Data science graduate programs: How to choose the right degree

Data is everywhere, and companies rely on it more than ever. That means skilled data professionals are in demand but figuring out how to break into the field, choose the right degree, and pay for it can feel like a lot.

That’s where graduate programs come in. Let’s walk through what they look like, what you’ll learn, how much they cost, and how to decide if this path makes sense for you.

What are data science graduate programs?

At a basic level, data science graduate programs teach you how to collect, manage, analyze, and interpret data so you can solve real-world problems. They combine technical skills with practical applications, so you’re not just learning theory. You’re learning how to use data to make decisions.

Most programs blend statistics, programming, machine learning, and data storytelling. If you’re exploring graduate education more broadly, it can help to start with learning the basics of a master's degree.

Types of degrees (MS, MPS, certificates)

Not all data science graduate degrees are built the same, so understanding the differences can help you choose a path that fits your goals.

• Master of Science (MS) in Data Science: Usually more technical and includes research, advanced math, or a thesis.
• Master of Professional Studies (MPS): Often more applied and career-focused, with an emphasis on practical skills and industry use cases.
• Graduate certificates: Shorter programs that focus on specific skills like machine learning, analytics, or data visualization without the commitment of a full degree.

Data science vs data analytics vs AI degrees

These fields overlap, but they’re not exactly the same.

• Data science: Combines statistics, programming, modeling, and data engineering to solve complex problems.
• Data analytics: Focuses more on interpreting data, building dashboards, and helping organizations make decisions.
• AI or machine learning: Goes deeper into predictive models, automation, and intelligent systems.

If you want a broader technical skill set that includes coding, modeling, and working with large datasets, a data science degree graduate program may be the best fit.

Types of data science graduate programs

Not every program looks the same, and the format you choose can shape your experience. Understanding your options can help you find the best fit for your schedule, learning style, and career goals.

Some students want a full-time, immersive program. Others need flexibility so they can keep working while they earn their degree.

Full-time vs part-time programs

• Full-time programs: Usually take about 1–2 years and may offer a more immersive experience with internships, group work, and faster completion.
• Part-time programs: Often take 2–4 years and can work better for students balancing a job, family responsibilities, or other commitments.

Online vs in-person programs

• Online data science graduate programs can be a strong option if flexibility matters to you. They may make it easier to keep working, reduce relocation costs, and fit coursework into your schedule.

• In-person programs can offer more face-to-face collaboration, campus resources, and networking. Both formats can be valuable, especially if the program is accredited and the curriculum aligns with your goals.

Bootcamps vs graduate degrees

Bootcamps and graduate degrees can both help you build data skills, but they serve different purposes.

• Bootcamps: Usually shorter, faster, and focused on immediate job-ready skills.
• Graduate degrees: Offer more depth in statistics, machine learning, and problem-solving, along with a credential that may carry more long-term value.

If you want advanced technical training, broader career flexibility, or a stronger foundation for long-term growth, a graduate degree may be the better choice.

Admissions requirements

This is often the part that feels the most stressful—but it doesn’t have to be. Many programs look at your full background, not just one number or one class.

That means your academic preparation matters, but so do your goals, experience, and readiness to handle technical coursework.

Typical prerequisites (math, programming)

MS in data science requirements can vary by school, but many programs expect you to have some background in:

• Statistics or probability
• Linear algebra
• Calculus
• Programming, often in Python or R
• Sometimes databases or computer science fundamentals

If your background isn’t a perfect match, that doesn’t always mean you’re out. Some programs offer bridge coursework or prep modules to help students get ready.

GRE and GPA expectations

Many programs look for a GPA around 3.0 or higher, though this can vary. Competitive applicants may have stronger academics, but schools often consider the full picture.

Some programs still require the GRE, but many are now test-optional. Always check individual program requirements before you apply.

Work experience requirements

Work experience isn’t always required, but it can help—especially if you’ve worked in analytics, software, business intelligence, research, or another data-related role.

