free and can be downloaded with ease.
R and Python are supported by thousands of contributors worldwide. If there is any development or up gradation available to languages then it is made available to the customers at ease.
SAS product is accessible only by the SAS Institute Incorporated and only the SAS developers are allowed to produce any new features. This takes a lot of time. And before you update the SAS features with new algorithms you can complete your project using any other tool.
Tutorials and Guide
SAS does not offer any step by step guidance to its customers. If you are starting with a new topic or wanted to learn something new in SAS then you should definitely seek the help of a SAS consultant who is again from SAS Institute Incorporated.
R and Python on the other hand provide you with detailed examples. It also offers a tutorial on the internet. Python contains reproducible notebooks called iPython. R exercises and iPython Notebooks are widely available in sites such as github and others.
Here is table view to easily compare all the three tools based on few criteria’s
|Advancements in tool||High||Low||Medium|
SAS can definitely satisfy all your data science needs but it is not suitable for the long run. Companies are now moving fast towards open source programming languages which is easy to access and use.
SAS being restrictive and closed tool it is not preferred much these days.
R and Python are open source tools which will help you increase your data science knowledge, learn new technologies and algorithms. Knowing about R and Python automatically makes you eligible for data science jobs these days.
The bottom line is there is no obvious winner among the three. All the three tools has its own advantages and disadvantages. Their strengths make them survive in the market for long run.
It is ultimately the data scientist who has to decide between the languages. As a data scientist its upto you to decide which language fits the best for your need. You can ask to yourself few questions and decide about that
- What type of problems you want to solve ?
- How much are you ready to spend to learn a language ?
- What are the commonly used tools in your field ?
- What are the other similar tools available in the market and how does in relate to the commonly used tools ?
The answers to these questions can help you to choose the best tool and go ahead in your career.
Learn and become a master of the language.