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Short version: Philip Guo and I wrote a paper about challenges facing data science instructors for the 2019 CHI conference.

Last year I had the privilege of talking to twenty data scientists who also teach their craft in a variety of settings: big and small courses, online and in person, and in academia and industry. They graciously told me about how they approach the challenge of teaching such a broad and rapidly-changing field. Data science now encompasses an overwhelming number of technologies and techniques including modeling, visualization, data wrangling, storytelling, version control, and system administration. How can they possibly keep a handle on everything? For all of the juicy details see the paper linked above. Below are the key points from our investigation:

The purpose of this investigation is to highlight these challenges for the human-computer interaction community, as a call to action to study the quickly growing field of data science education. It is my belief that in the near future data analytic programming will become the most common and in-demand form of programming. Since data science is becoming integrated into many other fields, it is imperative that we pay attention to the needs of this community so that we can expand learning opportunities and systematize best practices for data science educators.