On Friday, Nov. 5, the Upper School had an opportunity to hear University of Chicago professor Steven Levitt talk about his work in economics and data science.

A St. Paul Academy and Summit School, Harvard University and MIT graduate, Levitt began his session by sharing with the audience how he started his “career” in the realm of data: spending a lot of time, and money, at the race tracks. While he was still a high school student at the time, Levitt recalls how his first attempts at using data were not so successful. Using the data analytic skills that he had learned in Mr. Boulger’s computer class and the $5,000 that he had earned and raised from his friends and family, Levitt bet all his money at the tracks. In the end, Levitt said, “When I had lost all the money, I had to shut down the operation.”

At Harvard, Levitt continued to apply data science, economics, and computer programming to solve a problem of utmost importance to all students: getting the best dorm room. At the time, picking dorms at Harvard was based on the luck of a lottery draw. Students who drew higher lottery numbers got to choose their rooms first with the other people they chose to group up with. While everyone else grouped up in teams of two or three, Levitt decided to form a group with thirteen others and left his fate up to probability and statistics. In the end, this method did indeed prove to be more reliable than luck. Levitt’s group ended up getting the third draw of the night. However, Levitt said, “We hadn’t really sorted out the problem, which was that the fourteen of us didn’t even really like each other. We were only put together because we wanted to get a good draft.”

Similar to the message of his best-selling book “Freakonomics”, Levitt told these anecdotes to emphasize how data science is not just a subset of skills restricted to a certain career but is related to every aspect of life and impacts how society functions. The growing revelation on the importance of data has also caused well-paying jobs in data science to grow at unprecedented rates. In a Linkedin 2020 emerging job report, the vacancies and growth in data-related jobs were projected at 37%. According to the US Bureau of Labor Statistics, the average annual income of data scientists is $103,930.

Despite the growing interest in data on college campuses and in the professional world, exposure to data science during secondary or primary school has been essentially absent and often associated with only STEM subjects and not the humanities. But just like back in 1985 when Levitt attended SPA and described the school as, “fifteen years ahead of the curve, running like what a good ‘high school’ is considered to be now,” Levitt once again believes that SPA can be a leader. This time in data science. Levitt hopes to see data incorporated across every subject and potentially even an additional “senior project” to expose students to data science. And what Levitt means by data science is not learning about distribution curves, t-tables, and theoretical statistics, but about asking the right questions, collecting the right data, and using that data to unveil powerful discoveries.

In Levitt’s own career, these life-changing discoveries have always been nearby. Levitt has co-founded a consulting firm, TGG, to help companies better understand big data. Using GPS tracking and algorithms, Levitt has also teamed up with local Chicago police to release people from prisons, reintegrating them into society while also ensuring the safety of the greater community.

Even as a non-avid social media user, Levitt still believes that perhaps social media is the most intimate and influential application of data in people’s lives. In his experience, the occasional trips he makes to Instagram always leave him buying one out of every three things he sees. He said, “It’s like it knows me better than I know myself. It’s amazing. That’s the power of data if you use it well.”

Besides its pros, Levitt also recognizes that social media is multi-faceted and that much of data’s impact results in how an individual, or company, decides to utilize it. With its addictive algorithms, social media has the potential to spread negativity and worsen mental health. With all the user input, social media giants have much more data about users and could easily breach personal privacy. Levitt said, “It’s interesting because these companies like Google, Facebook, YouTube, adopted a business model that people like me thought were crazy. How can you not charge consumers for anything? Give away everything for free? It was really puzzling to economists. But they understood something we didn’t understand. The value of personal data was so big that they could give away all their services and monetize those through ads.”

Using the case of social media, Levitt also addressed that the power of data and data science is also heavily dependent on personal judgment and morals. Depending on the person, the incentives, and information asymmetry, data can result in very different outcomes. Like a recent Duke University phycologist scandal and even an incident (luckily caught before further actions) with one of his co-authors, Levitt said, “sometimes they [data and discoveries] turn out to be absolutely pure fraud.”

Levitt’s expansive knowledge and expertise in the realm of data science and economics comes after years and years of learning and interest. While Levitt is currently focusing his studies on the events of causality in data science, he also never forgets what got him started in data science- the race tracks. From a place where he had once lost $5000, Levitt has now turned the race tracks into a very profitable hobby. He said, “I was not a big success story at SPA. I got into tons of trouble, I didn’t get all that good of grades… but whatever is going on here, you get a fresh start. The clock starts over.”

From this story, Levitt hopes that students can not only realize the power of data in everyday life but, more importantly, that the most successful things you can achieve start with finding what you want, even if they don’t begin perfectly.