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IBM SPSS Statistics vs. Microsoft R Open / Revolution R Enterprise vs. pandas

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    IBM SPSS Statistics

    Score8.1 out of 10
    N/ASPSS Statistics is a software package used for statistical analysis. It is now officially named "IBM SPSS Statistics". Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection), data mining (IBM SPSS Modeler), text analytics, and collaboration and deployment (batch and automated scoring services).

    $105

    per month per user

    Microsoft R Open / Revolution R Enterprise

    Score8.9 out of 10
    N/AMicrosoft R Open and Revolution R Enterprise are big data R distribution for servers, Hadoop clusters, and data warehouses. Microsoft acquired original developer Revolution Analytics in 2016. Microsoft R is available in two editions: Microsoft R Open (formerly Revolution R Open) and Revolution R Enterprise.N/A

    pandas

    Score10 out of 10
    N/Apandas is an open source, BSD-licensed library providing high-performance data structures and data analysis tools for the Python programming language. pandas is a Python package providing expressive data structures designed to make working with “relational” or “labeled” data both easier. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.N/A
    Pricing
    IBM SPSS StatisticsMicrosoft R Open / Revolution R Enterprisepandas
    Editions & Modules
    Base
    USD 3,830
    one-time fee per user
    Standard
    USD 8,440
    one-time fee per user
    Professional
    USD 16,900
    one-time fee per user
    Premium
    USD 25,200
    one-time fee per user
    Monthly subscription
    USD 105
    per month per user
    Annual subscription
    USD 1,188.00
    per year per user
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    IBM SPSS StatisticsMicrosoft R Open / Revolution R Enterprisepandas
    Free Trial
    YesNoNo
    Free/Freemium Version
    NoNoNo
    Premium Consulting/Integration Services
    NoNoNo
    Entry-level Setup FeeNo setup feeNo setup feeNo setup fee
    Additional Details———
    More Pricing Information
    Community Pulse
    IBM SPSS StatisticsMicrosoft R Open / Revolution R Enterprisepandas
    Considered Multiple Products
    IBM
    Chose IBM SPSS Statistics
    Overall, IBM SSPS outperforms competitors in almost every arena. It's ability to both perform statistical analysis and geospatial analysis is unrivaled. Additionally, it is superior in handling large or complex datasets over many of the other similar programs. The only program …
    Incentivized
    Microsoft
    Chose Microsoft R Open / Revolution R Enterprise
    R requires knowledge of programming and can be a high learning curve versus if you're using a user-friendly SPSS or JMP.
    Incentivized
    Open Source
    No answer on this topic
    Key User Insights
    Would buy again
    94%
    Would buy again
    49 Answers
    No answers on this topic
    No answers on this topic
    Delivers good value for the price
    91%
    Delivers good value for the price
    42 Answers
    No answers on this topic
    No answers on this topic
    Happy with the feature set
    98%
    Happy with the feature set
    51 Answers
    No answers on this topic
    No answers on this topic
    Lived up to sales and marketing promises
    97%
    Lived up to sales and marketing promises
    34 Answers
    No answers on this topic
    No answers on this topic
    Implementation went as expected
    97%
    Implementation went as expected
    35 Answers
    No answers on this topic
    No answers on this topic
    Features
    IBM SPSS StatisticsMicrosoft R Open / Revolution R Enterprisepandas
    Platform Connectivity
    Comparison of Platform Connectivity features of IBM SPSS Statistics and Microsoft R Open / Revolution R Enterprise and pandas
    Feature
    IBM SPSS Statistics
    -
    Ratings
    Microsoft R Open / Revolution R Enterprise
    5.3
    3 Ratings
    45% below category average
    pandas
    8.5
    1 Ratings
    2% above category average
    Connect to Multiple Data Sources00 Ratings6.13 Ratings8.01 Ratings
    Extend Existing Data Sources00 Ratings6.03 Ratings8.01 Ratings
    Automatic Data Format Detection00 Ratings6.03 Ratings10.01 Ratings
    MDM Integration00 Ratings3.01 Ratings8.01 Ratings
    Data Exploration
    Comparison of Data Exploration features of IBM SPSS Statistics and Microsoft R Open / Revolution R Enterprise and pandas
    Feature
    IBM SPSS Statistics
    -
    Ratings
    Microsoft R Open / Revolution R Enterprise
    7.0
    3 Ratings
    18% below category average
    pandas
    -
    Ratings
    Visualization00 Ratings7.03 Ratings00 Ratings
    Interactive Data Analysis00 Ratings7.03 Ratings00 Ratings
    Data Preparation
    Comparison of Data Preparation features of IBM SPSS Statistics and Microsoft R Open / Revolution R Enterprise and pandas
