What Can You Do with a Data Science Degree? More Than You Might Think

From healthcare and finance to sports, sustainability and artificial intelligence, data science skills can open doors across industries. Here’s how UVA helps students turn possibilities into a career path.

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If you are considering a degree in data science, you may be asking a deceptively simple question: What can I do with a data science degree?  

The short answer: a lot.  

A data science degree can prepare graduates for careers in data analysis, machine learning, artificial intelligence, business intelligence, data engineering, consulting, healthcare analytics, financial analytics, and dozens of other data-driven roles across industries.

Data science is used across nearly every industry and application, and careers involving data are not limited to people with the job title “data scientist.” Data science graduates may work as data analysts, machine learning engineers, business intelligence professionals, data engineers, and other roles that combine technical skills with expertise in a particular industry or field of study.

That flexibility can be one of the greatest advantages of studying data science. It can also make choosing a career path feel overwhelming.

Data science students receive individually tailored career guidance, which begins by helping students consider two questions: What kind of work do I want to do, and where do I want to do it?

We recently heard from Stephanie Joynes, assistant director of career and professional development at the UVA School of Data Science, as she addressed an auditorium of new undergraduate and graduate data science students. The following are key takeaways to help guide your data science career exploration.

What jobs can I get with a data science degree?

A data science education can prepare students for many different career functions, including:

• Data analysis
• Machine learning and AI
• Data engineering and systems
• Business intelligence
• Predictive analytics
• Data visualization
• Analytics and decision-making

“The important distinction is between function and industry,” said Joynes. She pointed out that a data analyst, for example, could work in healthcare, financial services, professional sports, government, retail, or environmental sustainability. A machine learning engineer might develop AI systems for a technology company or work on models supporting healthcare, consumer products, or national security.

Rather than thinking of data science as one career, she advised, it can be useful to think of it as a set of skills that can be applied to problems in almost any field.

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Which industries hire data science graduates?

Data science skills are used across industries, impacting every sector of the economy. The School of Data Science hosts a career development series, “Modeling Your Future,” where advisors like Joynes highlight opportunities in industries including:

• Technology and artificial intelligence
• Healthcare and pharmaceuticals
• Finance and banking
• Consulting
• Government and national security
• Retail and e-commerce
• Sustainability and the environment
• Entertainment and media
• Sports

“Even within one industry, there can be many applications,” said Joynes.  

In sports, for example, data science can support player performance analytics, injury prevention, wearable technology, marketing analytics, consumer insights, and fan engagement. In healthcare, data professionals can work on everything from hospital operations and patient experiences to insurance, risk assessment, and pharmaceutical research.

The question Joynes challenged students to think of was less about “Where is data science used?” and more about “What problems do I want to use data science to solve?”

Is data science a good career if I don't know exactly what I want to do?  

Yes. Data science can be a good career choice if you don’t know exactly what you want to do because it gives you skills that can be applied across almost any field. You don’t have to choose between being interested in data science and being interested in people, policy, the environment, health, business, the arts, or social issues. Data science gives you tools to explore those interests and solve problems within them.

“You don’t have to have a commitment from the beginning of your program and know what you want to do by graduation,” said Joynes. “Make time to explore new things, research new companies, and learn how organizations are using data science in real-world scenarios.”  

For those who might gravitate toward a more liberal arts focus because they have many interests, data science can offer similar intellectual flexibility with a highly practical skill set. You can use data to study climate change, improve health outcomes, understand human behavior, inform public policy, strengthen communities, make organizations more effective, or develop responsible AI.

At the UVA School of Data Science, we encourage students to approach career exploration almost like a data science problem: form a hypothesis, test it, and use what you learn to make your next decision.  

“Choose an industry that interests you,” advised Joynes.  She suggests spending time learning about its companies, jobs, and people: “Attend employer information sessions. Talk with alumni and professionals. Follow organizations and professionals in the field. Then ask yourself: Does this still interest me?”  

How should I explore data science careers?

Students should explore data science careers by identifying industries that interest them, testing those interests through coursework, projects, networking, internships, and conversations with professionals, then refining their goals based on what they learn.

With data science used across nearly every industry, figuring out where you want to apply your skills can feel overwhelming. You might be interested in artificial intelligence, healthcare, sports, finance, sustainability, or something you haven't encountered yet.

Holly Stancil, career advisor at the UVA School of Data Science, encourages students to approach career exploration much like a data science problem: test possibilities, evaluate the results, and adjust accordingly.

“View your life like a scientist,” Stancil said. “Pick one, experiment with it, decide whether it’s worth keeping, and move on to something else.”  

