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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

    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
    Pricing
    AnacondaJupyter Notebook
    Editions & Modules
    Free Tier
    $0
    per month
    Starter Tier
    $15
    per month per user
    Business
    $50
    per month per user
    Custom
    Contact Sales
    No answers on this topic
    Offerings
    Pricing Offerings
    AnacondaJupyter Notebook
    Free Trial
    NoNo
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    YesNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details
    More Pricing Information
    Community Pulse
    AnacondaJupyter Notebook
    Considered Both Products
    Anaconda
    Chose Anaconda
    There are several reasons why Anaconda is better to use for me including that it is much easier to use than Baycharm. Also, the user interface is not as complicated as that of Baycharm. Even Anaconda does not slow down my device, using PaySharm slowed down my device in an …
    Incentivized
    Chose Anaconda
    In Anaconda, [it is easy] to find and install the required libraries. Here, we can work on multiple projects with different sets of the environment. [It is] easy to create the notebook for developing the ML model and deployment. Right now, it is the best data science version …
    Incentivized
    Chose Anaconda
    Free ware, better design ease of use
    Incentivized
    Chose Anaconda
    On top of all the software that I have used, Anaconda is the best because in Anaconda we have built-in packages that provide no headache to install packages and we can design a separate environment for different projects. Anaconda has versions made for special use cases. …
    Incentivized
    Chose Anaconda
    Some analyzed tools, such as PyCharm and Spyder, are simpler to use but still do not have all the libraries needed for those starting out in data science--or in institutions that need to grow in that direction. Anaconda is more robust but stable, more complete, and the …
    Incentivized
    Chose Anaconda
    If the project is not large scale then Jupiter notebooks or Visual Studio Code serve well. If you don't have any dependency on Python versions, these IDEs can be well suited for fast development and deployment.
    Incentivized
    Open Source
    Chose Jupyter Notebook
    Jupyter Notebook is the core feature extended on by many commercial alternatives. The commercial alternatives have more feature integration with the rest of their portfolio. RStudio is another competitor for interactive and literate programming.

    Incentivized
    Chose Jupyter Notebook
    haven't actually explored as I decided to use it on a friend 's recommendation.
    Incentivized
    Key User Insights
    Would buy again
    96%
    Would buy again
    24 Answers
    100%
    Would buy again
    23 Answers
    Delivers good value for the price
    100%
    Delivers good value for the price
    23 Answers
    100%
    Delivers good value for the price
    23 Answers
    Happy with the feature set
    100%
    Happy with the feature set
    25 Answers
    96%
    Happy with the feature set
    22 Answers
    Lived up to sales and marketing promises
    94%
    Lived up to sales and marketing promises
    15 Answers
    100%
    Lived up to sales and marketing promises
    17 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    21 Answers
    95%
    Implementation went as expected
    20 Answers
    Features
    AnacondaJupyter Notebook
    Platform Connectivity
    Comparison of Platform Connectivity features of Anaconda and Jupyter Notebook
    Feature
    Anaconda
    9.3
    25 Ratings
    11% above category average
    Jupyter Notebook
    9.0
    22 Ratings
    7% above category average
    Connect to Multiple Data Sources9.822 Ratings10.022 Ratings
    Extend Existing Data Sources8.024 Ratings10.021 Ratings
    Automatic Data Format Detection9.721 Ratings8.514 Ratings
    MDM Integration9.614 Ratings7.415 Ratings
    Data Exploration
    Comparison of Data Exploration features of Anaconda and Jupyter Notebook
    Feature
    Anaconda
    8.5
    25 Ratings
    1% above category average
    Jupyter Notebook
    7.0
    22 Ratings
    19% below category average
    Visualization9.025 Ratings6.022 Ratings
    Interactive Data Analysis8.024 Ratings8.022 Ratings
    Data Preparation
    Comparison of Data Preparation features of Anaconda and Jupyter Notebook
    Feature
    Anaconda
    9.0
    26 Ratings
    9% above category average
    Jupyter Notebook
    9.5
    22 Ratings
    14% above category average
    Interactive Data Cleaning and Enrichment8.823 Ratings10.021 Ratings
    Data Transformations8.026 Ratings10.022 Ratings
    Data Encryption9.719 Ratings8.514 Ratings
    Built-in Processors9.620 Ratings9.314 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of Anaconda and Jupyter Notebook
    Feature
    Anaconda
    9.2
    24 Ratings
    8% above category average
    Jupyter Notebook
    9.3
    22 Ratings
    9% above category average
    Multiple Model Development Languages and Tools9.023 Ratings10.021 Ratings
    Automated Machine Learning8.921 Ratings9.218 Ratings
    Single platform for multiple model development10.024 Ratings10.022 Ratings
    Self-Service Model Delivery9.019 Ratings8.020 Ratings
    Model Deployment
    Comparison of Model Deployment features of Anaconda and Jupyter Notebook
    Feature
    Anaconda
    9.5
    21 Ratings
    11% above category average
    Jupyter Notebook
    10.0
    20 Ratings
    16% above category average
    Flexible Model Publishing Options10.021 Ratings10.020 Ratings
    Security, Governance, and Cost Controls9.020 Ratings10.019 Ratings
    Best Alternatives
    AnacondaJupyter Notebook
    Small Businesses
    RapidMiner
    Score8.9 out of 10
    RapidMiner
    Score8.9 out of 10
    Medium-sized Companies
    Jupyter Notebook
    Score8.6 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
    AnacondaJupyter Notebook
    Likelihood to Recommend
    10.0
    (38 ratings)
    10.0
    (23 ratings)
    Likelihood to Renew
    7.0
    (1 ratings)
    -
    (0 ratings)
    Usability
    9.0
    (3 ratings)
    10.0
    (2 ratings)
    Support Rating
    8.9
    (9 ratings)
    9.0
    (1 ratings)
    User Testimonials
    AnacondaJupyter Notebook
    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
    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
    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
    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
    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
    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
    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
    Open Source
    No answers on this topic
    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
    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
    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
    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
    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
    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
    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
    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
    Read full review
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