Instructor Q&A: Keith McCormick
Predictive Analytics
Summer 2019
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.