The Master of Science in data science and the doctorate in data science were approved at the Nov. 20 UNC Board of Governors meeting, and applications for both programs open in early December. These new degrees are an addition to the school’s Bachelor of Science in data science and online Master of Applied Data Science programs.

Students will have core coursework in mathematical and statistical foundations, computational thinking, machine learning, artificial intelligence and data engineering. All degree programs at SDSS require coursework in both communications and ethics.

“We’re grateful to the Board of Governors for approving these two new graduate degrees which will create an additional pipeline of data scientists for our state and our world,” Dean Stan Ahalt said. “These graduate students will not only accelerate our faculty’s research, but make discoveries of their own, using the most cutting-edge tools and models. We’re excited to welcome our first cohort of residential graduate students.”

For both graduate programs, students will select one of four specialization tracks for their studies:

• Advanced data science foundations and AI (available fall 2026)
• Applications in physical, biological and health sciences (available fall 2026)
• Applications in social sciences and humanities (available fall 2027)
• Data engineering (available fall 2027)

Applications for the M.S. in data science and doctorate in data science programs open in early December with a Jan. 15 priority deadline and a Feb. 10 final deadline. For more information, visit datascience.unc.edu.

From health care to machine learning, four Carolina experts broke down how they think AI will change their respective fields over the next decade.

Kandyce Brennan

Clinical assistant professor, UNC School of Nursing 

Tell us about your research. 

My work focuses on integrating AI-enabled tools to improve patient-centered care, enhance educational outcomes and promote health equity, ensuring technology supports humanistic and compassionate health care practices.

I’m leading a project that exemplifies these principles by developing SARHAchat, an AI-enabled chatbot designed to provide sexual and reproductive health education and support to young adults in rural and underserved communities.

 How do you see AI affecting your field in the next decade? 

In the next five years, I expect AI in health care will shift from experimental tools to trusted partners in care delivery, especially for underserved populations. The key evolution won’t just be in the technology’s capabilities, but in our understanding of how to implement it equitably.

Looking 10 years ahead, I envision AI fundamentally reshaping health care accessibility. We’ll likely see AI systems that can provide personalized health education and support that adapts to individual literacy levels, cultural backgrounds and communication preferences.

Francesca Tripodi

Lead faculty, Master of Applied Data Science, UNC School of Data Science and Society; associate professor, UNC School of Information and Library Science 

Tell us about your research. 

My research focuses on how generative AI is reshaping the way people search for information. Together with my collaborators at the Search Prompt Integrity and Learning Lab, we are exploring how people come to trust information curated by GenAI, analyzing the sources AI-Overviews rely on and investigating how biases or inaccuracies on one platform can undermine the integrity of these results.

 How do you see AI affecting your field in the next decade? 

“AI” is both a concept and a tool impacting many facets of human life — labor, education and decision-making. I reject the notion that AI is “taking over” and rendering humans obsolete. The future of AI depends less on the technology itself and more on how corporations and governments choose to invest in human infrastructure. Humans created the foundations of AI, the companies applying it and the policies that regulate it. To understand how AI will evolve, we must look critically at these human actions — and the power structures that drive them.

Youzou Lin

Associate professor, UNC School of Data Science and Society 

Tell us about your research. 

My research focuses on teaching computers to “see” inside the Earth and the human body using waves. Just like ultrasound lets doctors look inside the body, or seismic waves let geologists study what’s underground, the new computer methods that I design combine AI with the laws of wave physics. By blending AI with physics, we can create sharper, faster and more reliable images.

How do you see AI affecting your field in the next decade? 

The next big step is building AI that is guided by wave physics — the science of how sound waves, seismic waves and other signals travel through materials. My team is focused on this challenge, creating AI systems that can work even when data are scarce by learning directly from physics. Looking further ahead, I believe AI will not only use known wave physics to make imaging sharper and faster, but also help us discover new physics, opening the door to breakthroughs in how we see both the human body and the Earth.

Jessica Zegre-Hemsey

Associate professor, UNC School of Nursing 

Tell us about your research. 

My program of research in emergency cardiac care is focused on improving patient outcomes among individuals with acute cardiovascular conditions. My team investigates noninvasive cardiac monitoring and other physiological measures for early disease prediction and to advance early risk stratification and triage, diagnosis and access to lifesaving care. I am a nurse scientist collaborating with an interdisciplinary team of cardiologists, emergency providers, EMS, engineers and computer scientists to advance science and knowledge of evidenced-based care.

