I had a wonderful time teaching for a day at the Wharton School of Business at the University of Pennsylvania, one of my alma maters. I was honored to do so, honored to be “Professor for the Day” (my host’s label).
I taught three classes during my day there – undergraduates and MBA students.
It occurred to me as I went through the day, that teaching is both mining a database and paying it forward – setting a clearer path for the next generation to follow.
First, a lot of data analysis goes into who gets to go to Wharton. It is a competitive process with Admissions officers trying to figure out the best candidates. I am pretty sure most of the applicants could pass the course work. But the admissions folks must also be looking for the candidates who will serve the institution well afterwards, while contributing to the breadth of experience and interest to the class. That means assessing a range of other variables, more than business experience, GPAs and GMAT scores. It is a perfect world for AI to mine the data of, not just the applicants, but how the graduates do compared to the overall applicant group. Could AI find correlates that would be predictive?
School itself means going through the immense amount of information that is all around us. We sift it and take out the parts that we think are important and useful. How to do that assessment is the message we relay to the next generation.
My message to the classes was both technical and philosophical. I taught about going through the data available using statistical tools to glean the real gold that is there.
Beyond the technical the message was to be open to new tools. Accept them fearlessly. Use them to benefit your organization and yourself. However successful or proficient you may become, maintain a sense of humility and curiosity, while respecting all you encounter. Find and work in an area in which you are passionate. Life ought to be a joy, including “work.” Work at what you love, and you’ll never “work” a day in your life…
The tools were different when I was at Wharton. Heuristic models were coming in. We were using Fortran and mainframe computers (with keypunched cards!). Now we can use the ever-increasing computer power available to do a much better job in identifying risks and opportunities. (At the same time, there is more of a flood of data, requiring greater care and even concern over appropriate use.)
When I was creating models, they were very useful and produced positive results. We were willing to try new tools and willing to fearlessly go where the results led us, and we were necessarily flexible and creative, continually reworking and refining our models to continuously improve them. That’s the real message.
Do your own sifting through the database of accumulated knowledge. Learn from it and follow it. Don’t be afraid to try something new. And keep your eyes out for something even better.
True about analysis techniques. True about the people that we educate to guide us into the future. And the students I met were inspiring and left me feeling very hopeful for that future. My little part of paying for my future was to pass on my knowledge. And have some fun as well.