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Episode 17: End-to-End Data Science

1:16:04
 
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Manage episode 355618614 series 3317544
Content provided by Hugo Bowne-Anderson. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Hugo Bowne-Anderson or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

Hugo speaks with Tanya Cashorali, a data scientist and consultant that helps businesses get the most out of data, about what end-to-end data science looks like across many industries, such as retail, defense, biotech, and sports, including

  • scoping out projects,
  • figuring out the correct questions to ask,
  • how projects can change,
  • delivering on the promise,
  • the importance of rapid prototyping,
  • what it means to put models in production, and
  • how to measure success.

And much more, all the while grounding their conversation in real-world examples from data science, business, and life.

In a world where most organizations think they need AI and yet 10-15% of data science actually involves model building, it’s time to get real about how data science and machine learning actually deliver value!

LINKS

  continue reading

47 episodes

Artwork
iconShare
 
Manage episode 355618614 series 3317544
Content provided by Hugo Bowne-Anderson. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Hugo Bowne-Anderson or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

Hugo speaks with Tanya Cashorali, a data scientist and consultant that helps businesses get the most out of data, about what end-to-end data science looks like across many industries, such as retail, defense, biotech, and sports, including

  • scoping out projects,
  • figuring out the correct questions to ask,
  • how projects can change,
  • delivering on the promise,
  • the importance of rapid prototyping,
  • what it means to put models in production, and
  • how to measure success.

And much more, all the while grounding their conversation in real-world examples from data science, business, and life.

In a world where most organizations think they need AI and yet 10-15% of data science actually involves model building, it’s time to get real about how data science and machine learning actually deliver value!

LINKS

  continue reading

47 episodes

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