January 2016 | Civis Analytics

Month January 2016

Q&A with Discovery on how they use Civis Media Optimizer

Q&A with Discovery on how they use Civis Media Optimizer

by Maura Foley

We recently launched the Civis Media Optimizer — bringing the precision of digital to the scale of TV. But the proof is in the execution. So I want to provide a glimpse into the work through the eyes of Discovery Communications, one of our customers using Civis Media Optimizer. In a time when television consumption is changing, it’s even more...

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Data Science on State of the Union Addresses: Obama (2016) vs. Obama (2015) vs. … vs. George Washington (1790)

Data Science on State of the Union Addresses: Obama (2016) vs. Obama (2015) vs. … vs. George Washington (1790)

by Michael Heilman

Barack Obama recently gave his final State of the Union address, and since we’re interested in analyzing text data at Civis Analytics, I figured I ought to see if I could discover anything interesting. Rather than trying to understand the conversation on social media as we’ve done in previous work, I decided to take a somewhat longer view, comparing the...

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Connect the Civis Platform to Google Sheets: Let Your Drive Be Part of Your Data-Driven Culture

Connect the Civis Platform to Google Sheets: Let Your Drive Be Part of Your Data-Driven Culture

by Civis Analytics

Civis Analytics helps organizations across sectors use data science to improve outcomes. While working across multiple engagements and sectors, we’ve determined the most successful organizations invite their entire team to participate in building a data-driven culture by setting every employee’s sights on central metrics. Many of these successful organizations complement big data with Google Sheets, as they allow employees outside...

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Workflows in Python: Using Pipeline and GridSearchCV for More Compact and Comprehensive Code

Workflows in Python: Using Pipeline and GridSearchCV for More Compact and Comprehensive Code

by Katie Malone

The last two posts in this series have been about getting a data science analysis quickly up and running, and then circling back to improve it or understand the patterns I find, for example, which algorithms are working best and why. The upshot was a better handle on my workflow, but I’m left with a lot of free parameters of...

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