What data scientists need to know but don't learn in school

From the "655: AI ROI: How to get a profitable return on an AI-project investment" in which Pandata’s Data Scientist in Residence Keith McCormick and ‪@JonKrohnLearns‬ 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.
Watch, listen to, or read the full episode at www.superdatas...

[@JMVATC]

Could not agree more. I am so grateful for Penn State’s MPS in Data Analytics including a capstone course for this. Groups of students source their own data set, create a fictitious business problem, and present their status at various stages long the journey.

[@JonKrohnLearns]

Sounds great, Jeremy!

[@ahmadvarasteh641]

Such a great episode. I learned a lot.

[@JonKrohnLearns]

Yeah, absolutely brilliant guest. Glad you learned a lot too, Ahmad!

[@user-wr4yl7tx3w]

Do software engineers report to product managers?

[@JonKrohnLearns]

Great question! Not usually. Usually software engineers report into engineering leads. In many organizations the engineering leads and the product managers then separately report into a VP of Engineering or a CTO.