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Tying It All Together

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Seidenberg Professor Constance Knapp teams up with a Lubin student to take a closer look at predictive analytics and data mining.

Have you ever wondered how your favorite online stores manage to suggest the best products for your individual taste? As the holidays and gift-giving season near, Information Systems and Qualitative Business Analysis double major Hannah Cherian ’14 is taking a closer look at what is called predictive analytics, via the Pace Undergraduate Research Program.

In her project titled “How Do They Know What I Want to Buy?: Customer Analytics and Data Mining,” Cherian, with the help of her faculty partner Seidenberg Professor Constance Knapp, PhD, is exploring specifically how financial markets use these predictive analytics. Since Cherian has been offered a full-time job at Fannie Mae after graduation, the two have focused their research on how investors make decisions on Wall Street, how the financial market decides what financial tools costumers might want, and more.

As a Seidenberg and Lubin student, Cherian says predictive analytics hit both her majors and was an area of interest to her. When Professor Knapp approached her with the possibility of pairing up for the Undergraduate Research Program, Cherian leapt at the chance. However, during an initial literature review, they were surprised to see just how extensive the topic would be.

“I had no idea how widely used predictive analytics were and how many different areas there are,” says Knapp. “For me, predictive analytics were always the Amazon thing—you know, ‘People who bought this book also bought that book,’ but it’s much broader than that.” In fact, Knapp says that predictive analytics are even used in universities to predict what college courses will be offered in the future.

That’s when Knapp suggested they focus on an area that would directly benefit Cherian as she begins her career at Fannie Mae—financial markets. “I really appreciate that she did that because I would have never thought to do that, and it makes so much sense now that I’m looking at it and taking a step back,” says Cherian. This spring, after graduation, she will enter a two-year rotational program at Fannie Mae, which she has requested to focus on data analysis in addition to her other areas of interest. “It excites me that I’m doing something now that’s going to help me, very specifically, for my job at Fannie Mae,” she says.

While their research and eventual presentation of their findings spans the course of an academic year, the two are still making strides in reviewing literature about predictive analytics in financial markets. Time permitting, Cherian hopes to interview some of her professors for first-hand opinions on the topic. “I have finance professors who have worked in investment banks for years before becoming professors, so I want to talk to them and see what their input is,” she says.

Cherian and Knapp both express gratitude for being included in the selective program. About 150 students applied for this year’s program, with only 30 available spots. For Cherian, the program has been an opportunity to see a real-world application of her studies. “It’s really taking what I’m learning in the textbook and what I’m doing on the job and tying it all together. It’s an integration of the two. I’ve always studied business and IT separately, so to see how they tie together is something that’s huge for me now,” says Cherian.

And for Knapp, a lover of learning, the program offered a chance to build a community out of the Undergraduate Research Program and learn new things to incorporate into her classroom teachings. “For the first time in a very, very long time, I got to sit at a table with folks from all over the University who are working in areas that I might never have known about,” she says. “I love this idea of a research community, and I think it’s really fascinating.”

To follow Cherian and Knapp’s progress on “How Do They Know What I Want to Buy?: Customer Analytics and Data Mining” and to view other Undergraduate Research projects, visit