    Feature
    IBM SPSS Statistics
    -
    Ratings
    Microsoft R Open / Revolution R Enterprise
    4.8
    3 Ratings
    52% below category average
    pandas
    -
    Ratings
    Interactive Data Cleaning and Enrichment00 Ratings5.13 Ratings00 Ratings
    Data Transformations00 Ratings5.03 Ratings00 Ratings
    Data Encryption00 Ratings3.01 Ratings00 Ratings
    Built-in Processors00 Ratings6.03 Ratings00 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of IBM SPSS Statistics and Microsoft R Open / Revolution R Enterprise and pandas
    Feature
    IBM SPSS Statistics
    -
    Ratings
    Microsoft R Open / Revolution R Enterprise
    6.0
    3 Ratings
    34% below category average
    pandas
    -
    Ratings
    Multiple Model Development Languages and Tools00 Ratings5.03 Ratings00 Ratings
    Automated Machine Learning00 Ratings5.02 Ratings00 Ratings
    Single platform for multiple model development00 Ratings8.03 Ratings00 Ratings
    Self-Service Model Delivery00 Ratings6.03 Ratings00 Ratings
    Model Deployment
    Comparison of Model Deployment features of IBM SPSS Statistics and Microsoft R Open / Revolution R Enterprise and pandas
    Feature
    IBM SPSS Statistics
    -
    Ratings
    Microsoft R Open / Revolution R Enterprise
    6.5
    2 Ratings
    27% below category average
    pandas
    -
    Ratings
    Flexible Model Publishing Options00 Ratings6.02 Ratings00 Ratings
    Security, Governance, and Cost Controls00 Ratings6.92 Ratings00 Ratings
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    User Ratings
    IBM SPSS StatisticsMicrosoft R Open / Revolution R Enterprisepandas
    Likelihood to Recommend
    8.5
    (116 ratings)
    6.0
    (5 ratings)
    10.0
    (1 ratings)
    Likelihood to Renew
    8.5
    (23 ratings)
    7.0
    (1 ratings)
    -
    (0 ratings)
    Usability
    8.0
    (15 ratings)
    7.0
    (1 ratings)
    10.0
    (1 ratings)
    Availability
    6.0
    (1 ratings)
    -
    (0 ratings)
    -
    (0 ratings)
    Performance
    6.0
    (1 ratings)
    -
    (0 ratings)
    -
    (0 ratings)
    Support Rating
    6.4
    (12 ratings)
    8.0
    (2 ratings)
    -
    (0 ratings)
    Implementation Rating
    8.7
    (7 ratings)
    -
    (0 ratings)
    -
    (0 ratings)
    Configurability
    5.0
    (1 ratings)
    -
    (0 ratings)
    -
    (0 ratings)
    Ease of integration
    5.0
    (1 ratings)
    -
    (0 ratings)
    -
    (0 ratings)
    Product Scalability
    5.0
    (1 ratings)
    -
    (0 ratings)
    -
    (0 ratings)
    Vendor post-sale
    5.0
    (1 ratings)
    -
    (0 ratings)
    -
    (0 ratings)
    Vendor pre-sale
    5.0
    (1 ratings)
    -
    (0 ratings)
    -
    (0 ratings)
    User Testimonials
    IBM SPSS StatisticsMicrosoft R Open / Revolution R Enterprisepandas
    Likelihood to Recommend
    IBM
    IBM SPSS Statistics is well suited for pretty much any data analytic scenario. It can handle extremely complex and large-scale datasets with ease. It especially shines if you have to do any kind of analyses that involve significance testing. Being able to do any number of significance tests (i.e., t-tests, chi-square, ANOVAs, etc.) right inside the tool is very valuable. The only scenario I would say it is less appropriate is if you need to work on a very small dataset and answer very simple questions, like frequencies or averages. In those cases, something like Excel could probably do the job just as easily.
    Incentivized
    Read full review
    Microsoft
    If you are a MS shop specifically, or have more generic data requirement needs from Microsoft sourced data this will work well. If you have a lot of disparate data across a number of unique platforms/cloud systems/3rd party hosted data warehouses then this product will have issues or a lack of documentation on the net. Performance-wise this product is equal to other R platforms out there.
    Incentivized
    Read full review
    Open Source
    Pandas are great for quick and relatively simple analytics and visualizations
    Pandas work well for exploratory ad-hoc analytic work
    But , We had little success in implementing complicated predictive analytics. And large data sizes can be a problem.
    Incentivized
    Read full review
    Pros
    IBM
    • SPSS has been around for quite a while and has amassed a large suite of functionality. One of its longest-running features is the ability to automate SPSS via scripting, AKA "syntax." There is a very large community of practice on the internet who can help newbies to quickly scale up their automation abilities with SPSS. And SPSS allows users to save syntax scripting directly from GUI wizards and configuration windows, which can be a real life-saver if one is not an experienced coder.
