Find Master's in Data Science and Artificial Intelligence Programs

What Are Master’s in Data Science and Artificial Intelligence Programs?

Master’s degrees in data science & AI are the new kids in town, blending two related disciplines into one field of study. In most cases, universities have taken their primary data science degree—with structured training in applied statistics, programming, databases, and machine learning (ML)—and tacked on a concentration or electives in artificial intelligence topics such as deep learning and natural language processing (NLP). That’s the most common format for online master’s degrees in data science in AI in our rankings.

But it’s important to point out that on-campus master’s programs in our rankings have a more nuanced approach to the intersection of data science and artificial intelligence. Here you’ll find degrees that explore high-level research topics in ML, give you options to customize your curriculum with advanced topics (e.g. bioinformatics), and encourage you to collaborate with industry partners. Because every degree is unique, we’ve included a “Strong Pick For” feature in our rankings to help you narrow down your choices.

Looking for programs in your own area? Skip ahead to our comprehensive state-by-state directory.

Rankings Methodology

We had to make a lot of tough decisions when it came to AI-focused graduate degrees for data scientists! We were on a mission to find you the best online programs in DS & AI and the best campus programs in DS & AI so you could compare their features side-by-side.

• Focus: As a start, we decided that all degrees in our rankings had to have AI or Machine Learning (ML) in their name. That means we excluded data science programs that can be customized with AI & ML electives (e.g. UC Berkeley’s Online MIDS). However, we have highlighted these degrees in our Honorable Mentions section. To keep the focus on computing, we’ve also excluded business-focused analytics programs with AI specializations. See our Analytics section for more on this alternative pathway.
• Quality: Our algorithm factors in U.S. News & World Report and QS World University rankings across a range of subjects, including AI, computer science, statistics, and data science. But we also consider AI research output! This means that a school that does well in multiple fields (e.g. Northwestern) will place higher than a university that only excels in one area.
• Outcomes: It’s important for us that you see a return on your investment. So our algorithm also factors in completion rates for relevant graduate programs (e.g. data science, computer science, computer & information sciences) and post-graduation earnings. That’s one of the reasons Cornell is #1 in our rankings of campus programs.

Here’s the most important finding from our research—every data science & AI degree has a distinct flavor. Some programs are tailored to career changers and folks who are new to the field of data science (e.g. Syracuse and Southern Methodist University). Others are designed to help those with a background in computer science voyage into advanced DS & AI research (e.g. Cornell). Some lean towards theory. Others focus on industry applications.

Take the extra minute to read the section in our rankings on the program’s strengths in data science and AI & ML, including the faculty bios and related research initiatives. If you’re socking $20k-$80k into a degree, you’ll want to take full advantage of departmental resources.

Honorable Mentions: Master’s in Data Science Programs with AI & ML Electives

When we first assembled our list of graduate programs that combined data science training with AI topics and ran our algorithm, three programs jumped out at us for quality. They were:

1. UC Berkeley: The Online Master of Information and Data Science (MIDS) is a well-known alternative to online MSAI programs. The curriculum has plenty of advanced electives in AI topics, including Machine Learning at Scale, NLP with Deep Learning, Machine Learning Systems Engineering, and Generative AI.
2. Boston University: The Master of Science in Data Science (MSDS) is available in an online or on-campus format. The curriculum for the online version achieves a lovely balance of data science skills and advanced AI & ML concerns. Compare it to the curriculum for its on-campus program to spot the differences in approach.
3. University of Chicago: The Master of Science in Applied Data Science (MS in ADS) from the Data Science Institute is available in an online or on-campus format. The curriculum for the online degree has a core of statistics, time series analysis, and ML that can be personalized with electives in Advanced ML & AI, ML Operations, NLP, and Real Time Intelligent Systems.

The only reason we have not featured them in our profiles is because they do not have specific concentrations in AI or ML. If we had expanded our parameters to general data science degrees, we would have been drowning in options. Nevertheless, we wanted to flag these three schools for your consideration. They’re very strong!

It’s a similar story for highly regarded master’s degrees in computer science or applied statistics. We could spend years talking about how you can customize a top-notch program with AI and data science work, especially if that degree is taught by faculty who are on the cutting-edge of research. (For more on CS options, see our rankings of the Best Master’s Degrees in Artificial Intelligence.)

