Open Data Science

Agility to adopt new tools is critical to drive innovation and retain talent. Domino gives you this agility through flexible, sandboxed environments, helping you avoid excessive maintenance burden, operational inefficiency, and even regulatory risk of a “wild west” tool landscape.

Domino

Environments Overview

Experiment and reproduce without fear

Domino's Compute Environment Management capabilities let you create reusable, shareable, revisioned environments using Docker images under the hood. This gives data scientists agility to experiment with new tools, while providing security and reproducibility.

Compute Environments can easily integrate with narrower package management tools like Anaconda, while providing a more powerful superset of features.


Benefits of a Data Science Platform for Pharmaceutical Companies

Domino Analytics Distribution

A ready-to-use, always-up-to-date toolkit

Domino comes configured with the Domino Analytics Distribution, an optimized scientific computing stack for work in Python, R, Julia, and more. We are constantly integrating new tools based on market feedback. Minimize time configuring packages, maximize time experimenting and sharing insights. Need more flexibility? Customize the Docker container to suit your needs.


Correlation graph

Flexible visualization and reporting

Domino can serve reports and host dashboards using tools that are more flexible than traditional BI solutions. With tools like Knitr, D3, matplotlib, and Plotly, you can quickly create compelling visual experiences for your stakeholders. Our Publication Bridge lets you easily permission and measure consumption patterns. Plus, all of your work is versioned and easily reproducible, unlike with proprietary BI tools.


Spotlight for Open Data Science

The Best Way to Run Jupyter

The Best Way to Run Jupyter

SOLUTION

Multicore Data Science with R and Python

Multicore Data Science with R and Python

ARTICLE

Data Science in the Cloud

Data Science in the Cloud

WHITEPAPER


Make your business model-driven.

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