 How do you see AI affecting your field in the next decade? 

In 10 years, I think AI will be more readily integrated into real-time clinical decision-making across health care settings. For example, in a fast-paced setting such as EMS, AI strategies might have potential to make predictions that can readily help EMS clinicians make decisions and take appropriate steps for treating an emergent condition.

In the future, it will be critically important that disciplines continue to work together to develop, test and validate models to ensure reliability and accuracy before they are used in standard practice. I think, however, there is great opportunity for AI to augment clinical practice across the health care spectrum.

Banerjee’s project focused on designing proteins — tiny building blocks that help our bodies to function. Scientists often use electron microscopes and computer models to figure out how these proteins are shaped, which is important for things like developing new medicines. But the imagery and data used to build these models can be messy or incomplete, making it hard to get accurate results.

“You can’t see a protein with your eyes,” Banerjee explained. “But with the right data and tools, you can reconstruct what it looks like. That’s what this project was all about.”

Banerjee’s research made extensive use of UNC Research Computing’s Longleaf shared computing cluster, which is provided at no cost to UNC faculty, students and staff. However, he determined that a more interactive, trial-and-error computing environment was necessary to successfully complete his work.

AIAP funding made it possible

That’s when Harlin Lee, Banerjee’s mentor and an assistant professor in the UNC School of Data Science and Society, learned about the AI Acceleration Program. The AIAP offers computing support through a strategic partnership with Microsoft Azure. The program provides cloud computing credits, essentially giving researchers access to powerful virtual machines in the cloud that can handle big data and complex tasks.

“This was exactly what we needed,” Lee said — not cash but computing capacity.

With help from Lee, Banerjee applied for AIAP funding and received Azure credits to continue his work. He used them to run experiments, refine his model and eventually publish a paper that became the first academic citation for the AIAP.

The paper, co-authored by Banerjee, Lee, assistant mathematics professor Caroline Moosmueller of Carolina and Xingyu Xu, a Carnegie Mellon doctoral student, presents a new approach for integrating multiple scientific measurements, such as distance data and electron density maps, to construct more accurate models of protein structures.

“We combined different types of measurements into one system,” Banerjee said. “That’s not something most models can do, and it helped us build more accurate protein structures.”

Just the beginning

The Azure cloud environment made a big difference. “It allowed me to rapidly test and prototype ideas,” Banerjee said. “I could visualize results in real time, which helped me catch problems early and make better decisions.”

Lee praised Banerjee’s creativity and independence. “We gave him ideas, and he tried them out — but the ideas that ultimately ended up in the paper were mostly his,” she said.

The preliminary results were published after a few months of work, but Banerjee sees this as just the beginning. “The hope is to use models like this for drug design and scientific discovery,” he said. “Not everyone has the resources to train these models from scratch, so learning how to use them effectively is really important.”

With more proposals expected in the coming year and a growing need for computing credits and fellowships, the AIAP is poised to help even more students, educators and researchers turn bold ideas into real-world impact.

Read more about the AIAP’s support of research.

The AI Leadership Council was established Sept. 2 by Gov. Josh Stein as part of an executive order to respond to the demand for AI as more industries move to North Carolina. University faculty and staff named to the council include:

• Stan Ahalt, dean, UNC School of Data Science and Society
• Rep. Zack Hawkins, N.C. House of Representatives, director, student affairs development
• Angel Hsu, associate professor, public policy department and environment, ecology and energy program, UNC College of Arts and Sciences
• David Yokum, professor of the practice, UNC School of Data Science and Society

The council will advise and support the governor and state agencies on AI strategy, policy and training to achieve North Carolina’s goals of fostering innovation, advancing AI-driven industries and preparing the workforce for the evolving technological landscape.

More information can be found in the news release about the executive order.

As AI changes the future of higher education and society at large, Carolina is committed to equipping students and faculty with the skills to harness this technology. The University boasts researchers who are working across disciplines to use AI for the greater good. More information can be found on the AI at Carolina website.

“It has been estimated that only 22% of known human diseases have at least one approved drug treatment,” Tropsha says. “If this effort is successful, it will dramatically affect finding cures — especially for rare and neglected diseases that Big Pharma, effectively, cannot afford to work on. It will collectively impact over 10% of people with rare diseases and is the most exciting and, potentially, most impactful project of my life.”

