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Infographic: Quick Guide on SAS vs R vs Python

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Introduction

One of the perennial points of debate in data science industry has been – “Which is the best tool for the job?“. Traditionally, this question was raised for SAS vs. R. Recently, there have been discussions on R vs. Python.

A few decades back, when R / SAS launched, it was difficult to envisage the possibilities future will offer. And this turned out to be a ‘blessing in disguise’. Because, it made easy for them to focus on one tool!

But today ? The situation is different. Even before deciding what technique they should apply, they fall into the pit of searching for the best tool to perform that particular task. And finally, they get nothing out of it.

The honest answer is that there is no universal winner in this contest. Each tool has its own strength and weakness. A prudent data scientist would diversify his / her repository of tools and use the one appropriate in each situation. In order to do this, it is critical to know the strengths and weakness of each tool, which is what this infographic offers.

 SAS vs R vs Python infographic datascience

Note: You can read the comprehensive version of this article here.

 

End Notes

By now, you must have realized, there is no clear winner in this race. Every tool has its own importance and own strength areas. These strength areas provide them the leverage to survive in industries and hence factors defined in the infographic plays a significant role in their evaluation.

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19 Comments

  • Saurabh Verma says:

    Very nice info graphics! Clear thought on parameters for comparison.
    Good for all (beginners and experts) to get a view on tools of their choice vs. other two closest options available.

    Great job!

  • Sudhindra says:

    Awesome comparison and thorough insight 🙂 Thanks for sharing! Also we have many other levels of SAS like SAS PM, SAS DI and SAS BI. Any inputs on these please?

  • Sanjay Karn says:

    Thanks Mr.Manish for this comparative post really helpfull for beginners into data analytics.

    Can you please suggest a tool for a beginners into data analytics like me to start with prospect of Job scenario.
    I have recently registered with JIGSAW academy for Data science with R, but still confused, whether to go for SAS or R.
    I am PGDM(Markrting & IT), B.Sc. with more than 8yrs Exp. in Real Estate & BFSI on Key account Mngmnt, CRM, MIS, People and process mnagmnt. Please suggest….!!

    Thx.

  • Mayur says:

    How about Scala?
    I heard Scala is also gaining momentum in analytics market.

  • Vikram Choudhary says:

    Can you also put some light on Alteryx

  • Bill Venables says:

    Why learn just one tool?

    For a young person entering the profession the only thing certain is that throughout their career they will have to learn to work with new tools all the time. The important thing is to “learn how to learn”. It should not be beyond the capacity of anyone in the game to use both R and Python, in my view.

    • Nick Byrd says:

      “A prudent data scientist would diversify his / her repository of tools and use the one appropriate in each situation. “

  • Brett Wujek says:

    I really like the infographic comparisons here and I think the assessment is overall pretty accurate. For full disclosure – I am a member of the Advanced Analytics R&D team at SAS…that being said, I agree with the sentiment expressed here and supported by a few other in the comments that it is wise to expand your toolbox and be prepared to use whatever language/package best suits your current situation and application. Moreover, the desire/need for hybrid solutions is becoming a reality, and SAS is embracing this through more recent promotion of integration with open source languages.
    See http://blog.revolutionanalytics.com/2015/05/call-r-and-python-from-base-sas.html and the post from my colleague Patrick Hall at https://communities.sas.com/docs/DOC-10746

  • ranjanpossible says:

    hi Manish,
    i am really not convinced with your comment that learning path of python is easier than R because i feel if a person is having good programming background then python will seem to be easier otherwise R and SAS are very easy to learn for a person who is not of hard core programming background .

  • Vijay Gupta says:

    1. Some of the conclusions are incorrect. Data Handling– SAS is the clear winner. Even with Hadoop, Big Data, and Parallel Processing, R and Python are nowhere near SAS. Ease of learning: R is actually the most difficult to master because it is unlike a normal language (for example, it does not have macros and there are several ways to achieve the same result). Mastering SAS MACROS is also difficult.

    2. No true statistician/data-scientist uses only one tool. Ideally, one should know SAS and [a] R if a researcher, or [b] Python if a software person getting into this field.

    3. The “companies”using the tools is misleading as it includes those using Python for a web site. Moreover, every analyst, even they are using SAS/SPSS/Statistica will also use a freeware like R.

