After studying theoretical mathematics in college and working in data-focused roles, Lanie Kropp discovered her passion for solving real-world problems with data. She already had well-developed math skills, but she decided she had to add data management and computer science mastery to the mix if she were going to take her career to the next level.
A Tufts University employee, Kropp knew first-hand the quality of instruction and support at the school. She knew that if she were going to enter a graduate program, it would be the Tufts online Master of Science in Data Science (MSDS). Through research, she determined that she could meet the program requirements and that the program complemented her career goals.
In the conversation below, Kropp shares more about her experience with the Tufts online MSDS, including her journey, mentorship, and advice for prospective students.
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Please start by introducing yourself.
I’m Lanie Kropp. I have an undergraduate degree in mathematics and am finishing my last semester in the Tufts online MSDS program this December. I’m doing the program part time while working as a data analyst at Tufts in the Office of Institutional Research. Before coming to Tufts, I worked in another data role.
Tell us a little about why you chose to pursue your master’s degree and why you chose Tufts.
My educational background is in theoretical math, but I quickly realized I preferred the applied space. I like solving real-world problems and working with real-world data. While I had a strong mathematical foundation, many of my data and computer science skills were self-taught. I decided to pursue a master’s degree to round out that knowledge and engage more deeply with the conversations happening in the field. The Tufts online MSDS program was a good way to access that information and connect with others working in this space.
Did the online format of the program factor into your choice, and how has the experience been for you?
It was a huge factor in my decision. I chose this program because it’s at Tufts and offered online, which allowed me to pursue it part time while continuing to work full time. The flexibility to take classes from my apartment rather than attending in person was the only way I could make it work with my schedule. This option was a significant factor in making the program accessible for me.
You mentioned that you’ve been able to find community with your classmates. What does that look like in the online space?
It depends on the class. In my smaller classes, the weekly Zoom sessions felt very conversational, allowing us to get to know each other well. They were mostly discussion-based rather than lecture-focused, which was nice. In my larger classes, some professors used breakout groups, which was great because it’s less intimidating to speak when there are just a few faces on the screen instead of 20.
Some professors also encouraged us to interact on online platforms, which sparked great conversations about assignments and provided insight into others’ experiences. These connections reminded me that other people were right there with me, working through the coursework.
Is there a particular assignment or project that stands out as challenging, exciting, or fun?
I really enjoyed my final project for reinforcement learning. The assignment required us to pick a problem that interested us, frame it as a reinforcement learning problem, and figure out how to make it work. It was great because the class had such a vast range of ideas. One classmate worked on financial data while I focused on pond ecology. Even though these are very different domains, we were able to apply the same reinforcement learning principles to both.
I didn’t know much about pond ecology but was interested in it and knew how to frame the problem effectively. This project deepened my understanding and allowed me to apply reinforcement learning practically. I enjoyed the project, and it ended up being pretty successful.
Can you talk more about what reinforcement learning is?
Reinforcement learning is a problem paradigm where a computer agent performs a task and receives positive or negative rewards based on performance. From these rewards, the agent learns how to behave the next time it performs the task. A common example of reinforcement learning is playing a game like tic-tac-toe, where players earn points for winning or lose points for losing. Over time, players learn which actions lead to better rewards and adapt their strategy accordingly.
Can you talk a little bit about any relationships and the support, guidance, or mentorship you’ve found with faculty members?
One faculty member I connected with was my statistics professor. We share similar mathematical backgrounds, so it was interesting to see how we both came to the statistical space without having a specifically applied background.
Aside from being a great professor, he was always really accessible. I reached out to learn about his career journey, the role statistics and data science had played, and how his mathematical foundation contributed. We had some great conversations that provided valuable career insights and help on my capstone project.
What’s your capstone project?
I love talking about my capstone project. It’s called Transfer Learning and Medical Imaging and focuses on a challenge in the medical data space: having data sets that are robust enough to train successful models. Privacy concerns with medical data make it costly and difficult to share, and some medical conditions are rare, meaning there’s limited labeled data available. This makes training effective models really difficult.
My project involves transfer learning, where you leverage knowledge from a computer that can learn from a problem with robust data and apply it to a problem with limited data. I’m trying to train a model on ImageNet, a large dataset of generic images, and transfer that knowledge to X-ray, MRI, and ultrasound images. I’m also looking at doing direct transfer learning between those medical imaging domains so that well-labeled ultrasound data can be used to solve an X-ray problem. It’s exciting to take something humans do naturally, like using knowledge from one problem to solve something similar, and apply it to computers.
Where do you want to go after you finish your degree?
I don’t have a specific answer. One of the great things about this program has been discovering how vast the field of data science is. What I’ve learned most is that I want to work on interesting questions. Sometimes, limited data or resources restrict you to basic questions, but I want to dig deeper and explore more meaningful ones. I want to work with a supportive team and company that is excited to answer questions and provide the resources to make it happen.
Is there any advice you’d give to a student considering this program? Or something you wish you’d known that would have made your decision easier?
My best advice is to reach out and talk to as many people associated with the program as possible. When I was applying, I had some questions. I reached out through the website and connected with the admissions team and Marty Allen, the program director. We had many conversations in which I asked questions about my application and what the program would be like. The staff are happy to answer any questions, so don’t spend time speculating. Ask your questions, and you’ll figure out if this program is the right fit for you.
Almost like how you could build a data set.
Yeah, exactly. Applicable to everything.
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The Tufts online MSDS helps you unlock the power of data to solve real-world problems. You’ll learn essential skills in data mining, machine learning, Python, and systems analysis, preparing you to make an impact in any data-driven field. Throughout the program, you’ll gain hands-on experience and collaborate with peers and expert faculty members while benefitting from a comprehensive range of resources to support your academic success.
Contact an enrollment advisor to learn more about the Tufts online MSDS program and admissions process. If you’re ready to take the next step, you can start your application online today.