Tropsha is the K.H. Lee Distinguished Professor in UNC Eshelman School of Pharmacy. He also holds appointments in the College of Arts and Sciences’ computer science and biomedical engineering departments, the computing institute RENCI and the UNC School for Data Science and Society.

His field is cheminformatics, a name coined in 1998 by Tropsha’s colleague Frank Brown, then an adjunct professor in the pharmacy school. Tropsha and other researchers worldwide are creating reliable, computational models of chemical data that can forecast new uses or new compounds with a desired property — important in drug discovery and development.

An artificial intelligence approach

In addition to cheminformatics, Tropsha is also an expert in AI. In 2018, he and his colleagues published one of the first papers on generative chemical AI, demonstrating that they could artificially create new chemical entities with desired properties.

For one of his current projects, Tropsha is integrating diverse data sets into structured formats called knowledge graphs, which organize data in a way that allows machines to understand and use it.

Tropsha has built on this work in collaboration with RENCI via an open-source knowledge graph called ROBOKOP. Pulling on information from large biomedical databases, ROBOKOP is a roadmap uncovering answers to questions by examining connections between topics like drugs, diseases and genes.

What genes are involved in disease X? What drugs treat those genes? What side effects do those drugs have? ROBOKOP can provide the answers or create new data-supported hypotheses about these connections.

“Knowledge graphs are excellent at bringing together heterogeneous information into a single system so that it can be more easily explored,” says Chris Bizon, director of data science and analytics at RENCI.  “Two previously unconnected pieces of information will sometimes produce an ‘aha’ moment or unexpected discovery that wouldn’t be obvious otherwise.”

A team effort

Bizon is Tropsha’s co-principal investigator for the ARPA-H project, and ROBOKOP is a key component. The team intends to build models and research tools to evaluate every possible drug-disease pair for the likelihood that the drug may treat a disease — approximately 2,700 drugs and 18,500 diseases.

“The entire chemical disease matrix is much bigger than what we considered originally,” Tropsha confirms. “And it includes the challenge of discovering new medications as well as repurposing existing medications for both known and new diseases.”

Tropsha and Bizon are focused on the modeling side of this project and are just part of an incredibly large team that includes Carolina geneticist Melissa Haendel, who works with them to accurately code the information in ROBOKOP.

“We need to adopt an end-to-end approach,” Tropsha says. “We produce analytical suggestions for the data, and our methodologists are making assumptions. We need to know what the clinical team is thinking about so we can modify the process to make our predictions more intelligent and acceptable for the medical world. It’s a feedback loop.”

Read more about Tropsha’s research.

Tripodi is an associate professor at the UNC School of Information and Library Science and lead faculty at the UNC School of Data Science and Society. She studies how artificial intelligence is reshaping search engines like Google — and how it can amplify the biases already present in the data used to train these systems. She also teaches a master’s-level ethics course at the data science school. Through her research and teaching, she unpacks how AI is changing how we access and understand information.

AI is rapidly expanding, with the market projected to reach $1.34 billion by 2030. It’s already being used in self-driving cars, surgical tools and health apps. And while AI offers benefits like increased efficiency and decision-making abilities, it also raises serious concerns about its energy use, data privacy, algorithmic bias and workforce disruption.

UNC Research Stories sat down with Tripodi to discuss these issues and why ethics must be at the heart of AI development.

You teach a master’s-level course on AI ethics. How do ethical considerations shape the way we collect and use data in AI systems?

Ethics are messy. You can’t just “do” ethics; you have to keep incorporating them. Plus, ethical frameworks are often at odds with one another. In AI and data science, there’s this idea of creating unbiased automated decision-making. But I try to teach students that everything — from how you define the problem to the data you use — is shaped by human choices….

And so what concerns me is how are we getting the data? Are we getting access to data from places with more lax consent procedures? Are we creating agreements with other countries where citizens don’t have the same data rights? What are the larger societal consequences?

What are the pros and cons of using AI tools in everyday life?

I think all tools have the potential to help or harm. Take ChatGPT. I used to make camping lists and always forgot something. ChatGPT generated a checklist in seconds and saved me hours. AI can save time and increase clarity.

But let’s look at other applications. For example, there are new e-commerce tools being used to determine when someone sees a doctor. In theory, they reduce bias — patients might otherwise be seen out of order due to how they look or act, or because of underlying social biases. But those same biases may already be embedded in the AI’s training data. Nurses, doctors, and patients cannot really override the algorithm if their experience or “gut instinct” tells them otherwise.

