This chapter discusses the ongoing preference for simpler models like decision trees and linear regression over cutting-edge models in data science due to their practicality and effectiveness in solving business questions efficiently. The speakers emphasize the importance of learning core concepts and avoiding unnecessary complexity, explore challenges with large language models, and highlight the necessity of defining precise scopes for data science projects to achieve optimal results.
I have a special episode for you this time around. We're coming to you live from PyCon 2024. I had the chance to sit down with some amazing people from the data science side of things: Jodie Burchell, Maria Jose Molina-Contreras, and Jessica Greene. We cover a whole set of recent topics from a data science perspective. Though we did have to cut the conversation a bit short as they were coming from and go to talks they were all giving but it was still a pretty deep conversation.
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