For career changers, relevant projects, certifications, or hands-on technical experience can also strengthen an application.

What you’ll learn in a data science program

This is where things start to feel more concrete. Data science programs are designed to help you build both technical skills and practical experience so you can work with real-world data after graduation.

You’ll usually learn how to clean data, build models, interpret results, and communicate insights clearly to different audiences.

Core coursework (statistics, ML, Python)

Most programs focus on a mix of math, coding, and applied data skills, including:

• Statistics and probability: Understanding data patterns, distributions, and uncertainty
• Machine learning: Building models that make predictions from data
• Programming: Often in Python or R for analysis, modeling, and automation
• Data visualization: Communicating insights through charts, dashboards, and reports
• Databases and SQL: Working with structured data efficiently

These are the foundational skills that many data science roles rely on day to day.

Specializations (AI, big data, business analytics)

As you move through the program, you may have the option to focus on a specialty based on your interests and career goals.

• Artificial intelligence and machine learning: More advanced modeling and predictive systems
• Big data and data engineering: Working with large-scale datasets and infrastructure
• Business analytics: Applying data insights to business strategy and decision-making
• Data visualization or storytelling: Turning technical findings into clear, useful communication

Capstone projects and internships

Many programs include hands-on components that help you apply what you’ve learned in practical settings.

• Capstone projects: Real-world data problems using actual datasets
• Internships: Opportunities to gain professional experience and build industry connections

These experiences can help you create a portfolio, which can be an important part of landing your first role after graduation.

How much do data science graduate programs cost?

Before you apply, it’s important to understand the full cost—not just tuition. This helps you plan more confidently and avoid underestimating what the degree may really cost.

That’s especially important in graduate school, where living expenses, fees, and time away from work can all affect your total investment.

Tuition ranges (public vs private)

The cost of data science graduate programs can vary a lot depending on the school, location, and program format.

• Public universities: About $20,000–$50,000 total tuition
• Private universities: About $50,000–$90,000+ total tuition

Additional costs (fees, living expenses)

Tuition is only part of the picture. You’ll also want to think about:

• Housing and living expenses
• Books, software, and supplies
• Technology and student fees
• Transportation

Looking at the full cost of attendance—not just tuition—can give you a more accurate picture of what you’ll need.

Online program cost differences

Online data science graduate programs may reduce some costs, especially if you can keep working or avoid relocating. You may also save on commuting and some campus-related expenses.

That said, online tuition is not always lower, so it’s still important to compare the full cost and what’s included in the program.

How to pay for a data science graduate program

Paying for grad school can feel overwhelming at first, but it becomes more manageable when you break it into steps. Most students use a mix of different funding sources—and you can too.

1. Start with free money

Maximizing money you don’t have to pay back should be your first move when funding your data science degree. Scholarships, grants, and other types of aid can lower how much you need to pay out of pocket or borrow—sometimes by a lot. 

2. File the FAFSA^® and understand your aid

Filling out the Free Application for Federal Student Aid (FAFSA^®) is an important step, even for graduate school. It helps you access federal financial aid and gives you a clearer picture of what support may be available.

If you’re not sure where to start, our FAFSA^® Guide can walk you through the process step by step.

3. Explore assistantships, fellowships, and employer support

Some data science graduate programs offer funding opportunities that can help reduce your overall cost.

Assistantships may involve research, teaching, or lab support in exchange for tuition discounts or stipends. Fellowships are often merit-based and can cover part—or even all—of your tuition. Employer tuition assistance may also be available if you’re already working in a related field.

These options can be especially valuable in data science, where industry partnerships and applied projects are common.

4. Apply for scholarships

Scholarships are a great way to lower the cost of your degree because they don’t need to be repaid—and there’s no limit to how many you can apply for.

Start by exploring graduate student scholarships and use tools like Scholly Scholarships can help you find opportunities that match your background, skills, and career goals.

For an easy place to begin, consider Scholly^® Easy Apply Scholarships and the $5,000 No Essay Grad School Scholarship. The applications are quick and simple, so you can apply regularly without added stress.