Master’s in Analytics Programs with AI or ML Concentrations

Universities have been quick to jump on the artificial intelligence bandwagon and add AI & ML concentrations to their analytics degrees. We briefly considered profiling them in our rankings, but we decided against this for two reasons:

• Focus: Analytics is a different field to data science. Analytics professionals use existing tools & methods to solve problems and generate data-driven insights. Data scientists are inventors, programmers, and statisticians who are experts in computational methods and ML. They have the expertise to create complex models and systems. If you’re interested in data science, you need a rigorous, interdisciplinary degree that’s going to give you those “inventor” skills.
• Relevance: Analytics degrees with AI tracks are frequently (but not always) developed by the School of Business, not the Department of Computer Science. That means they’re more preoccupied with areas like Business Intelligence (BI), financial decision-making, and advanced analytics techniques. Stevens Institute of Technology’s Online Master of Science in Business Intelligence and Analytics – Data Science & AI is a great example of this approach.

Even some data science degrees can lean more toward analytics than true data science. For instance, we decided to exclude the University of Dallas’s sturdy Master of Science in Data Science & Artificial Intelligence from our campus rankings because of its business origins and its coursework. It’s still a good program—it just didn’t have the same parameters as the other MSDS programs that we profiled.

AI Analytics Degrees from Schools of Computing

• Nova Southeastern University: Master of Science in Data Analytics and Artificial Intelligence
• University of Central Florida: Master of Science in Data Analytics – Artificial Intelligence

AI Analytics Degrees from Schools of Business

• University of Dallas: Master of Science in Data Science & Artificial Intelligence
• Stevens Institute of Technology: Online Master of Science in Business Analytics
• University of Texas at Dallas: Master of Science in Business Analytics & Artificial Intelligence
• Dakota State University: Master of Science in Analytics and Applied Artificial Intelligence
• Indiana Wesleyan University: Online Master of Science in Artificial Intelligence – Data Analytics
• Rutgers University: Master of Information Technology & Analytics – Data Analytics and Machine Learning

Data Science Graduate Degrees: AI vs. ML Specializations

In our online and on-campus rankings, you’ll encounter data science graduate programs that feature a specialization in Artificial Intelligence (AI), as well as data science degrees that feature a concentration in Machine Learning (ML). So which one do you choose? And what’s the difference?

The answer is—there may not be much of a difference. If you start to examine the plans of study, you’ll notice that the most popular trio of topics for either an ML or an AI concentration are:

• Natural Language Processing (NLP)
• Deep Learning
• Advanced ML course (e.g. Statistical Machine Learning)

Some programs are more innovative and flexible, but this is the standard template. For this reason, we recommend you ignore the degree title and focus on the course descriptions to learn what’s actually covered. We’ve included curricula links in our profiles so you can check the details.

Machine Learning Coursework

Thanks to its foundations in statistics and algorithms, machine learning (ML) has always been tightly associated with data science. That’s why every single degree in our rankings features a mandatory core course in “Applied Machine Learning,” “Practical Machine Learning,” or “Machine Learning.” No matter what program you choose, you’re going to be schooled in ML models and methods.

If you’re determined to head into advanced ML territory, pick the schools with relevant elective courses and faculty who are working on high-level research. For example, Arizona State University’s MS in DSAE has a specific concentration in Bayesian Machine Learning. Remember, too, that you can use your capstone or thesis to test out innovative ML methods and techniques.

Artificial Intelligence Coursework

Machine learning is just one subset of AI. Other subsets include areas like deep learning, NLP, neural networks, robotics, and computer vision. That’s usually what universities mean when they say they offer AI coursework within a data science program. As we’ve mentioned, deep learning and NLP are—by far—the most popular AI courses that we’ve seen included in AI or ML concentrations. In some degrees, you’re not going to get much more than those two subjects.

Having said that, there are plenty of data science degrees that will give you room to play. One of the best examples of this is FIU’s MS in DS & AI. In the AI specialization, you can choose five courses from a whole host of topics, including Neural Networks, Affective Intelligent Agents, Bioinformatics, Advanced Human-Computer Interaction (HCI), and even Game Theory. If you really love AI, don’t worry—you can find something that suits your ambitions.

Skills to Look For in Graduate-Level Coursework

Buckle up—data science is a highly competitive field for employment. You’re going to be vying with candidates who have earned master’s degrees in pure computer science and advanced statistics, as well as folks who hold a PhD. So it pays to be extra careful when you’re assessing the skill sets that are taught within a program. You can’t afford to walk away with “fluff.”

Fundamental Data Science Skills

At a bare minimum, data scientists should be well-versed in:

• Statistical Analysis
• Data Mining
• Programming in Python & R
• Machine Learning (ML)
• SQL & Databases
• Data Visualization & BI Tools (e.g. Tableau)
• Big Data Technologies (e.g. Spark & Hadoop)
• Cloud Computing

Almost every master’s in data science will cover these skills in their core coursework. Some programs will be very basic and walk you through the fundamentals of programming. Others will expect you to be coming into the degree with intermediate skills. This is usually made clear in the admissions section. However, if you’re finding the experience level hard to evaluate, talk to the program coordinator and recent alumni.

