Thinking on "Executive Data Science" Specialization

I love taking various online education resources to broaden my view and knowledge base. Recently, I finished the "Executive Data Science" specialization provided by Coursera, and found its quite helpful. Just write some thoughts on what I learned from this course.

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These series courses does not provide technical knowledge for people to become data scientist, but offer the insights and toolkits to lead a data science project and manage a data science team. Although its discussion is mostly focusing on "statistical analytics insights - data scientist" work setting, I think quite a few concepts can still be transferable if working in a "machine learning product - data scientist" environment.

The highlights in this specialization (for me) is following:
1. How to build a team: different focus and cooperation between "data engineer", "data scientist", and "business analyst". Although in reality, many times we play all three hats, it is nice to realize that intrinsically there are some difference so that to grow a team, we know what is the next stage hiring or knowledge sets required. 
2. How to manage project: basically, using statistical sense to identify the top priority and move agilely along the right direction. I think most of the discussions make common sense in data science region, and it really helps to get a summarize view about how to prioritize data science focus, identify the right talent to do that, and continuously monitor/guide the project to produce end-result.

Appreciate the faculties in Johns Hopkins to produce this great specialization. Happy learning!

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