Instructor Q&A: Keith McCormick, Predictive Analytics

Q. Why did you decide to become an instructor?

A. I guess I've always been involved with and enjoyed teaching. Even in high school and college I did quite a bit of tutoring. Immediately after obtaining my degree, I ran a small test prep business with nearly a dozen employees, one of whom was a sport psychologist who lectured on test anxiety. I also nearly pursued an academic career, but in the late nineties there were no advanced degree programs in Data Science, the career that I stumbled into. A couple of years ago, UCI needed a highly customized course using software that I was expert in. The project went very well so I started teaching public courses soon after.

Q. What's unique about your teaching style?

A. Despite decades of teaching experience, including thousands of hours of software instruction, I really consider myself a consultant at heart. The vast majority of my business is still dependent on producing successful outcomes for my clients. My classes are unique in that I teach the same way that I would when training a new hire. I know that most of my students in the predictive analytics program are not taking my class because they want to pursue a PhD. They are trying to make themselves more valuable to their employers. I assume that they will be doing this in their career so assignments are challenging, but as real-world as I can make them. However, I think conceptual knowledge is critical so we do a fair amount of reading. I think this surprises students who think data science is only about the math or about following “recipes” for performing data analytics.

Q. What's your favorite lesson to teach and why?

A. I am currently teaching courses on the Deployment and Data Understanding phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). I also teach the Introduction to Predictive Analytics course. My favorite lesson is the opening week of the Data Understanding course, probably because it is my newest course and I spent a lot of time designing it. I love when students discover that seemingly primitive analyses, when performed correctly, can uncover the strangest things about the data. I also appreciate when they understand what information you must share with a Subject Matter Expert (SME) before you can safely build a model. It's taken me more than 25 years to get there, but I can find weird quirks in a dataset in less than an hour that my clients didn't know were there. This is not a kind of performance art to impress the client, but rather an uncovering of critical issues that might endanger a project. It is metaphorically like a home inspection before buying a house. I like revealing this new world to groups of potential future colleagues.

Q. What do you find most rewarding about being an instructor?

A. I don't know if folks will even believe my answer — it is grading. The reason is that although I share Skype calls with quite a few students I don't “meet” all of them one on one. So grading is my primary correspondence with students, especially those that get full points on most assignments since they are less likely to arrange a help session with me. Therefore, I don't comment only on poor submissions as constructive criticism. I also comment on submissions with perfect scores and explain why the work is good. I even love it when a student with a perfect score on an assignment submits a second version just to clarify something in our correspondence or tries an alternate approach. When there is an interesting task, but it is perhaps a bit too challenging to make it a course requirement, I offer it as extra credit to encourage this behavior even more. I've also had students turn awful submissions into excellent ones through our correspondence. I emphasize the creative and subjective aspects of predictive analytics. For those of us that make our living doing this work, the creative aspect dominates. So, when I start to see students turning in professional work, especially when I can detect true improvement from week to week, I find it very rewarding.