Everand Logo

Welcome to Everand!

  • Discover this podcast and so much more

    Podcasts are free to enjoy without a subscription. We also offer ebooks, audiobooks, and so much more for just $11.99/month.

    790: Open-Source Libraries for Data Science at the New York R Conference

    790: Open-Source Libraries for Data Science at the New York R Conference

    FromSuper Data Science: ML & AI Podcast with Jon Krohn

    Start listeningView podcast show

    790: Open-Source Libraries for Data Science at the New York R Conference

    FromSuper Data Science: ML & AI Podcast with Jon Krohn

    ratings:
    Length:
    7 minutes
    Released:
    Jun 7, 2024
    Format:
    Podcast episode

    Description

    The experts reveal their top open-source R libraries with us live from the New York R Conference! This Super Data Science Podcast episode features an exclusive panel with data science trailblazers Drew Conway, Jared Lander, Emily Zabor, and JD Long. They share their favorite R libraries and valuable insights to enhance your data science practice.

    Additional materials: www.superdatascience.com/790

    Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.
    Released:
    Jun 7, 2024
    Format:
    Podcast episode

    Titles in the series (77)

    The Super Data Science podcast with Jon Krohn brings you the latest and most important machine learning, artificial intelligence, and broader data-world topics from across both academia and industry. As the quantity of data on our planet doubles every couple of years and this trend is set to continue for decades to come, there's an unprecedented opportunity for you to make an enormous impact in your lifetime. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, and commercialization − everything you need to crush it with data science.