Making Agile work for data science

Ryan welcomes Anil Dash, writer and former Stack Overflow board member, back to the show to discuss how AI is not a magical technology, but rather the normal next step in computing’s evolution. They explore the importance of democratizing access to technology, the unique challeng ...  Show more

Ryan sits down with Corey Quinn, Chief Cloud Economist at Duckbill, at AWS re:Invent to get Corey’s patented snarky take on all the happenings from the conference. They discuss whether the AI agent hype is supported by actual buyers, how startups are faring as AWS focuses on larg ...  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 ...