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Multiple languages, one team: Bringing R and Python together with Civis Platform

by Michael H.

What follows is a brief example of how the Civis Platform enables data scientists to collaborate effectively. Data scientists work in unique contexts and use different programming languages, even within the same organization, making collaboration a constant challenge. Consider two data scientists, whom I’ll call Alice and Bob. Alice and Bob need to work together on a project with a...

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Civis Data: The powerful dataset built right into Civis Platform

Civis Data: The powerful dataset built right into Civis Platform

by Wade J.

I work alongside data scientists every day, so I know that data science teams can help their businesses thrive if they have the right tools and the best, most complete data. That’s why I’m happy to share that Civis Platform comes with a powerful dataset built right in. With accurate, predictive data included, Civis Data helps organizations learn more about...

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You’ve built it, now automate it with Workflows in Civis Platform

by Lori E.

Today we’re launching Workflows in Civis Platform! This new feature is a data science automation tool that enables data scientists to cross small, manual tasks off their daily to-do lists so they can focus on what they do best: help decision makers see the big picture and make informed decisions. Workflows allow piecewise development, testing, and deployment of data science...

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Moving from exploration to production with Notebooks and Scripts

by Marshall M.

When it comes to data science code, there are two sides to the story. There’s the part where you do the exploratory science — and there’s the part where you put it in production. Last month, we announced that Civis Platform now allows data scientists to work in Jupyter notebooks, which is all about making data science exploration scalable, shareable,...

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From Science to Production: Unleash your Jupyter Notebooks

From Science to Production: Unleash your Jupyter Notebooks

by Lori E.

Data scientists are explorers. They use Jupyter Notebooks, one of the most popular environments for data science analysis, to begin work toward creative solutions to big problems. But once those solutions are discovered…what’s the next step? In order for data scientists to make a major impact, the creativity that starts in notebooks needs to find its way out to the...

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