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Resources for data scientists and business leaders.

  • Marketers and COVID-19: Tapping Into The Data You Need to Steer The Ship in Uncertain Times

    Marketers and COVID-19: Tapping Into The Data You Need to Steer The Ship in Uncertain Times

    In these increasingly uncertain times, marketing leaders who start thinking data science-driven will not only stay ahead of the pack but also keep their organizations afloat.

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  • COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

    COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

    For lenders, pre-COVID-19 data is no longer useful in a post-COVID-19 world. Here's how we're giving our customers external data to ensure their risk models work in our new reality.

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  • Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

    Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

    It’s time to look outside of our own silos to external data and make sure that despite the crises we face, we can give our organizations the power to make it through. The post Tomorrow Comes Today –

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  • Explorium: Product Overview1:03

    Explorium: Product Overview

    Explorium is the future of data science. We enable you to automatically connect to thousands of data sources, distill the best features, and deploy the best models.

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  • COVID-19 Reset: 3 Critical Use Cases That Marketing Leaders Must Implement Today

    COVID-19 Reset: 3 Critical Use Cases That Marketing Leaders Must Implement Today

    The business landscape is rapidly changing, driven by shifting preferences and trends, and largely by the current crisis. How can marketers find the data they need to understand this new reality?

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  • Explorium for Business

    Explorium for Business

    No other platform does it all: automatically join your data with a broad range of datasets, extract and select the best set of features, and build production-ready models.

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  • Learn to turn the "art" of feature generation into a science.

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  • AI is Making BI Obsolete, and Machine Learning is Leading the Way

    AI is Making BI Obsolete, and Machine Learning is Leading the Way

    Why are we still hung up on BI? It’s time to embrace a paradigm that empowers us to make smarter, better predictions using real data with machine learning.

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  • How SMBs Can Boost Their Bottom Line With External Data

    How SMBs Can Boost Their Bottom Line With External Data

    There are some insights that can only come from the data you produce or collect in-house. Historical sales figures, for example. Foot traffic through your store. The post How SMBs Can Boost Their...

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  • Data Science Salon 2020 - The Next Frontier of Data Science: Automated Feature Generation19:15

    Data Science Salon 2020 - The Next Frontier of Data Science: Automated Feature Generation

    Dedy Kredo, Head of Customer Facing Data Science at Explorium, presents about augmented data and feature discovery at Data Science Salon Austin.

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  • Understanding and Handling Data and Concept Drift

    Understanding and Handling Data and Concept Drift

    Over time, ML models start to lose predictive power due to a concept known as model drift. How can you spot data and concept drift and avoid it? Read more.

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  • The Essential Guide to Feature Selection

    The Essential Guide to Feature Selection

    Feature selection is a key step in building powerful and interpretable machine learning models, but it’s also one of the easiest to get wrong.

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  • 5 Steps to Mine Hidden Gold Out of An External Data Source

    5 Steps to Mine Hidden Gold Out of An External Data Source

    Extracting gold from an external data source is all about data prep. Follow these five steps and you’ll be well on your way to creating golden enrichments.

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  • Feature Generation: The Next Frontier of Data Science

    Feature Generation: The Next Frontier of Data Science

    It's time to take feature generation - a subset of feature engineering - from an art to a science by opening up additional data sources to achieve breakthroughs in predictive models.

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  • Support and Coverage – Data Integration Metrics You Should Know

    Support and Coverage – Data Integration Metrics You Should Know

    Data enrichment is a crucial step in the modeling process that data scientists tend to overlook due to the difficulty in finding and utilizing external sources.

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  • How Data Scientists Can Get a Seat at the Strategy Table

    How Data Scientists Can Get a Seat at the Strategy Table

    How can you earn yourself a seat at the table when decisions are being made? It starts with making yourself accessible and invaluable. Read how you can get started now.

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  • How Much For The Set? How You Can Monetize Your Datasets

    How Much For The Set? How You Can Monetize Your Datasets

    Data science websites overflow with tips for organizing and handling your data. The question is, how can you take this clean data and monetize it?

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  • Data Scientists and Augmented Data Discovery: A Match Made in Heaven

    Data Scientists and Augmented Data Discovery: A Match Made in Heaven

    Data science automation has historically focused on hyperparameter tuning and model optimization but now it’s time to see how new tools can empower data scientists to use more and better data.

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  • It’s Official: Online Lenders Can No Longer Afford to Ignore Alternative Data

    It’s Official: Online Lenders Can No Longer Afford to Ignore Alternative Data

    You don’t know what you don’t know, as the old saying goes. But in the age of Big Data, you simply can’t afford to shrug off "unknown unknowns".

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  • Stay Inside The Lines: Coloring With Artificial Intelligence

    Stay Inside The Lines: Coloring With Artificial Intelligence

    Can you teach AI to color better than a human? Using a generative adversarial network we can certainly try.

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  • The Complete Guide For Data Acquisition

    The Complete Guide For Data Acquisition

    This complete guide breaks down data acquisition into six steps, including data provider due diligence and data provider tests to uplift your model's accuracy.

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