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IBM v. SAS v. R v. Python

  • 1.  IBM v. SAS v. R v. Python

    Posted Fri January 11, 2019 01:48 PM

    Hello IBM Community,


    For years, I have used R, SAS, JMP, and SPSS, and am currently learning Python. 


    I learned SPSS as an undergraduate and learned a few more things using SPSS in graduate school, and have always found those things easier to do in SPSS than other formats like R, Python, JMP or SAS most likely, because I first learned them in SPSS.  I am wondering if there are things you have found that are easier to do in SPSS than SAS, R, or Python, or, if it is because you first learned them in SPSS. 


    I feel that visualizations are substantially better in R and Python, and maybe even JMP or SAS, and am wondering what other competitive advantages SPSS offers over these statistical solutions beyond being someone's first programming language. 


    Any ideas or feedback you have is welcome in my justification for why I should keep purchasing SPSS in addition to other software solutions.






    John Barnshaw, Ph.D.

    Associate Vice President

    Research and Statistics

    Ad Astra

    O: 913.652.4143 | M: 704.560.0229


    Ad Astra


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  • 2.  RE: IBM v. SAS v. R v. Python

    Posted Mon January 14, 2019 03:32 AM
    Hello John,
    I personally use and prefer Python for AI/ML application, I don't yet know  SPSS. For visualization there are several tools such as matplotlib, seaborn, etc. But new tools appear every year to make things easier. I have just discover a new one I would like you to try too, it is called plotly:
    It would be kind from you, if you have a bit of time, to then make a post about differences between both (Python and SPSS).


    Konan Jean-Claude Kouassi
    Practice Makes Perfect!

  • 3.  RE: IBM v. SAS v. R v. Python

    Posted Tue January 15, 2019 08:17 AM

    The advantage I see with SPSS is that you do not need to write lines of code to get a graph. Most things can be achieved with a few mouse clicks.

  • 4.  RE: IBM v. SAS v. R v. Python

    Posted Wed January 16, 2019 08:42 AM
    I leaned SPSS Stats in a quantitative social science PhD program. While there gaining skills in research stats I became interested in the predictive capabilities of the various types of regression and discriminant function analysis. Becoming then a practitioner rather than a professor, I self-educated on machine learning and promptly purchased IBM Modeler. I love it because I never learned SQL or R. I can access, wrangle, predict, report and impress. While I'm on a slow path learning Python, I'm simply not of a coding mindset and I don't have much time to learn and practice. I have to instead lead, strategize and enjoy life some. Modeler has facilitated my becoming a subject matter expert in fundraising analytics in non-profit organizations.

    Stephen Lambert

  • 5.  RE: IBM v. SAS v. R v. Python

    Posted Wed January 16, 2019 09:25 AM
    OK @Stephen Lambert, Thanks for sharing!​​

    Konan Jean-Claude Kouassi
    Practice Makes Perfect!

  • 6.  RE: IBM v. SAS v. R v. Python

    Posted Wed January 16, 2019 11:46 AM
    Hi John,

    I've used SPSS, R and Python.

    The main advantage of SPSS is that you don't need to have programming skills in order to use it, you could just focus on finding the right model for your data.
    On the other hand, you need to know how to code in R or Python in order to use one of them, but the main advantage of those programming languages is the flexibility.  Both Python and R are open source, relative easy to learn and there is a big community of programmers who help each other in forums.

    I usually switch between SPSS and Python (I haven't been using R lately because I wanted to learn Python) and I like to use Python for big data sets. PySpark is the Python API for Spark and it's great for handling big data.

    Finally, for graphics and reports I definitely prefer Python (or R). With the library Plotly you can plot nice graphs and it's available for Python and R. For reports I like Jupyter Notebooks where you can write text and add graphics using Markdown but you can also write and run code in the same file!

    In IBM Cloud you can use Watson Studio where you can create projects, use Jupyter Notebooks, the IDE Rstudio, and add resources from the IBM Cloud Catalog.

    Raquel Vargas
    Presales Data Scientits
    IBM Ecuador

  • 7.  RE: IBM v. SAS v. R v. Python

    Posted Fri January 18, 2019 01:00 AM
    I have used SPSS, SAS, R and Python. Each has its own strengths in terms ease of use, flexibility and of course the cost.
    Currently I am using R it gives me more flexibility in carrying out analytical activities including Machine learning and Deep learning 

    International Journal of Statistics and Medical Informatics

    Editor IJSMI

  • 8.  RE: IBM v. SAS v. R v. Python

    Posted Tue February 05, 2019 05:30 AM
    Lets start with R: R  is  statistical and visualization language which is deep and huge and we need good mathematical skills.People without having skills will find it very difficult .
    Python:it is software development language which is deep but it is based on C.It is multipurpose free and open sourse.
    SAS: SAS stands for Statistical Analysis System.It is used for data management,business analytic, advance analytic and it is very much in demand in the present days .
    for knowing more about R,python,SAS  do visit Best SAS training in delhi.

    Raj Shivakoti