5. Borrow smart

Even after scholarships and financial aid, you may still need a loan to cover remaining costs—and that’s common.

Most students start with federal student loans because they may offer benefits like income-driven repayment options. If you still have a gap, a private graduate student loan may be a good option to cover remaining costs.

Being thoughtful about how much you borrow—and having a plan for repayment—can make a big difference after graduation.

What can I do with a data science graduate degree?

A data science graduate degree can open the door to a wide range of roles across industries that rely on data to guide decisions and build products. Your exact path may depend on your technical background, specialization, and experience, but the field offers a lot of flexibility.

Common roles (data scientist, ML engineer)

Graduates often move into roles like:

• Data scientist: Builds models, analyzes large datasets, and uncovers patterns that help organizations make decisions
• Machine learning engineer: Designs and implements predictive systems and production-level models
• Data analyst: Interprets data and creates reports, dashboards, and business insights
• Data engineer: Builds and manages the pipelines and infrastructure that support data systems

Salary expectations

• Entry-level: $70,000–$90,000
• Mid-career: $90,000–$120,000
• Advanced roles: $120,000–$150,000+

Your salary can vary based on your role, technical specialization, location, and experience.

Source: U.S. Bureau of Labor Statistics (BLS).

Job demand and growth outlook

Demand for data-related skills remains strong across industries like healthcare, finance, retail, consulting, and technology. As more organizations rely on data to make decisions, roles in analytics, machine learning, and data infrastructure are expected to stay important.

How to choose the right data science program

Choosing the right data science graduate program comes down to fit—not just rankings. Because programs can vary a lot in technical depth, tools, and outcomes, taking time to compare your options can help you make a more confident, informed decision.

Accreditation and reputation

Start by looking for accredited programs with a strong reputation in data science, computer science, statistics, or related fields.

Programs connected to well-established departments—or with partnerships in tech, healthcare, finance, or other data-driven industries—may offer stronger networking opportunities and more relevant training.

Curriculum fit

Not all data science programs teach the same skills, so it’s important to look closely at the curriculum.

Some programs are more technical, with a heavier focus on machine learning, advanced statistics, and data engineering. Others are more applied, with more emphasis on analytics, visualization, and business decision-making. Look for programs that teach tools commonly used in the field, such as Python, R, SQL, and cloud platforms.

Cost and ROI

Cost matters but it’s not just about tuition. You’ll want to compare the total cost of attendance against likely career outcomes.

A higher-cost program may still be worth it if it offers strong career placement, a better technical fit, or access to higher-paying roles.

Career placement support

One of the biggest differences between programs is the level of career support they offer.

Look for schools that provide internship support, resume help, employer connections, capstone projects, and strong placement outcomes. In data science, hands-on work can matter just as much as the degree itself.

Top factors to compare across programs

When comparing programs, it helps to focus on a few key factors that can make the biggest difference in your experience and long-term outcomes.

Looking at these side by side can make it easier to find a program that fits your goals, budget, and lifestyle.

• Cost: Tuition, fees, and total cost of attendance
• Duration: Full-time vs part-time timeline
• Format: Online, in-person, or hybrid
• Outcomes: Internships, job placement, and salary potential

Tools like Scout College Search can help you compare programs side by side.

Is a data science graduate program worth it?

Grad school is a big commitment, so it’s worth thinking through the return on your investment. For many students, the right program can open doors to higher-paying, more technical, or more specialized roles.

A data science graduate program may be worth it if it helps you build in-demand skills, move into a new field, or grow into more advanced roles. It may be less necessary if you already have strong hands-on experience and can reach your goals through work experience or other training paths.

Your next steps

If you’re still figuring things out, that’s okay—most people are. What matters is that you’re taking the time to explore your options and think carefully about what kind of future you want to build.

Start by comparing programs, looking closely at costs, and thinking about the technical skills you want to build. Use Scout College Search to explore programs and Scholly Scholarships to look for funding opportunities that match your goals.