What worries me is that we’re investing heavily in machines and not in people. These systems are marketed as neutral, but they’re really about cutting costs — and what’s being cut is investment in human beings. For every task we automate, could we instead invest in human infrastructure?

What role should private companies, governments and universities play in setting the ethical boundaries for AI development and deployment?

The corporate development of AI is key. Companies have a responsibility to approach it with integrity and caution — not just rush to monetize it without understanding the long-term impacts.

Governments also have a role to play. It’s disappointing that we still lack real legislation around data privacy and governance. The federal budget reconciliation bill is especially concerning. It removes states’ abilities to regulate data, which goes against the U.S. federal structure.

At the education level, we need to teach students how to use these tools responsibly, think ethically and help improve them.

Read more about Tripodi’s work.

Faberman, who has a double-major in data science as well as statistics and operations research, created a metric called Box Plus Minus Over Expected, which he abbreviates to BOE.

Box Plus Minus is a widely used basketball statistic that estimates a player’s on-court performance using traditional box score stats like points, rebounds, assists and steals. Faberman’s twist was to calculate how much a player over- or under-performed relative to expectations — and to attribute that difference partly to the coach.

“I’ve seen analysis on NFL coaching decisions, but I haven’t seen as much on NBA coaches. Coaching in basketball is hard to quantify. It’s not easy to separate a coach’s influence from the talent of the players,” he said.

Constructing the “expected” BPM wasn’t straightforward. “I went through 10 to 15 versions,” said Faberman, who earned an analyst spot in Carolina’s Sports Analysis Intelligence Laboratory.

His formula considers a player’s previous BPM, age-based improvement curves and performance trends. “I couldn’t just multiply last year’s BPM by a percent change,” said the Philadelphia native. “Negative and positive values don’t work well together mathematically. I had to get creative.”

Faberman built a “scraper” tool in R, a programming language, and captured NBA statistics for seasons 2015-16 to 2024-25 from the public website basketballreference.com. He compiled the data into a format he could analyze.

He presented his findings at Carolina’s 2025 Celebration of Undergraduate Research. Using nearly 40 reference sources, Faberman peppered his research paper with analyses of players such as JaKarr Sampson, Malik Beasley and Luka Dončić and coaches such as Steve Kerr, Tyronn Lue, Jason Kidd and Chris Finch.

Ime Udoka of the Houston Rockets was among the most influential coaches with a +0.668 BPM Over Expected. On the opposite end was Brian Keefe of the Washington Wizards, who scored – 0.616, although he was excluded from the final graphs due to limited data. (2024 was his first season.)

Joe Mazzulla of the Boston Celtics was an interesting case. Despite his team claiming the 2024 NBA title and 61 wins in 2025, Mazzulla had a low BOE of -0.182. Faberman thinks that may be because he inherited a roster stacked with elite shooters. “It’s hard to tell if success is because of his coaching schemes or because of the talent,” he said.

Miami Heat coach Erik Spoelstra stood out with a +0.177 BOE. “Players who joined Spoelstra’s team tended to outperform expectations in their first year,” Faberman said. “After leaving, they underperformed. That suggests his system elevates players.” Spoelstra appeared in a quadrant of Faberman’s graph that indicates a coach’s strong positive influence before and after a player’s departure.

Faberman looked at unique data to see which coaches get the most out of their players. (submitted photo; Graphic by Gillie Sibrian/UNC-Chapel Hill)

Surprisingly, many recent championship-winning coaches did not score high. “Some of the coaches who people think of as elite weren’t near the top in my metric. That suggests talent and roster construction may matter more than coaching when it comes to winning titles,” Faberman said.

“Shane’s methodology stands out through his novel creation of the BOE metric. It’s an innovative solution,” said adviser Kendall Thomas, a graduate teaching fellow in the statistics and operations research department.

Faberman doesn’t claim to identify the NBA’s best coach but offers a data-driven glimpse into who is making a measurable difference. “This new metric is not gospel,” Faberman said. “It’s a useful way to see which coaches really help players get better — and which ones might just be along for the ride.”

“This graduation ceremony marks a significant milestone for both our four students and our school,” said Stan Ahalt, SDSS dean. “It celebrates the culmination of years of dedication and hard work by these graduates, as well as the collective efforts of hundreds across our campus who have brought the school to life. For our newest alumni and our institution, this is just the beginning of an exciting journey.”

