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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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Civis Bookshelf: Fairness Modeling, Creating a Hurricane Graph & More

Civis Bookshelf: Fairness Modeling, Creating a Hurricane Graph & More

by Henry H.

This post is part of our Bookshelf series organized by the Data Science R&D department at Civis Analytics. In this series, Civis data scientists share links to interesting software tools, blog posts, scientific articles, and other things that they have read about recently, along with a little commentary about why these things are worth checking out. Are you reading anything...

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The People Behind the People Science: Intern Edition

The People Behind the People Science: Intern Edition

by Nollie B.

Summer in Chicago means warm weather, food trucks, patio season, and a festival for every occasion. At Civis, summer means welcoming a handful of talented students from across the country. Over the course of 10 weeks, Civis’ summer interns work closely with a mentor in various departments, like Applied Data Science, Research and Development, Product, and Engineering. While here, they...

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Civis Bookshelf: Modeling to predict sarcasm with emoji

by Keith I.

This post is part of our Bookshelf series organized by the Data Science R&D department at Civis Analytics. In this series, Civis data scientists share links to interesting software tools, blog posts, scientific articles, and other things that they have read about recently, along with a little commentary about why these things are worth checking out. Are you reading anything...

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Q&A with the JDRF Illinois Chapter: Using Data to Diversify and Grow the Nonprofit’s Supporter Base

by Ola T.

As part of the nonprofit team at Civis, I have the opportunity to help mission-driven organizations work more effectively using data. Working with the JDRF Illinois Chapter was no different. JDRF is the leading global organization funding type 1 diabetes research, with a mission to accelerate life-changing breakthroughs to cure, prevent and treat T1D and its complications. JDRF’s grassroots approach...

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Civis Bookshelf: What’s Growing Faster — Python or R?

by Peter S.

This post is part of our Bookshelf series organized by the Data Science R&D department at Civis Analytics. In this series, Civis data scientists share links to interesting software tools, blog posts, scientific articles, and other things that they have read about recently, along with a little commentary about why these things are worth checking out. Are you reading anything...

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Fairness in Data Science

by Henry H.

Here at Civis, we build a lot of models. Most of the time we’re modeling people and their behavior because that’s what we’re particularly good at, but we’re hardly the only ones doing this — as we enter the age of “big data” more and more industries are applying machine learning techniques to drive person-level decision-making. This comes with exciting...

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Civis Data Science R&D Bookshelf

by Michael H.

This post is part of a new series from the Data Science R&D department at Civis Analytics. In this series, a Civis data scientist will share some links to interesting software tools, blog posts, scientific articles, and other things that he or she has read about recently, along with a little commentary about why these things are worth checking out....

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More Data More Problems: Variable Selection with Multiple Response Variables

by Sam W.

More data isn’t always better! This post will go over why and how we removed uninformative variables from a modeling dataset using a custom-built neural network architecture along with cross-checks using more traditional supervised learning algorithms. The end result is a better curated dataset for our model-building process. The Problem This is kind of weird, right? All you hear about...

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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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