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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Jupyter Notebook

    Score8.6 out of 10
    N/AJupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports over 40 programming languages, and notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. It is used with JupyterLab, a web-based IDE for…N/A

    SAS Enterprise Miner

    Score9 out of 10
    N/ASAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.N/A
    Pricing
    Jupyter NotebookSAS Enterprise Miner
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    Jupyter NotebookSAS Enterprise Miner
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    Jupyter NotebookSAS Enterprise Miner
    Considered Both Products
    Open Source
    No answer on this topic
    SAS
    No answer on this topic
    Key User Insights
    Would buy again
    100%
    Would buy again
    23 Answers
    No answers on this topic
    Delivers good value for the price
    100%
    Delivers good value for the price
    23 Answers
    No answers on this topic
    Happy with the feature set
    96%
    Happy with the feature set
    22 Answers
    No answers on this topic
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    17 Answers
    No answers on this topic
    Implementation went as expected
    95%
    Implementation went as expected
    20 Answers
    No answers on this topic
    Features
    Jupyter NotebookSAS Enterprise Miner
    Platform Connectivity
    Comparison of Platform Connectivity features of Jupyter Notebook and SAS Enterprise Miner
    Feature
    Jupyter Notebook
    9.0
    22 Ratings
    7% above category average
    SAS Enterprise Miner
    8.8
    4 Ratings
    5% above category average
    Connect to Multiple Data Sources10.022 Ratings8.14 Ratings
    Extend Existing Data Sources10.021 Ratings9.04 Ratings
    Automatic Data Format Detection8.514 Ratings9.34 Ratings
    MDM Integration7.415 Ratings9.02 Ratings
    Data Exploration
    Comparison of Data Exploration features of Jupyter Notebook and SAS Enterprise Miner
    Feature
    Jupyter Notebook
    7.0
    22 Ratings
    18% below category average
    SAS Enterprise Miner
    8.1
    4 Ratings
    3% below category average
    Visualization6.022 Ratings7.14 Ratings
    Interactive Data Analysis8.022 Ratings9.14 Ratings
    Data Preparation
    Comparison of Data Preparation features of Jupyter Notebook and SAS Enterprise Miner
    Feature
    Jupyter Notebook
    9.5
    22 Ratings
    15% above category average
    SAS Enterprise Miner
    8.0
    4 Ratings
    3% below category average
    Interactive Data Cleaning and Enrichment10.021 Ratings7.84 Ratings
    Data Transformations10.022 Ratings8.24 Ratings
    Data Encryption8.514 Ratings8.12 Ratings
    Built-in Processors9.314 Ratings8.12 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of Jupyter Notebook and SAS Enterprise Miner
    Feature
    Jupyter Notebook
    9.3
    22 Ratings
    10% above category average
    SAS Enterprise Miner
    8.8
    4 Ratings
    4% above category average
    Multiple Model Development Languages and Tools10.021 Ratings7.54 Ratings
    Automated Machine Learning9.218 Ratings9.82 Ratings
    Single platform for multiple model development10.022 Ratings8.54 Ratings
    Self-Service Model Delivery8.020 Ratings9.23 Ratings
    Model Deployment
    Comparison of Model Deployment features of Jupyter Notebook and SAS Enterprise Miner
    Feature
    Jupyter Notebook
    10.0
    20 Ratings
    15% above category average
    SAS Enterprise Miner
    7.8
    4 Ratings
    9% below category average
    Flexible Model Publishing Options10.020 Ratings7.04 Ratings
    Security, Governance, and Cost Controls10.019 Ratings8.54 Ratings
    Best Alternatives
    Jupyter NotebookSAS Enterprise Miner
    Small Businesses
    RapidMiner
    Score8.9 out of 10
    RapidMiner
    Score8.9 out of 10
    Medium-sized Companies
    Anaconda
    Score8.8 out of 10
    Anaconda
    Score8.8 out of 10
    Enterprises
    IBM Watson Studio
    Score10 out of 10
    IBM Watson Studio
    Score10 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Jupyter NotebookSAS Enterprise Miner
    Likelihood to Recommend
    10.0
    (23 ratings)
    9.9
    (4 ratings)
    Usability
    10.0
    (2 ratings)
    -
    (0 ratings)
    Support Rating
    9.0
    (1 ratings)
    10.0
    (2 ratings)
    User Testimonials
    Jupyter NotebookSAS Enterprise Miner
    Likelihood to Recommend
    Open Source
    I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.
    Incentivized
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    SAS
    SAS Enterprise Miner is world-class software for individuals interested in developing reproducible models in a reasonable amount of time. Perhaps the most useful part of SAS Enterprise Miner is the ability to compare models with other models without writing code. The ensemble modeling capabilities is the easiest way to do ensemble modeling I have come across. SAS Enterprise Miner is well-suited for beginning to advanced analysts who know something about advanced analytics. The software is not well-suited for analysts or companies that have little interest in advanced modeling.
    Incentivized
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    Pros
    Open Source
    • Simple and elegant code writing ability. Easier to understand the code that way.
    • The ability to see the output after each step.
    • The ability to use ton of library functions in Python.
    • Easy-user friendly interface.
    Incentivized
    Read full review
    SAS
    • Enterprise Miner is really visual and lets you do a whole lot without actually going into the detailed options. For decent results, you should really explore the different advanced options though.
    • The recent versions of Miner allow users to use R code in Miner. You can then compare several models and approach to get the best performing model.
    • The resulting data is really well displayed and easy to understand (ex: the lift graph, score ranking, etc.)
    • Miner has the ability to integrate custom SAS code which allows the user to add functionalities that are specific to the project.
    Incentivized
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    Cons
    Open Source
    • Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
    • Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.
    Incentivized
    Read full review
    SAS
    • SAS is not as user friendly as other stats software.
    Incentivized
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    Usability
    Open Source
    Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.
    Incentivized
    Read full review
    SAS
    No answers on this topic
    Support Rating
    Open Source
    I haven't had a need to contact support. However, all required help is out there in public forums.
    Incentivized
    Read full review
    SAS
    SAS' customer support used to be non-existent many years ago. Today, contacting SAS customer support is great. They are responsible, knowledgable, and seem to have an interest in getting the results right the first time. With that said, Enterprise Miner's online support is weak, probably because the user base is much smaller than other tools.
    Incentivized
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    Alternatives Considered
    Open Source
    With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.
    Incentivized
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    SAS
    SAS EM has a very great set of machine learning and predictive analytics toolsets, which helped our organization achieve its goals. We used other tools, but for us, SAS EM was the most intuitive and easy to learn the tool and it provides greater data exploration and data preparation capabilities compared to the other tools we used.
    Incentivized
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    Return on Investment
    Open Source
    • Positive impact: flexible implementation on any OS, for many common software languages
    • Positive impact: straightforward duplication for adaptation of workflows for other projects
    • Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration
    Incentivized
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    SAS
    • In our organization, users were using SAS already so the learning curve was really low. Within a few weeks after the implementation, the users were already delivering models developed with SAS Enterprise Miner. It is difficult to talk about ROI as models were already being developed before. It was mostly a change of technology and it was a smooth transition.
    • Going with Enterprise Miner came with migration from desktop use of SAS to a server use of SAS. This created a new role of SAS administrator. This was obviously a cost but as the use of SAS increased greatly, it was expected.
    • From a methodology standpoint, Enterprise Miner helped greatly in the documentation of the model development which was a requirement in a few groups such as the risk groups. Having a visual "GUI-like" approach to development, the flowchart or diagram of the project in Miner was able to give users a good understanding of the approach the analyst took to develop the model.
    Incentivized
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    ScreenShots