Data Science Rabbit Podcast

Dive into the chaos of data science with Michael Bagalman as he tackles burnout, resume blunders, and A/B testing nightmares—all served with a side of snark. Whether you're drowning in datasets or just looking for a laugh, this episode has you covered. Stay curious, stay sane, and remember: even data nerds need a break.

Join host Michael Bagalman on the Data Science Rabbit Podcast for a witty dive into the world of data science. This episode explores teaching marketers data science vs. improving data scientists' communication skills, offers a practical checklist (from none other than the data scientist nonpareil, Cornelius P. Snarkington) for ethical predictive modeling, and features a "comedy" bit about being a seasoned data scientist. Expect sharp insights, edgy humor, and relatable stories from the data trenches.

Welcome to another insightful episode of the Data Science Rabbit Podcast, the official podcast of the Data Science Rabbit Hole, hosted by Michael Bagalman. Today, we dive deep into the intriguing data science behind human body temperature. Join us as we debunk the long-held belief that 98.6°F is the "normal" body temperature and explore how modern technology and data science are reshaping our understanding of health.

We'll also share a humorous yet insightful sponsor message about P-Values, discuss the importance of deep understanding and clear communication in the digital age, and let the data scientist nonpareil, Cornelius P. Snarkington, reflect on a wise quote from Doctor Who about the dangers of altering facts to fit views.

Whether you're a data science enthusiast or simply curious about how data can improve our health, this episode is for you. Tune in and join us as we fall down the rabbit hole into the fascinating world of data science and human body temperature!

Join host Michael Bagalman as he dives down the Data Science Rabbit Hole to explore the latest developments in training large language models. In this episode, we discuss a recent paper from Meta and NYU that proposes letting AI models sculpt themselves through self-rewarding learning, rather than relying solely on human feedback. We ponder the implications of AI systems becoming their own harshest critics and the balance between human creation and potential hubris.

The episode also features tips on making data visualization more engaging by providing context relevant to your audience and using interactive dashboards. Finally, we close with a lighthearted AI-themed graduation speech I wrote last year, inspired by Mary Schmich's famous essay that is commonly referred to as "Wear Sunscreen".

Tune in for thought-provoking insights on the future of AI!

In this episode, host Michael Bagalman shares his experiences at Sony Music and discusses the importance of building a strong data culture within organizations. He provides a guide to help you start fostering a data-driven mindset in your company.

Resident spreadsheet sorceress, Clarissa Snarkington, takes the stage to share her love for spreadsheets and offers tips on how to level up your skills to become a true data wizard. She also delves into the history of spreadsheets, including the revolutionary Lotus Improv.

Finally, Michael challenges the notion that data scientists must always be meticulous and provides insights on when it's okay to trust your instincts and "wing it," sharing a personal anecdote to highlight the value of experience and expertise.

Tune in for practical advice, historical tidbits, and a fresh perspective on the world of data science. Subscribe and join the conversation on social media using #DataScienceRabbitHole!

Welcome to Episode 004 of the Data Science Rabbit Podcast, where we're spelunking into the rabbit hole of data science to learn about the median.

Let's learn about the median's history and its practical application in guessing the weight of a cow! Subscribe now to the Data Science Rabbit Podcast and embark on this captivating journey with us.