“When do we start working on an open-ended question?”

This was a question I have gotten today when sharing with a large group of students on Data Science and Artificial Intelligence as a career.

Quite an interesting question and it got me thinking. As a data professional,  we are constantly experimenting with our learning as well.

We will only know what we do not know when we attempt to work on projects. And when we know our unknowns, we always start with reading about it but  it has to move to the next step, and that is application, to be able to make the learning “stick”.

We are constantly learning and how do we know we have learn “enough” is really through the application and  if we want to make the learning “stickier” is to actually teach it. The  more we teach it, the stickier the lesson will become. In addition, what I found is that the more I teach it, the better I get at explaining the concept, moving toward simpler terms or statements that layperson can understand.

TLDR: How do you know you learning enough and how to make it the learning stick? Apply, share and teach! :)

What are your thoughts? :)

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