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Office Hours: Assistant Professor of Data Analytics Lulu Wang

by Tony Moore

Assistant Professor of Data Analytics Lulu Wang earned her Ph.D. at the City University of New York. Recent publications include Comparing Analysis of the United States and Germany Pension System Reform and China’s Experience in 2015 Stock Market Crash.

Dickinson’s liberal-arts twist on data science means students don’t just crunch numbers—they learn to tell stories with them, whether in economics, politics or even pop culture. What do you think makes Dickinson a great place to study data analytics, and how do your courses set students up to connect the dots across disciplines?

You are absolutely right! Data science is often less about the numbers themselves and more about the stories they might tell. At Dickinson, students get strong foundations in math, statistics and computer science, but they also learn to apply those skills in real-world contexts.

I think what makes Dickinson a great place to study data analytics is that the curriculum here is designed to encourage students to be interdisciplinary, exactly what studying data analytics is for: turning raw data into meaningful stories in various fields! This is something that really stood out to me since I joined Dickinson last fall. Students don’t just gain technical skills; they also develop a deep understanding of their own fields.

To contribute to this interdisciplinary approach, students in Intro to Data Science, for example, work with real-world datasets drawn from diverse fields such as economics, environmental studies and public health, learning to interpret data in its context. They also frequently collaborate on final projects, addressing real-life problems they encounter in other disciplines and developing practical solutions.

Your research dives into everything from predicting financial volatility to exploring graph neural networks and recommender systems. What first got you hooked on data analytics, and what keeps you excited about digging into all these different ways data can explain the world?

I’ve always loved finding patterns in a pile of information. My first job out of college as a quantitative trader really hooked me on data analytics. You spend your days looking for patterns in all sorts of market data, from stock prices and trading volumes to macroeconomic indicators, and then build models to try to make winning trades. What’s exciting about trading is that the feedback is almost immediate; you see right away whether your model works or not. It felt a bit like playing a challenging, high-stakes puzzle game every day.

Later, during graduate school, I worked as a statistical consultant, helping faculty and students tackle a wide range of data and model-related problems. That experience opened my eyes to just how universal data science is. I realized that these skills can be applied to virtually any field, from economics, finance and public health to linguistics, zoology and political science. Suddenly, data was no longer just numbers; it was a way to understand and influence the world in countless ways.

What keeps me excited today is the sheer variety of projects I get to explore. The ability to connect technical skills with real-world context and to see how data can tell compelling stories is what makes working in data analytics endlessly fascinating. It’s this combination of curiosity and problem-solving that keeps me hooked.

Data are everywhere—from Spotify suggesting your next favorite song to analysts forecasting global markets. In your view, what’s one surprising or even fun way data analytics reshapes the world around us, and why is it so important for students to have these skills right now?

I think as human beings, we’ve always tried to make sense of the world by observing, collecting information and turning that into knowledge to solve problems. What’s different now is the scale of data availability. Never before in history have we left such a detailed trail of data behind. Every click, every playlist, every ride on a scooter or swipe on social media, data is constantly being generated, whether we notice it or not.

Sports are a great example. Colleges are now using AI and wearable data to monitor things like fatigue and injury risk, helping athletes stay healthier while also giving students hands-on experience with cutting-edge tools. At the University of Pittsburgh, for example, their new Sports Analytics Innovation Center links athletic performance with real training in AI and cloud computing. And beyond campus, teams like Liverpool FC and the Golden State Warriors are proving how powerful analytics can be for strategy and performance.

The fact that data is reshaping every aspect of our lives and behaviors makes me think that learning data analytics is almost essential to adapt to this transformation. Just as relying only on flip phones when everyone else has moved to smartphones is possible but impractical, resisting the data-driven world is increasingly inconvenient. More than simply adapting, the real challenge—and opportunity—is to navigate this technological wave to create positive change. This is why equipping our students with these skills is so critical today.

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Published September 11, 2025