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Resources about the purpose and the importance of a data science platform for businesses, including examples of various platforms like Continuum Analytics, Adatao and more.
Table of Contents:
Below we have compiled publicly available sources from around the world that present views on Data Science Platforms.

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Perspectives on Data Science Platforms

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Specific Environments

Tools

Further Reading

  • Arimo - Adatao — Resources about Arimo (formerly known as Adatao), a company that offers an interface that enables business users, data scientists, developers, and data engineers to collaborate and present insights from big data.
  • Algorithmia — Resources about Algorithmia, a company that provides a marketplace that enables algorithm developer to explore, create, and share algorithms as a web services.
  • Context Relevant — Resources about Context Relevant, a big data analytics startup that sells on-premises software, cloud services, and professional service solutions to help businesses accelerate analysis and actionable insight.
  • Continuum Analytics — Resources about Continuum Analytics, the creator and driving force behind Anaconda, the leading Open Data Science platform powered by Python.
  • Dataiku — Resources about Dataiku, a company that develops collaborative data science software marketed for big data.
  • Domino Data Lab — Resources about Domino Data Lab, a company that provides data science teams with best practice knowledge management, reproducibility,rapid development and deployment of models.
  • Mode Analytics — Resources about Mode Analytics,  a cloud service that data analysts can use to query and visualize data.
  • Plotly — Resources about Plotly, a service for creating and sharing data visualizations that also offers statistical analysis tools and a robust API with the ability to graph custom functions and a built-in Python shell.
  • Nutonian — Resources about Nutonian, a data mining software package. It offers Eureqa Desktop, a technology solution that uncovers the intrinsic relationships hidden within complex data in oil and gas, life sciences, and retail industries.
  • Yhat — Resources about Yhat, a cloud solution that allows users to embed predictive models written in Python and R into various software applications.

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