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Communicating data science, from academia to industry

Communicating data science, from academia to industry

For something as multifaceted and ill-defined as …

Linear Digressions

December 30, 201926m 15s

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Show Notes

For something as multifaceted and ill-defined as data science, communication and sharing best practices across the field can be extremely valuable but also extremely, well, multifaceted and ill-defined. That doesn’t bother our guest today, Prof. Xiao-Li Meng of the Harvard statistics department, who is leading an effort to start an open-access Data Science Review journal in the model of the Harvard Business Review or Law Review. This episode features Xiao-Li talking about the need he sees for a central gathering place for data scientists in academia, industry, and government to come together to learn from (and teach!) each other. Relevant links: https://hdsr.mitpress.mit.edu/

Topics

datasciencemachinelearninglineardigressions