Skip to content
    Latest

    How To Make Data-Driven Predictions with Predictive Modeling

    When you hear words like machine learning (ML) or artificial intelligence (AI), one of the first things that comes to mind is correctly predicting future occurrences or...

    Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

    In this article, we'll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot...

    Item Response Theory in R for Survey Analysis

    In this guest blog post, Derrick Higgins, of American Family Insurance, covers item response theory (IRT) and how data scientists can apply it within...

    Model Deployment Powered by Kubernetes

    In this article we explain how we’re using Kubernetes to enable data scientists to deploy predictive models as production-grade APIs. Background ...

    Benchmarking Predictive Models

    It's been said that debugging is harder than programming. If we, as data scientists, are developing models ("programming") at the limits of our...

    Reflections on "Buy vs Build" for Data Science Tools

    “Buy vs build”, “not-invented-here syndrome” and even “invented-here-syndrome” have been written about extensively. I want to share a few reflections...

    Subscribe to the Data Science Blog

    Receive data science tips and tutorials from leading Data Scientists right to your inbox.