715: Make Better Decisions with Data, with Dr. Allen Downey

Join us as Dr. Allen Downey, renowned author and professor, shares insights from his upcoming book 'Probably Overthinking It,' breaking down underused techniques like Survival Analysis, explaining common paradoxes, and discussing the dynamic Overton Window. This episode is brought to you by the Zerve data science dev environment (https://zerve.ai), by Modelbit (https://modelbit.com), for deploying models in seconds, and by Grafbase (https://grafbase.com), the unified data layer. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • Why interpreting data is not always easy [06:21] • What is Survival Analysis [15:32] • Preston's Paradox [22:09] • Are you Normal? [36:52] • How to better prepare for rare “Black Swan” events [42:48] • What is an Overton Window? [53:06] • What is the base rate fallacy? [1:23:31] • How to protect yourself from biased samples [1:33:39] • Simpson’s Paradox [1:42:43] Additional materials: www.superdatascience.com/715

Om Podcasten

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. 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, commercialization, and entrepreneurship − everything you need to crush it with data science.