A big interest of mine is how to impart what little I know of the tools and techniques of data science to others. When I was at Booz Allen, I taught numerous classes both for internal staff and for various clients. I’ve also taught for Metis, O’Reilly Publishing and for the last three years, at BlackHat so I do have some experience in the matter. I’ve looked at MANY data science programs to see if what they are teaching lines up what I’m teaching and I’d like to share some things which I’ve noticed which will hopefully help you build a better data science program. My goal here is to share my mistakes and experiences over the years and hopefully if you are building a data science training program, you can learn from what I learned the hard way. I make no claims to be the perfect data science instructor, and I’ve made plenty of mistakes along the way.
While I’m at it, I’ll put in a plug for an upcoming data science class which I am teaching with Jay Jacobs of BitSight Security at the O’Reilly Security Conference in NYC, October 29-30th.
Really, data science instruction is an optimization problem: as an instructor, your goal is to minimize confusion whilst maximizing understanding. To do this, you must remove as many obstacles as possible from the students’ path which create dissonance. This may seem silly, but I have observed that if you have small errata in your code, or your code doesn’t work on their machine, even due to something they did, it significantly detracts from their learning experience and their opinion of you as an instructor. Therefore, removing all these obstacles to understanding is vital to your success as an instructor.