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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Anaconda

    Score8.8 out of 10
    N/AAnaconda is an enterprise Python platform that provides access to open-source Python and R packages used in AI, data science, and machine learning. These enterprise-grade solutions are used by corporate, research, and academic institutions for competitive advantage and research.

    $0

    per month

    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

    Pricing
    AnacondaIBM SPSS Statistics
    Editions & Modules
    Free Tier
    $0
    per month
    Starter Tier
    $15
    per month per user
    Business
    $50
    per month per user
    Custom
    Contact Sales
    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
    Offerings
    Pricing Offerings
    AnacondaIBM SPSS Statistics
    Free Trial
    NoYes
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    YesNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    AnacondaIBM SPSS Statistics
    Considered Both Products
    Anaconda
    No answer on this topic
    IBM
    Chose IBM SPSS Statistics
    - It's very user-friendly.
    - It can be used from simple to advanced analytics.
    - It runs faster and smoother than other software.
    Incentivized
    Key User Insights
    Would buy again
    96%
    Would buy again
    24 Answers
    94%
    Would buy again
    49 Answers
    Delivers good value for the price
    100%
    Delivers good value for the price
    23 Answers
    91%
    Delivers good value for the price
    42 Answers
    Happy with the feature set
    100%
    Happy with the feature set
    25 Answers
    98%
    Happy with the feature set
    51 Answers
    Lived up to sales and marketing promises
    94%
    Lived up to sales and marketing promises
    15 Answers
    97%
    Lived up to sales and marketing promises
    34 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    21 Answers
    97%
    Implementation went as expected
    35 Answers
    Features
    AnacondaIBM SPSS Statistics
    Platform Connectivity
    Comparison of Platform Connectivity features of Anaconda and IBM SPSS Statistics
    Feature
    Anaconda
    9.3
    25 Ratings
    11% above category average
    IBM SPSS Statistics
    -
    Ratings
    Connect to Multiple Data Sources9.822 Ratings00 Ratings
    Extend Existing Data Sources8.024 Ratings00 Ratings
    Automatic Data Format Detection9.721 Ratings00 Ratings
    MDM Integration9.614 Ratings00 Ratings
    Data Exploration
    Comparison of Data Exploration features of Anaconda and IBM SPSS Statistics
    Feature
    Anaconda
    8.5
    25 Ratings
    1% above category average
    IBM SPSS Statistics
    -
    Ratings
    Visualization9.025 Ratings00 Ratings
    Interactive Data Analysis8.024 Ratings00 Ratings
    Data Preparation
    Comparison of Data Preparation features of Anaconda and IBM SPSS Statistics
    Feature
    Anaconda
    9.0
    26 Ratings
    9% above category average
    IBM SPSS Statistics
    -
    Ratings
    Interactive Data Cleaning and Enrichment8.823 Ratings00 Ratings
    Data Transformations8.026 Ratings00 Ratings
    Data Encryption9.719 Ratings00 Ratings
    Built-in Processors9.620 Ratings00 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of Anaconda and IBM SPSS Statistics
    Feature
    Anaconda
    9.2
    24 Ratings
    8% above category average
    IBM SPSS Statistics
    -
    Ratings
    Multiple Model Development Languages and Tools9.023 Ratings00 Ratings
    Automated Machine Learning8.921 Ratings00 Ratings
    Single platform for multiple model development10.024 Ratings00 Ratings
    Self-Service Model Delivery9.019 Ratings00 Ratings
    Model Deployment
    Comparison of Model Deployment features of Anaconda and IBM SPSS Statistics
    Feature
    Anaconda
    9.5
    21 Ratings
    10% above category average
    IBM SPSS Statistics
    -
    Ratings
    Flexible Model Publishing Options10.021 Ratings00 Ratings
    Security, Governance, and Cost Controls9.020 Ratings00 Ratings
    Best Alternatives
    AnacondaIBM SPSS Statistics
    Small Businesses
    RapidMiner
    Score8.9 out of 10
    No answers on this topic
    Medium-sized Companies
    Jupyter Notebook
    Score8.6 out of 10
    Alteryx Platform
    Score9 out of 10
    Enterprises
    IBM Watson Studio
    Score10 out of 10
    Alteryx Platform
    Score9 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    AnacondaIBM SPSS Statistics
    Likelihood to Recommend
    10.0
    (38 ratings)
    8.5
    (116 ratings)
    Likelihood to Renew
    7.0
    (1 ratings)
    8.5
    (23 ratings)
    Usability
    9.0
    (3 ratings)
    8.0
    (15 ratings)
    Availability
    -
    (0 ratings)
    6.0
    (1 ratings)
    Performance
    -
    (0 ratings)
    6.0
    (1 ratings)
    Support Rating
    8.9
    (9 ratings)
    6.4
    (12 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.7
    (7 ratings)
    Configurability
    -
    (0 ratings)
    5.0
    (1 ratings)
    Ease of integration
    -
    (0 ratings)
    5.0
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    5.0
    (1 ratings)
    Vendor post-sale
    -
    (0 ratings)
    5.0
    (1 ratings)
    Vendor pre-sale
    -
    (0 ratings)
    5.0
    (1 ratings)
    User Testimonials
    AnacondaIBM SPSS Statistics
    Likelihood to Recommend
    Anaconda
    I have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.
    Incentivized
    Read full review
    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
    Pros
    Anaconda
    • Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
    • Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
    • Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.
    Incentivized
    Read full review
    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
    Cons
    Anaconda
    • It can have a cloud interface to store the work.
    • Compatible for large size files.
    • I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.
    Incentivized
    Read full review
    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
    Likelihood to Renew
    Anaconda
    It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
    Incentivized
    Read full review
    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
    Usability
    Anaconda
    I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.
    Incentivized
    Read full review
    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
    Reliability and Availability
    Anaconda
    No answers on this topic
    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
    Read full review
    Performance
    Anaconda
    No answers on this topic
    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
    Support Rating
    Anaconda
    Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.
    Incentivized
    Read full review
    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
    Implementation Rating
    Anaconda
    No answers on this topic
    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
    Read full review
    Alternatives Considered
    Anaconda
    I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!
    Incentivized
    Read full review
    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
    Scalability
    Anaconda
    No answers on this topic
    IBM
    I am neutral because I have not had to look into scalability since I am using as a student.
    Incentivized
    Read full review
    Return on Investment
    Anaconda
    • It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
    • By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
    • There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.
    Incentivized
    Read full review
    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
    Read full review
    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