In January 2024, SDSS launched the online Master of Applied Data Science for working professionals. Enrollment has grown to more than 130 students. The rigorous curriculum includes nine foundational courses in data science and a capstone experience that allows students to apply their skills to real-world challenges. Nicholas Horton, Andrew McGuire and Sivani Pillutla are the first graduates of the master’s program.

Check out these 10 things you need to know before attending UNC-Chapel Hill’s Spring Commencement.

In August 2024, SDSS welcomed its first undergraduate students. Students earn a Bachelor of Science in data science by completing 62 credit hours, covering a comprehensive curriculum that includes theoretical and computational foundations of data science, communication and data ethics. William Zahrt III will graduate with a bachelor’s degree in data science and a bachelor’’s degree in computer science. He will continue his studies at Carolina, pursuing a master’s degree in computer science. This summer Zahrt will intern as a full-stack software engineer for Align Technology.

SDSS will host its graduation ceremony at 3:30 p.m. May 10 at the Frank Porter Graham Student Union Auditorium.

Meet the 2025 graduates

William Zahrt III, B.S. in Data Science

Hometown: Morgan Hill, California

Favorite DATA course: DATA 150: Communication for Data Science

SDSS faculty/staff who made a difference: Anita Crescenzi, Johanna Foster and Katie Smith

Zahrt thought data science would be “a good fit for me,” he said. “I knew analyzing data and coming up with stories using data would be really useful.” Zahrt continued to learn how to tell these stories, including an elevator speech, in Anita Crescenzi’s DATA 150 class on communications.

Nicholas Horton, Master of Applied Data Science

Hometown: Statesville, North Carolina; currently living in Washington, D.C.

Favorite DATA course: DATA 750: Mathematical Tools for Data Science

SDSS faculty/staff who made a difference: Rei Sanchez-Arias and Rick Marks

Horton sees two parts to working with Geographic Information Systems data — the art side and the technical side. “I had always gravitated toward the technical side,” said Horton, who served in the U.S. Marine Corps. “I realized that a lot of the stuff that I enjoy in my job that I do every day leans into the data science side of things.” Horton used the GI Bill to further his training in data science, knowing that the data science tools helped him reduce uncertainty for his commanders in battle.

Andrew McGuire, Master of Applied Data Science

Hometown: Lives in Statesville, North Carolina, and works in Charlotte, North Carolina

Favorite DATA course: DATA 760: Visualization and Communication

SDSS faculty/staff who made a difference: Vincent Stuntebeck

McGuire works as an applications and project analyst for a large North Carolina-based law firm and is responsible for ensuring the environments of 26 of the firms’ applications are running smoothly. “I make sure that they’re running well and assist with migrations when there’s a new application or product.” In addition to obtaining his MADS degree, McGuire is also getting his real estate license.

Sivani Pillutla, Master of Applied Data Science

Hometown: Cary, North Carolina

Favorite DATA course: DATA 740: Governance, Bias, Ethics and Fairness in Data Science and AI

SDSS faculty/staff who made a difference: Rei Sanchez-Arias and Charles Pepe-Ranney

With a biology degree from NC State and working in biotech, Sivani Pillutla still yearned for a STEM-focused experience. She wanted to explore public health and potentially pursue a degree in epidemiology. “Then I thought about how public health and data science kind of intersect,” said Pillutla. Thinking about the future of the MADS program, “I’m excited to see what classes you will be creating because the field of data science is always changing,” she said.

But when the UNC School of Data Science and Society welcomed its first undergraduates in fall 2024 to four “data houses,” the process was as modern as the school’s subject matter.

In a nod to Hogwarts tradition, students discovered their house assignments in a special ceremony, opening letters sealed with Carolina Blue wax. But instead of magic, this process relied on psychological theory backed by hard data.

“What we know from theory and evidence is that when students have a sense of belonging, their academic achievement improves, their mental health improves and they’re more likely to access campus services,” said Johanna Foster, SDSS assistant dean for academic affairs.

The evidence led Foster and Katie Smith, SDSS executive director for undergraduate studies, to create data houses as smaller communities within the school, building what education researcher David Scobey calls “a place of relentless welcome.”

