The latest machine learning, A.I., and data career topics from across both academia and industry are
Geoffrey Hinton and Sir Demis Hassabis: The Nobel Prize committee is an achievement of the highest o
Neuroscientist Bradley Voytek outlines to Jon Krohn the incredible use of data science and machine l
The citizen data scientist: Fact or fiction? Jon Krohn holds a conversation across episodes in this
Ritchie Vink, CEO and Co-Founder of Polars, Inc., speaks to Jon Krohn about the new achievements of
Next-gen IDEs, efficiency-boosting open-source Python libraries, and changes in hiring for data scie
Data contracts are redefining data quality and governance, and Chad Sanderson, CEO of Gable.ai, join
Llama 3.2 brings a new era of AI innovation with lightweight models tailored for on-device applicati
Virtual humans are rewriting the rules of digital communication and reshaping entire industries. Thi
NotebookLM, Google’s latest AI tool, takes content creation to a new level. This week, Jon Krohn sha
Marck Vaisman speaks to Jon Krohn about his paradigm for understanding core data practitioner types.
Jon Krohn takes OpenAI’s new models (o1-preview and o1-mini) for a spin in this Five-Minute Friday,
SuperDataScience veteran and Udemy teacher Luka Anicin is on the podcast to talk about his brand-new
Experts from AI and data science discuss the impact and benefits of decentralization, the importance
Dr. Julia Silge, Engineering Manager at Posit, introduces the brand-new Positron IDE, perfect for ex
Jon Krohn takes on a listener's challenge to explain his work in data science to his 94-year-old gra
Polars, Python, Narwhals, Rust, and Pandas: Marco Gorelli talks to Jon Krohn about the many ways to
As summer winds down, this episode shifts focus from the usual tech discussions to something more pe
Jerry Yurchisin from Gurobi joins Jon Krohn to break down mathematical optimization, showing why it
In this episode of Five-Minute Friday, Jon Krohn investigates published findings from the startup Sa
Nick Elprin talks to Jon Krohn about how and when to scale a data science team and its workflows to