Do data science students need an internship to get a job?

No, data science students do not necessarily need an internship to get a job. While internships provide valuable experience, employers also evaluate technical projects, portfolios, coursework, research, and demonstrated problem-solving ability.

Internships can provide valuable professional experience, but they are not the only way to demonstrate that you can apply data science skills.

For students who are just starting their degree, whether it be undergraduate or graduate, career advisors recommend focusing on projects and a portfolio, particularly as students develop the technical skills employers seek.

“A personal data science project can start with a question you genuinely care about,” explained Joynes. “You might analyze environmental data, historical sports statistics, museum collections, or another publicly available dataset. Clean the data, ask questions, analyze it, and build a visualization. Then document what you discovered.”

That project can then be listed on your resume, LinkedIn profile, GitHub account, or personal portfolio as evidence of your abilities. The project does not have to change the world. It needs to show that you can take something from an initial question through analysis to a meaningful result.

How do I build a data science portfolio with no experience?  

Build a portfolio by applying skills you learn in class to independent projects and datasets that interest you. Those projects can then be showcased to demonstrate your skills, interests, and career goals.

“Start with the work you are already doing,” said Joynes. She suggested applying a new skill learned in class to another dataset. These become independent projects around subjects that interest you that can be added to your portfolio.

She emphasized that a strong student portfolio can demonstrate technical proficiency as well as motivation and curiosity. It can also help answer an important question for recruiters: What do you want to be known for? If you are interested in sports analytics, for example, projects involving sports data can help reinforce that path. If you want to work at the intersection of data science and healthcare, your coursework, projects, professional experience, and networking can begin pointing in that direction. If AI interests you, think about questions relating to AI models that haven’t been answered.

How important is networking for data science careers?

Technical skills matter, but so do relationships. It can be what sets you apart in the applicant pool.

As applying for jobs becomes easier through online platforms and AI-assisted tools, employers may receive large numbers of applications, sometimes thousands per job posting. Networking can help students learn about organizations, understand career paths, and build genuine connections with people already working in a field.

Networking also does not have to begin with asking someone for a job. It can start with introducing yourself at an employer presentation, asking a thoughtful question at a career event, connecting with alumni, or talking with another data science student.

According to Joynes, those small interactions add up. At UVA, students can tap into the University's alumni network as well as a growing community of data science students and graduates.  

At the UVA School of Data Science, students across the B.S., M.S., and Ph.D. programs get to know one another in small, working cohorts. This is important because today's classmates may become tomorrow's colleagues and the foundation of your professional network.

How do I choose the right data science career?

Salary and job title are only part of the equation. Career advisors remind students to also consider what they value in their work. That might include:

• The mission of an organization
• Salary and advancement potential
• Geographic location
• Remote, hybrid, or in-person work 
• Work-life balance 
• Job stability 
• The social impact of the work 
• Ethical questions surrounding how data and AI are used

“We want to make sure that when you are going into a certain field, you’re going into a field that you feel good about, that you’re excited about,” said Joynes. “Whatever the mission of the company you are applying for and interviewing with, what they’re doing as a mission should align with what you want to be using your data science for.”

She pointed out that different industries offer different tradeoffs. A student passionate about environmental sustainability, for example, may prioritize mission over salary. Someone else might place greater emphasis on compensation, location, or opportunities for advancement.

There is no single “best data science career.” Students are reminded that their goal is to find a career that combines their skills, interests, and values.

“Everybody has different priorities when it comes to their job search,” said Joynes, “and that’s one of the reasons why we work with students individually so they can build the strategy that works for them.”

Why study data science at UVA?

Data science at the University of Virginia combines technical expertise with real-world application. Students learn how to work with data, build models, and communicate insights while exploring how those skills can be applied in healthcare, business, sports, technology, sustainability, government, and other fields that matter to them.

Is a data science degree worth it?

At the UVA School of Data Science, we believe the value of a data science degree extends beyond preparing for a single job title.

Students learn how to work with data, ask meaningful questions, develop and evaluate models, communicate findings, and use evidence to solve problems. Those abilities can be applied across industries and can evolve as technology and the job market change.

Just as importantly, students can learn how to connect technical expertise with a domain they care about.

That career preparation is integrated into the student experience at UVA. The School's career team provides individual advising, as well as programming on career exploration, portfolios, networking, technical interviews, and professional development. Students also have access to UVA career resources, employer events, and alumni connections. The “Modeling Your Future” series brings career advisors, recruiters, and alumni into the conversation to help students understand how their education connects to opportunities after graduation.

The objective isn't simply to help students land a job. It is to help them get personalized guidance to map out where they want data science to take them.