    • Many statistics package users are doing scientific research with an eye to publish reproducible results. SPSS allows you to save datasets and syntax scripting in a common format, facilitating attempts by peer reviewers and other researchers to quickly and easily attempt to reproduce your results. It's very portable!
    • SPSS has both legacy and modern visualization suites baked into the base software, giving users an easily mountable learning curve when it comes to outputting charts and graphs. It's very easy to start with a canned look and feel of an exported chart, and then you can tweak a saved copy to change just about everything, from colors, legends, and axis scaling, to orientation, labels, and grid lines. And when you've got a chart or graph set up the way you like, you can export it as an image file, or create a template syntax to apply to new visualizations going forward.
    • SPSS makes it easy for even beginner-level users to create statistical coding fields to support multidimensional analysis, ensuring that you never need to destructively modify your dataset.
    • In closing, SPSS's long and successful tenure ensures that just about any question a new user may have about it can be answered with a modicum of Google-fu. There are even several fully-fledged tutorial websites out there for newbie perusal.
    Incentivized
    Read full review
    Microsoft
    • It allows distributed algorithm runs on Hadoop HDFS cluster
    • It allows using different file formats such as SAS7BAT files or complex files in tab or comma delimited making data munging easier
    • It provides scalable solutions by allowing users to re-use R scripts and distributing the computing over nodes through RHadoop
    Read full review
    Open Source
    • It is easy to do statistical analysis
    • It is easy to clean the data
    • It is easy to produce graphs and charts to visualize
    Incentivized
    Read full review
    Cons
    IBM
    • Cost is becoming prohibitive.
    • Availability of procedures in the base package seems to be dwindling.
    • The copy-and-paste function from output to Excel is not as easy as it once was (now I have to do a "paste special").
    • Text and date handling are terrible.
    • Need to include AI-based NLP for survey verbatims and other text-based fields. This is becoming increasingly important in the CX world, yet SPSS seems to be ignoring it.
    Incentivized
    Read full review
    Microsoft
    • Very steep learning curve... for such a quick and useful tool the learning curve is unacceptable.
    • Very dangerous in the wrong hands. Because most add-ons are pre-written, you have to trust the community that malicious script is not used.
    Incentivized
    Read full review
    Open Source
    • There are a lot of libraries and ways to do visualization. Sometimes it is very confusing.
    • Error handling can be a challenge. Sometimes the error messages do not provide valuable clues for the debugging.
    • In our case, there are a bunch of different frameworks and libraries working together. I would rather work with one framework, well tuned for my use case
    Incentivized
    Read full review
    Likelihood to Renew
    IBM
    Both
    money and time are essential for success in terms of return on investment for any kind of research based project work. Using a Likert-scale questionnaire is very easy for data entry and analysis
    using IBM SPSS. With the help of IBM SPSS, I found very fast and reliable data
    entry and data analysis for my research. Output from SPSS is very easy to
    interpret for data analysis and findings
    Incentivized
    Read full review
    Microsoft
    In general, Revolution Analytics brings a lot of value to the organization. The renewal decision would be based on return on investment in terms of quantified actionable insights that are getting generated against the cost of the product. Additionally, market brand of the tool and reputation risk in terms of possible acquisition and its impact to overall organizational analytic strategy would be considered as well.
    Read full review
    Open Source
    No answers on this topic
    Usability
    IBM
    Probably because I have been using it for so long that I have used all of the modules, or at least almost all of the modules, and the way SPSS works is second nature to me, like fish to swimming.
    Incentivized
    Read full review
    Microsoft
    It is good, easy to use, improvements are being made to the product and more info being shared in the community. It just needs some more time to become more integrated to other platforms and tools/data out there.
    Incentivized
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    Open Source
    Over the years, we tried a lot of different frameworks and tools, homegrown and commercial. Pandas provide the best results.
    It is lightweight, flexible and easy to implement.
    Incentivized
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    Reliability and Availability
    IBM
