Transparent data science, profitable AI, and what’s missing from a data science education: Pandata’s Data Scientist in Residence Keith McCormick and Jon Krohn discuss how “insights” can never be the end product of a data science project, how to ensure you have a specific goal at the start of a project that is related to revenue, and why there is so much miscommunication between data scientists and their clients. Exclude the C-suite at your peril!This episode is brought to you by Glean (glean.io), the platform for data insights, fast. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.In this episode you will learn:• What an Executive Data Scientist in Residence is [05:27]• What A.I. transparency is and how it relates to the field of Explainable A.I. (XAI) [17:34]• How companies can ensure they profit from AI projects [36:47]• Possible organization structures for data science teams to be profitable [1:02:41]• The current gaps in data science education [1:09:58]
Additional materials: www.superdatascience.com/655

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