Amanda Bullert

What are your current research interests?

I am a data manager and research consultant at the Minnesota Supercomputing Institute, where I assist researchers from the Masonic Institute for the Developing Brain with their data needs. In my role, I help develop the cyberinfrastructure and storage solutions required for data management, and I utilize various tools to analyze and visualize the data.

How do you define Data Science?

To me, data science is a broad field that encompasses everything related to working with data. This includes designing comprehensive data collection strategies, analyzing large datasets using advanced tools, and crafting effective visualizations to communicate insights clearly. The essence of data science is to transform raw data into actionable insights that can drive strategic decisions and address real-world challenges across different industries.

Can you share an interesting or surprising result you’ve found in your data?

In the current project, I’m involved with, we’re collaborating with the Department of Defense to support military families and those in rural areas of Minnesota in addressing their children’s mental health needs. This project aims to develop telehealth tools that facilitate more seamless and effective interactions between families and their care teams. The goal is to enhance access to mental health support for families who may face challenges due to frequent relocations or living in areas where specialized care is not readily available. It’s a critical reminder that consistent, high-quality medical care isn’t always accessible to everyone, and this project seeks to bridge that gap by leveraging technology to connect families with the care they need, no matter where they are.

Are there any interesting new tools or libraries you or your students have been using?

I’ve found Tableau to be an incredibly useful tool in my own data science work for the current project I am involved in. It really shines in making the whole process smoother and more efficient. I’ve used it for everything from cleaning up messy data to creating interactive visualizations and dashboards. What I think both the researchers and I appreciate most is how it makes it easy to build and share dashboards that are both informative and engaging.

What are you most excited about in the field of data science in the next 5 years?

I’m excited about several areas of growth in data science. I’m particularly interested in how tools for data collection and visualization will evolve to become more automated, user-friendly, and widely accessible. Emphasizing strong data management practices will also be crucial as technology continues to advance. I believe that as researchers recognize the value of detailed metadata and thorough documentation, we’ll see significant improvements. Additionally, advancements in data interoperability are exciting, as they will make it easier to seamlessly combine and analyze data from diverse sources, leading to more comprehensive insights and better decision-making across various sectors.