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Data Cleanup: Are We Done Yet?

Data cleanup takes a long time, especially if you’re looking to create visual analytics. Learn how to do data prep at a faster, more simplified rate with the right wrangling tool.

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Self-Serve Data Prep for Machine Learning

Machine learning is a popular form of business analytics. With advancements in data collection, as well as progressive tools that allow ease of access to that data, robust models can cultivate information on newly minted datasets that help business analysts make strategic decisions based on the data that they have.

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Segmentation: The Potential of Higher Predictive Power

If you build models on your data, you’ve probably considered segmenting your modeling effort. A segmented approach to modeling involves building different models for different subgroups. The results can dramatically improve how well you capture the unique behaviors of your population.

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Supporting Women in Data Science

As a way of celebrating and supporting the “Women in Data Science” series, Senior Data Scientist Caitlin Hudon share a few ways that some of the “good guys in tech” have had an impact on her career in the hopes that others can follow their example.

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Data Democratization: Exploring the Possibilities

The buzz-phrase “Data Democratization” came to the forefront for us here at Rapid Insight when recently we led a round-table discussion at an annual conference. Seeing the response to that discussion, I ran the phrase by some customers, some colleagues, and some connections I’ve made to get their reactions too. I received a wide variety of responses. I’m more convinced now than I was at the start- data democratization is worth some thought, wherever you sit in the data-usage spectrum.

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Recap of the #RapidInsight2018 Conference

As we all adjust back to regular office life after the amazing 2018 Rapid Insight User Conference in Portland, Maine, we want to continue to celebrate how much a coming together like that means to our company. It was an inspiration to see the positive energy coming from everyone!

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