So many analyses will never actually be implemented or actualized. The process is extremely cyclical; it requires so much hypothesis testing and trial and error for just one set of questions."
"Oftentimes, you need to go back and look at an entirely different variable or different metric to try and find your answer. A project with a defined end goal is extremely satisfying, but you don’t see that all the time in practice," Professor Virtu explained.
Professor Virtu learned firsthand that communicating complicated concepts in a clear, relevant, and relatable manner can better drive data-driven initiatives and facilitate meaningful collaboration. Beyond just seeking answers to tough business questions, Virtu enjoyed helping other people understand complex concepts—which required figuring out how to explain technical ideas in layman's terms.
“I found myself breaking down complex concepts frequently in the business world,” she explained. “If data scientists try to use technical terms with our stakeholders, they're not going to understand exact terminology or why we're using certain processes. Oftentimes, I found myself educating external parties to reach a shared understanding.”
Virtu felt a lot of parallels between her role as a kind of interpreter at work and the process of teaching. Her interest in helping others understand complex technical concepts led her back to Kogod.
While working at her corporate jobs, Professor Virtu was also a TA for Professor Espinosa’s ITEC-621 class, Predictive Analytics. She would return to campus to talk a lot about her time in the workplace and how she used predictive analytics in the real world. During COVID, she transitioned to an adjunct position for some of the analytics online courses, including ITEC-300 and Programming Tools for Analytics: R.
When I had the opportunity to come to Kogod full-time, I couldn’t say no."
Now Professor Virtu will teach ITEC-621, Predictive Analytics, which students take in their MS in Analytics program. The course explores all kinds of math theories, algorithms, and modeling techniques. Professor Virtu finds that bringing an experiential approach to her classes is incredibly fulfilling, allowing students to make rewarding, real-world connections.
Through her teaching, students will build the necessary skills to critically think through a data-centric lens. To do this, Virtu brings in real-world applications blended with her personal experiences.
“I think being able to relate exactly what we’re learning in class with what I've done in my past career has been super helpful for students to not only understand what we're doing but also why we're doing it and how it connects to their future jobs,” Professor Virtu explained.
When students make business recommendations, she challenges them to get past the “what”. She believes recommendations become meaningless if students can’t effectively communicate both “why” a finding is important and the overall impact the implementation of their recommendation will have on the business unit.
“Ultimately, my goal is to prepare students to be responsible, thoughtful, and impactful leaders in the broader society,” she said.
Virtu is especially excited about where the future of predictive analytics is headed, and how she and her students can work to make analytics more accessible to a wider populace. Models like Chat GPT, built on open AI, have brought analytics and machine learning to a very accessible and public forum.
“Until recently, you had large language models that were inaccessible; they were behind APIs, or you had to be in the market to really be able to use them and access them and build upon them,” Virtu explained. “Now, you can simply go to a website and plug and play. I think the speed and the accessibility of these open models have forced some upper-level stakeholders to start acknowledging the importance of analytics and start looking to see where else data analytics could make an impact.”
Professor Virtu does note that these platforms still present concerns over privacy and misinformation. “I think we’re going to have to become really good critical thinkers and get better at identifying if something is real or not,” she said. “And a whole framework will have to be built around data privacy protocols around best use and ethics. I think that’s all to come. But this is just the start of the conversation! It’s all exciting.”