To encourage belonging, each house bears the name of an inspirational pioneer in computer science or machine learning, from diverse backgrounds:

• Annie Easley, one of the “Hidden Figures” who made critical contributions to NASA’s rocket systems and energy technologies
• Grace Hopper, U.S. Navy rear admiral who devised the theory of machine-independent programming languages
• Fei-Fei Li, artificial intelligence researcher who established the ImageNet visual database, often called the “godmother of AI”
• DJ Patil, first chief data scientist of the U.S. Office of Science and Technology Policy

Each house is small — only 30 students with two faculty advisers — and “cross-pollinated” by bringing together students with different majors and class years.

The data houses emphasize the power of connection, an important concept for students who spent much of high school in COVID-19 lockdown.

“The pandemic made students more inclined to socialize online rather than through traditional, in-person experiences,” Foster said.

SDSS students holding up their letters with their house assignments. (Kristen Smith Young/UNC School of Data Science and Society)

Game night togetherness

Students don’t live in the data houses, but they do come together for meetings and activities. In November, the school hosted data house game nights in the ITS Manning lobby, with snacks and tables set up for playing checkers, Uno, Connect 4 and The Fuzzies, a Jenga-style game using colorful balls of fuzz. Students could also screenprint the SDSS logo on posters.

And there was talk — about class assignments, internship applications and whether moving a particular Fuzzy would cause the tower to collapse.

“The surgeon general says that we need more social connection to be able to combat feelings of loneliness and anxiety,” Foster said. “We just want them to be able to commingle and get to know each other.”

Waiting for her screenprint poster to dry, Parnika Dandepally of the Patil house talked about learning and meeting new people in her data house. “I’ve found out more about data science and computer science at the same time,” said Dandepally, a sophomore majoring in both. “One more tool in the toolbox.”

Rishabh Singh of the Li house is double-majoring in business and data science and likes pairing the SDSS and UNC Kenan-Flagler Business School experiences. “They’re each amazing in their own way,” the sophomore said. “It’s like going to a big and a small school at the same time.”

Assistant professor Dan Kessler, who heads up the Li house, likes the idea of building communities that welcome people with different backgrounds and experiences. “I’m excited that there’s this focus on inclusion and belonging,” he said, taking a break from a game of The Fuzzies. “It’s important for students to know, and it makes them feel more welcome.”

A mathematical biologist walks up to a farmer and says, “Hey, if I can guess how many sheep you own, can I take one home with me?”

The farmer says, “You’ll never get it right, so go ahead!”

The mathematical biologist runs calculations, makes his guess and it’s exactly right, so he picks up his prize and starts to walk away. The farmer says, “That was amazing! But if I can guess what your job is, will you give me my animal back?”

The mathematical biologist says, “That’s fair. What’s your guess?”

The farmer says, “You’re a mathematical biologist, and I can tell because you just picked up my dog.”

McAvoy uses the joke to illustrate a point about using data science tools. “I think there is insight into how modeling can be a double-edged sword,” he said. “It can be extremely useful, but it requires abstracting away the ‘right’ amount of information, which can feel like an art as much as a science.”

McAvoy, who has a secondary appointment in the mathematics department in the UNC College of Arts and Sciences, was among the first faculty members recruited to SDSS in 2023, and his farmer joke aligns with the school’s emphasis on a “human-centric” approach to applications of data science that solve real-world problems.

SDSS has assembled a cohort of faculty in fields that include the humanities, social sciences and medicine in an effort to facilitate collaboration and help students across Carolina develop data science skills.

“It’s a misconception that data science only happens in certain domains,” said Santiago Olivella, associate professor of political science in the College of Arts and Sciences with a full joint appointment in SDSS. “The school has given me an opportunity to see data science applied to areas that I wasn’t fully aware of, like comparative literature. I think many researchers who use data science are asking similar questions and face similar analytical challenges, just in different contexts, so understanding that can really catalyze innovation.”

Harlin Lee, assistant professor at SDSS with affiliations in the computer science and mathematics departments in the College of Arts and Sciences, said that collaboration helps generate progress beyond a single academic field.

“You don’t want to do just math sitting in your office and have it never see the light of the day,” Lee said. “You want to be working with experts in other scientific or social science domains. You want to advance the theory and methods of data science while also advancing how it’s used outside your immediate field.”

Lee is teaching Intro to Data Science this fall. “Back in college, I actually changed my major and my whole career because of one introductory course, so I am very passionate about making this a good experience that is exciting and hands-on,” Lee said. “We don’t expect you to have any computational knowledge. Just come with an appetite, and hopefully we’ll teach you enough to want to learn even more.”

Read more about these School of Data Science and Society faculty members.