    SPSS can tend to crash when I am trying to do a lot of data. This can slow me down when I need to do a lot of data
    Incentivized
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    Microsoft
    No answers on this topic
    Open Source
    No answers on this topic
    Performance
    IBM
    SPSS does the job, but it can be slow. I do have to plan a lot of time to get through a huge amount of data.
    Incentivized
    Read full review
    Microsoft
    No answers on this topic
    Open Source
    No answers on this topic
    Support Rating
    IBM
    I have not contacted IBM SPSS for support myself. However, our IT staff has for trying to get SPSS Text Analytics Module to work. The issue was never resolved, but I'm not sure if it was on the IT's end or on SPSS's end
    Read full review
    Microsoft
    Generally support comes through the forums and user generated channels which are helpful, easy to access, quickly turned around and provided by knowledgeable users. However the support channels are not employees and the channels are often used as a way to learn quick difficult elements of R. Better design, users interface and tutorial options would alleviate the need for this sort of interaction.
    Incentivized
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    Open Source
    No answers on this topic
    Implementation Rating
    IBM
    Have a plan for managing the yearly upgrade cycle. Most users work in the desktop version, so there needs to be a mechanism for either pushing out new versions of the software or a key manager to deal with updated licensing keys. If you have a lot of users this needs to be planned for in advance.
    Incentivized
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    Microsoft
    No answers on this topic
    Open Source
    No answers on this topic
    Alternatives Considered
    IBM
    If you have made it this far, you should have a very good idea of how SPSS stacks up the competition (data processing and analytics tools). Even the free ones, such as r Studio or Stata, are leaps and bounds ahead of SPSS. IBM is resting on a reputation developed nearly 30 years ago and has shown no desire to improve.
    Incentivized
    Read full review
    Microsoft
    The two are different products for different purposes. But for someone who has little or no experience in R programming, Power BI would be better for starting with. Having said that, Microsoft R is built on R, thus allowing for customization of complex calculations not typically available otherwise.
    Incentivized
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    Open Source
    All these frameworks are great for gathering data and providing some initial analysis. But for real performance debugging work one needs more than tools provided by this tools. That's where the pandas excel.
    Incentivized
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    Scalability
    IBM
    I am neutral because I have not had to look into scalability since I am using as a student.
    Incentivized
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    Microsoft
    No answers on this topic
    Open Source
    No answers on this topic
    Return on Investment
    IBM
    • I found SPSS easier to use than SAS as it's more intuitive to me.
    • The learning curve to use SPSS is less compared to SAS.
    • I used SAS, to a much lesser extent than SPSS. However, it seems that SAS may be more suitable for users who understand programming. With SPSS, users can perform many statistical tests without the need to know programming.
    Incentivized
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    Microsoft
    • Helped save company money versus buying other stat software
    Incentivized
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    Open Source
    • Performance debugging was time consuming and mostly poorly automated exploratory process. Once we started use pandas for these tasks, it really moved the needle. Pandas are instrumental to provide actionable insights. As a result we were able to improve notably cloud software resource utilization and performance
    • Analytics implemented with pandas allow us to detect and. address problems in our APIs before they are notable to our customers
    Incentivized
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    ScreenShots

    IBM SPSS Statistics Screenshots

    Screenshot of SPSS Statistics Forecasting. This enables users to build time-series forecasts regardless of their skill level.Screenshot of SPSS Statistics Regression. These predict categorical outcomes and apply nonlinear regression procedures.Screenshot of IBM SPSS Statistics Neural Networks. These can discover complex relationships and improve predictive models.Screenshot of IBM SPSS Statistics Curated Help. These can interpret correlation output.Screenshot of IBM SPSS Statistics AI Output Assistant interprets statistical output in easy to consume language