Making Agile work for data science

Ryan welcomes Jason Williams, senior software engineer at Bloomberg and the creator of Rust-based JavaScript engine Boa, to the show to dive into why date and time handling in JavaScript is so difficult and how the Temporal proposal aims to fix it. They explore the current flaws ...  Show more

Ryan welcomes Harsha Chintalapani, co-founder and CTO at Collate and co-creator of Open Metadata, to the show to discuss why AI and LLMs struggle with real-time, structured production data. They explore how schema changes, inconsistent definitions (like “customer”), and weak gove ...  Show more

The most common application for data science is to solve problems within your own organization, and as professionals become more data literate, they rely less and less on others to solve their problems and unlock professional growth and career advancement.But in the world of cons ...  Show more

We revisit the 2018 Microsoft Build in this episode, focusing on the latest ideas in DevOps. Kyle interviews Cloud Developer Advocates Damien Brady, Paige Bailey, and Donovan Brown to talk about DevOps and data science and databases.

For a data scientist, what does it e ...

“Garbage in, garbage out.” It’s a philosophy every data leader is familiar with. Your algorithms and models are only as good as the data you put in them -- so how do you ensure the data you are leveraging is reliable and trustworthy? 

Joining Cindi today is Opendoor Co-f ...

The data science field is expanding because so many businesses and other institutions require skilled workers who can manage data as well as provide insights. Companies and students are clamoring for more academic programs. There is great need, but academic institutions are st ...