Alumni Spotlight: Broad focus, yet targeted for career development

Author: Deanna Csomo Ferrell

Amanda Cameron takes a selfie on the field of the stadium.

A connection to classmates, as well as a curriculum that has both a theoretical and practical focus, are reasons to consider the online M.S. in Data Science program at Notre Dame, a 2022 graduate said.

Amanda Cameron works at Google in Atlanta as an artificial intelligence and machine learning (AI/ML) customer engineering specialist, and found the program—which combines synchronous, online learning with in-person immersions—to be an essential component for advancing in her field.

Cameron, who has a bachelor’s degree in applied mathematics from the University of Georgia, was working for AT&T in cybersecurity when she began the 21-month program. Her goal: to work in Big Tech, but she was unsure how to make the jump. She first tried graduate school through Georgia Tech’s online program for computer science, but it wasn’t the right fit, nor did it match the work she was doing at AT&T.

“I saw a lot of peers getting master’s degrees and thought, okay, I guess it’s time for me to get mine,” Cameron said. “I didn’t start out having a clear, defined path. I also think it’s important for people to know that it is okay to admit failure and admit when things aren’t working. This leads us closer to our goals and the career we are trying to build.

“For grad school you’re going deeper, not broader, so you really need to make sure you’re connected to the material and the faculty. If I could do it over again, that’s what I would tell myself.”

A colleague at work told her about the Notre Dame program, which was more mathematical and better suited to her skills. Despite the pandemic, the program ran smoothly, and she remembers the connections she had with classmates in the midst of challenging circumstances. She credits Associate Director Samantha Adamczewski with remaining calm as they all faced the pandemic together.

Having a professional goal anchored everyone, Cameron said. “She was like, everyone’s stressed; let’s not make this more stressful for other people. We’re all doing our best right now, and if we come together as a class, we can graduate as a class.”

The professors managed the wide range of skills in the course in a seamless manner, which Cameron also appreciated. Some people are skilled with statistics, others are skilled with computer science, and yet others are data storytellers. And within the program and the immersions where they worked together in person, they were all able to share and learn new skills.

She enjoyed how the program covered broad, theoretical topics, but also provided the level of focus that all data science students need. For instance, the first class about probability and statistics packed a year’s worth of statistics into about 12 weeks.

“We had to very quickly upscale and adapt our thinking to how statistics can be used in an applied role, going beyond the area under the curve and fundamental interpretability of statistical models,” Cameron said. “But it’s very focused at the same time.”

Cameron described that the program was still different than she expected, because there wasn’t much structured lecture—especially given the caliber of professors in the program. The professors would share their knowledge, then hand over problems to the students who worked in teams to solve them.

She soon understood that combining the lectures with group assignments better mimicked the real world.

“I guess we don’t want our bosses lecturing at us for like an hour, either,” she said, jokingly.

She took advantage of the invaluable personal support, as well as resume and interviewing tips she learned in the program, when she interviewed for Google during the final year of the program. She’s since used the skills she learned in different ways, noting that as she talks to customers about AI or other topics, she has to wear different hats and uncover other opportunities to help meet the customers’ needs.

And that’s important, because the field is even more dynamic than in the past.

“Data science as a whole has changed, because now we’ve got generative AI, right?” Cameron noted. “It’s an interesting testament to, you know, even in 2023, the technology is always changing.”


Originally published by Deanna Csomo Ferrell at on December 20, 2023.