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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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2018 AIR Forum

At the AIR Forum in Orlando, I led a facilitated discussion on the meaning of the term “democratized data”. Here are some of the insights I gained and also a chance for you to weigh in on the discussion. My goal is to get more people thinking about this, since it’s really the most important aspect of data-work. Watched pots never boil, and uncommunicated data never leads to change! Let us know what you envision when you hear “democratized data”, regardless of your background and experience working with data.

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Black Swan Events and the Importance of being Data-Driven in Higher Ed

The consequences of black swan events are troublesome for analysts because the events themselves are extremely difficult to predict, they cause ripple effects elsewhere in a system, and they leave a legacy that must be accounted for when future observations are made of those systems. Since these events, by their definition, lie outside the realm of regular expectations, it’s not only difficult to gauge their likelihood of occurrence, but it’s also difficult to quantify the size and scope of the impact across the system.

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