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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 Civis Analytics

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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My First Week at Civis

by Bryan B.

There can be a lot of uncertainty when starting a new job. Whether it’s your first job or your fifth, there will always be anxiety and questions on the first day. What projects or clients will I be working on? Will I work well with my manager and my team? What’s the office environment like? Will I like my coworkers?...

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Civis Healthcare: The Rx For Healthcare Analytics

by Crystal S.

At Civis, we often find that a solution we’ve created for one industry can be adapted to solve a similar problem in another because foundationally, the data and statistical problems are drastically similar, even if the clients themselves are as different as night and day. From the outside, our data scientists can be considered generalists in that they may be...

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