For better or worse, something called “Data Science” is a big deal. Whatever it is seems to be co-opting Statistics as a mere piece, maybe even not so important, of a new and larger discipline. The purported reason that this is more than just Statistics is the size of data, but that’s nonsense because sometimes Statistics has grappled with large data in the past.
Since 2015, I had the privilege of teaching many data science students who are full with research goals to transform the future in a variety of settings: K-12 education, academia, healthcare, automotive industry, environmental protection, cognitive science, and social science. Having instructed 200+ students in several Python programming, mathematics and statistics courses at the University of Michigan as a graduate student instructor, students graciously told me about how they approach the challenge of disseminating data-driven knowledge publicly at low cost and with high interactivity.
Data science now encompasses a superfluous variety of technologies and techniques. They are, but not limited to, visualization, data manipulation, modeling, version control, storytelling, data elicitation and system administration. The advancement of computation technology has allowed:
As my research goal lies in personalized learning at scale, I am exploring effective and efficient ways of augmenting data science education at scale through human intelligence, adaptive pedagogy and literate programming approaches.