  • Somesh says:

    Python is the clear winner.. I would rate is at 28 out of 30 r with 27 and Sas a 24. Consider data once connection to sql and spark for big data. Also mahout being not very userfriendly. Also code deployment to website for real time prediction and champion challenger python wins due to ease. Also consider BI stack integration python is close to Java on which most Bi stack and hadoop stack take use of again Python wins hands down.. Guys python Is the language for the future. With H2o and Django gaining popularity with spark.. Python is the clear number 1.. Cheers

  • Somesh says:

    My two cents.. Python is the clear winner.. I would rate is at 28 out of 30 r with 27 and Sas a 24. Consider data once connection to sql and spark for big data. Also mahout being not very userfriendly. Also code deployment to website for real time prediction and champion challenger python wins due to ease. Also consider BI stack integration python is close to Java on which most Bi stack and hadoop stack take use of again Python wins hands down.. Guys python Is the language for the future. With H2o and Django gaining popularity with spark.. Python is the clear number 1.. Cheers

  • Rakesh Kumar says:

    Thanks Manish. It’s great to have a comparison table. Ultimately, all 3 analytical tools have great potential to make anyone a Data Scientist. One should decide and learn based on the application scope in his current / future role. So, a combination of SAS with R or SAS with Python will be great.

  • Tom Kari says:

    Your comparison is very nice; it’s always good to see different tools compared.

    Personally, when I’m considering this kind of comparison, I view i) software costs, ii) ease of learning, and iii) product capabilities as being all factors in “cost to solution”. That is, you pay 1) for the software, with dollars, 2) to learn to use the software, with time, which equates to dollars, and possibly training, with dollars, and 3) to develop each project, with time, which equates to dollars. This lets me use one number, cost, to compare a powerful but expensive software product, with which I can quickly code a solution, versus a less-powerful but less expensive product, but that takes much longer to code the solution.

    And then the suitability of the product to the requirement is also of great importance.

    Thanks for the interesting article!
    Tom

  • RAKESH KUMAR says:

    Manish, you have provided very good comparison over SAS vs R vs Python. Can you please also highlight – how important is MS Excel in a Data Scientist’s life? Does it play any significant role or one can dream to become a Data Scientist without acquiring Excel Knowledge?

    Regards,
    Rakesh

  • Alan Dunham says:

    Here’s my $.02 (46 years using a variety of analytic tools)….this graphic is good in that it encourages thoughtful choices, but the choices are more complex than shown. When my son, going through an MS in analytics degree program, asked which tool to use, I said he should use whatever his professors and academic department preferred. He wound up using R and is content to use it in his follow-on job. R and Python require programming, SAS does not require programming if one uses Enterprise Guide. SAS is free for students but expensive after college if you are a one-off license not in a corporate or gov’t office. If your job future is Gov’t or business analytics, then SAS is a better bet. If you are going to stay in an academic or scientific lab environment, then R is what you will find your colleagues using more often, although all three will work well. Python is a programming language taught to new IT professionals, which should tell you something about the language’s capabilities and its complexity. If you do not know a programming language, then Python or R is worth the investment for your long-term career. R is free, and so is Python, but if I were supervising a team of analysts, I would wonder if I want my people doing analysis or writing code. If R or Python is the choice, then they will be spending more time writing code, testing the code, and verifying and validating that the code does what it is supposed to do under a a robust set of circumstances. All of that is done for you and documented by SAS. The graphics in SAS is superb. Interestingly, at work I use SQL to download data to a csv file, SPSS on a desktop to do statistical analysis on the data, export contingency tables from SPSS to Excel for the graphics, and export graphics from Excel to Powerpoint and Word for deliverables. I have and use Tableau occasionally, but Tableau is not useful for data wrangling since it cannot modify a dataset.. SAS procurement is underway in my office to streamline analytic tasks at work using a single application. FWIW, I have purchased and use JMP (a separate SAS product) for my own use on a personal laptop. It is much more affordable for individuals and superb, doing a lot (not all) of what SAS can do in a totally interactive manner. I enjoy writing code, but get more pleasure from analyzing and solving more problems faster. Hopefully these comments will stimulate you to lay out your own tradeoffs and choices.

  • Randeep says:

    Nice and crisp comparison of the three tools. Thanks for sharing.

  • Atish says:

    Dear Sir,
    i had a query to ask upon.
    I am btech in ece + Post grad in banking & have 3 yrs exp in retail (sales operation) banking segment .
    Now i am planning to do a mid career shift to analytics but confused regarding choosing SAS/R .
    As per market SAS has got less openings with compared to R .
    But again no banking or financial companies use R ..they use SAS only .
    R is for startups and e commerce companies .
    ..
    Will it be Good for me to go with R tool as a beginner?? .

    My major concern is to ultimately land up in a job that will consider my past experience of 3 yrs ./
    How is the job scenario for a fresher like me ?

    I request you to kindly guide and clarify my doubt .


    Regards,
    Anand Kumar
    Bangalore
    Mob 8123556525

  • Valerie says:

    It is a very helpful comparison, however the slide “Companies using” is about nothing. I work in a major investment bank as a quant and we may use Python, R, SAS, C++, Matlab, VBA and whatnot. Normally, one team sticks to 1-3 options, but across teams and divisions it can be different. The same was with my previous job at another big company.
    Anyway, thank you very much for info graphics. I was thinking whether I want to learn SAS or not and decided, that I really don’t.

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