Making Agile work for data science

Ryan welcomes Prakash Chandran, CEO and co-founder of Xano, to the show to discuss the intricate relationship between frontend and backend development, the potential challenges that universal frontend interfaces pose for developers, and the importance of understanding both your f ...  Show more

From the floor at AWS re:Invent, Ryan is joined by AWS Senior Principal Engineer David Yanacek to chat about all things AWS, from the truth behind AWS’s Black Friday origin mythos to the development of essential cloud tools like SQS and DynamoDB. Plus, how David envisions autonom ...  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 ...