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Anaconda

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Jovian

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Anaconda vs Jovian: What are the differences?

Anaconda: The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders. A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda; Jovian: Tooling and workflows built specifically for data science. It is a better place for your data science projects, Jupyter notebooks, machine learning models, experiment logs, results, and more.

Anaconda and Jovian can be categorized as "Data Science" tools.

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What is Anaconda?

A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.

What is Jovian?

It is a better place for your data science projects, Jupyter notebooks, machine learning models, experiment logs, results, and more.

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    What are some alternatives to Anaconda and Jovian?
    Python
    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
    PyCharm
    PyCharm’s smart code editor provides first-class support for Python, JavaScript, CoffeeScript, TypeScript, CSS, popular template languages and more. Take advantage of language-aware code completion, error detection, and on-the-fly code fixes!
    pip
    It is the package installer for Python. You can use pip to install packages from the Python Package Index and other indexes.
    Jupyter
    The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.
    NumPy
    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
    See all alternatives