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.

Welcome to Episode 003 of the Data Science Rabbit Podcast, where we're diving headfirst into the whirlwind of data science topics in our first Mishmash episode!

We kick off our mishmash with a heartfelt reflection on the power of unity, drawing inspiration from a timeless quote: "Conquer not just lands but hearts. The greatest empire is a united front." Next up, we take a whimsical journey through "A Day in the Life of a Data Scientist." Then we delve into the practical realm with advice on aligning data with business outcomes. But wait, there's more! Clarissa Snarkington unravels the secrets behind choosing the right Key Performance Indicators (KPIs), drawing parallels to the art of assembling the perfect outfit.

So whether you're a seasoned data scientist seeking fresh perspectives or a curious novice eager to explore the diverse landscape of data science, join us for a mishmash of insights, reflections, and advice. Subscribe now to the Data Science Rabbit Podcast and embark on this captivating journey with us.

Welcome to the Data Science Rabbit Podcast, where we dive deep into the mysteries of data science and emerge with newfound knowledge. Join your host Michael Bagalman as we embark on an enlightening journey through the history of the arithmetic mean.

From the insights of ancient Greek philosophers to the mathematical prowess of Indian kings, we'll unravel the historical threads that led to the development of this fundamental statistical tool. Along the way, we'll encounter pivotal figures like Jacob Kobel and Karl Friedrich Gauss, who helped refine our understanding of the mean and its applications.

We'll also delve into the debates surrounding the use of the mean, from Daniel Bernoulli's skepticism to William Stanley Jevons's groundbreaking insights into the nature of measurement and error.

Whether you're a seasoned data scientist or a curious listener eager to learn more about the history of mathematics, join us as we uncover the hidden depths of the arithmetic mean. Subscribe now to the Data Science Rabbit Podcast and join us on this captivating exploration into the heart of statistical reasoning.

Hop down the rabbit hole with your host Michael Bagalman and uncover the tangled roots from whence data science grew!

Journey back to the 60s statistics split that led pioneers like John Tukey to carve an applied path different from the mathematical ivory tower. Learn how the modern phrase "data science" first percolated abroad before reaching American shores. Trace tensions between data modeling and algorithmic camps, with outspoken statisticians like Leo Breiman calling for a paradigm shift. Follow data science’s emergence in tech titans like Google and explosive growth after the famed Netflix competition.

Despite current domination by computer scientists, see openings for statisticians and domain experts to yet claim a seat at the table in this ever-evolving field that extracts practical value from the exponential explosion of data.