What I learned from teaching 1,500 analytics students

Engaging, teaching, mentoring, and advising mature, mostly employed, often enthusiastic and ambitious adult learners at University of Toronto has taught Jerrard Gaertner more about analytics in the real world than he ever imagined. Jerrard shares stories he learned about everything from hyped-up expectations and internal sabotage to organizational streamlining and creating transformative insight.

Sometimes, people can become so focused on technology, data, statistics, modeling, or other fascinating and important problems before them that they forget the world is not their big data playground. Quite the opposite. In fact, most of the real world misunderstands or fears what data scientists do (or, more often, just doesn’t care). Jerrard Gaertner has taught about 1,500 adult learners at the University of Toronto School of Continuing Studies over the past four years through the Management of Enterprise Data Analytics program. Smart, mostly employed, ambitious, and intellectually engaged, these students come from a wide variety of industries, have diverse academic and employment backgrounds, and are for the most part anxious to share their experiences, both positive and negative, with their classmates. Although the statistician in Jerrard recognizes that this is a nonrepresentative, self-selecting, geographically limited cohort, the social scientist, organizational psychologist, technology strategist, and security auditor nevertheless sees an incredibly valuable richness in the combined experience of these individuals. Jerrard shares stories he learned about everything from hyped-up expectations and internal sabotage to organizational streamlining and creating transformative insight. He covers big data projects, predictive analytics, organizational intransigence and subsequent liberation, fearful technologists, and immensely grateful patients and demonstrates how to reverse-engineer a big data job posting (for fun). Along the way, Jerrard explains why some of his students become so concerned about the ethical risks of prediction that they joined political or advocacy groups and summarizes some of the key factors underlying the high failure rates and slow penetration and progress of data-driven decision making. And of course, he will also try to entertain you.