Artificial Intelligence Skills

Within the employment market, some of the most requested AI skill sets for data scientists include:

• Natural Language Processing (NLP)
• Machine Learning Algorithms & Models
• Deep Learning
• Neural Networks
• AI Model Deployment
• AI Cloud Services

As we’ve mentioned, NLP and deep learning are often included in data science degrees with an AI or ML concentration. But you will need to locate and read the course descriptions to discover how deep the topic goes. Since most of your degree will be taken up with data science courses, you won’t have a lot of time to explore advanced AI techniques.

Practical, Real-World Experience

Skills-related keywords will get you through the first wave of job applications and résumé scans. But once you reach the next stage, employers are going to be asking hard questions about your level of practical experience.

• Portfolio: Does your master’s portfolio contain examples of real-world data science projects that you tackled in class?
• Capstone: Which company/organization did you partner with on your capstone? Which high-level skills in ML and AI did you deploy to solve their problem?
• Internship: Did you select an AI-related internship as one of your MS electives? Who did you work for?
• Research: Have you collaborated with any faculty members? Were you involved with work in the Data Science Institute or AI labs?

You can plan ahead for these questions! Chat with the program coordinator about your career goals. Have a virtual coffee with the most relevant faculty members in the program before you start your degree. Investigate what’s happening in the labs and institutes. Ask if you can speak to someone on the department or program’s Industry Advisory Board. Build your network of contacts early and focus on applying theory to practice.

Insider Tips on Choosing a DS & AI Program

We hope our online and on-campus rankings have made it easier to compare the strengths (and weaknesses) of the coursework in each data science & artificial intelligence degree. Once you have a shortlist of master’s programs, you can narrow your choices down even further by doing the following:

• Speak Directly to Alumni: We’ve included links to profiles of MSDS alumni whenever possible, but you’ll also be able to find recent graduates on LinkedIn and Reddit. Reach out to them! They’ll give you all the gossip on the best courses and professors, as well as nitty-gritty details on the industry relevance of the coursework.
• Ask for Examples of Capstone Projects: If you can’t find samples on the program website, ask the program coordinator for a list of recent MSDS titles and topics. This is one of the quickest ways to determine whether the degree is too niche, too broad, or just right for your needs.
• Try Before You Buy: In some online MSDS programs, you’ll be allowed to monitor—or even take—an introductory class before you commit to the degree. This may be more difficult to do with a campus program, but there’s no harm in finding out if it’s possible.

Career Paths with a Master’s in Data Science & Artificial Intelligence

Why Earn a Master’s Degree?

Most folks start thinking about a master’s degree in data science & artificial intelligence at three key points in their career:

1. They’re in a business or analytics-related role and they’re feeling frustrated by their lack of knowledge and advanced skills in data science and AI.
2. They’re folks who have been strongly advised by their employer or mentors that an MSDS would make them eligible for promotion, a pay rise, and leadership roles.
3. They’re considering a doctorate, but they’re not quite ready to make the jump.

Graduate education is expensive, so before you start submitting applications, assess your level of experience and what you hope to get out of a master’s degree. You might want a research-focused campus program that could prep you for a PhD in the future. Or perhaps you need an industry-focused qualification that sets you up for AI and ML engineer jobs. Or maybe you’re looking to specialize in a specific field (e.g. bioinformatics). Whatever your goal, there is bound to be a degree that suits.

Common Job Titles for MSDS in AI Graduates

• Data Scientist
• Data Engineer
• Big Data Engineer
• Data Architect
• Machine Learning Scientist
• Machine Learning Engineer
• Machine Learning Developer
• Statistician
• Bioinformatician
• Geospatial Data Scientist
• AI Engineer
• AI Analyst
• Chief AI Officer (CAIO)
• AI Research Scientist

Identify the data science roles that appeal to you most and use generative AI to analyze current postings on job sites. What degree is preferred? What skill sets are prioritized? Get your checklist in hand so you can compare it against the program’s course descriptions.

Data Science & AI FAQ

Is a Data Science Degree in AI Looked Down Upon By Employers?

The answer is maybe. The common argument is that employers are going to favor candidates with a master’s degree in computer science, statistics, math, or hard sciences over candidates with a wishy-washy graduate degree in data science (e.g. MSDS) or artificial intelligence (e.g. MSAI).

There is some truth to this statement. As one Reddit contributor noted:

“When I am screening candidates for our internship programs, I will always prefer the Masters [in] Statistics or Masters in pure CS over Masters in DS, or Masters in Hard Sciences over a masters in DS, because I do think DS degrees are less rigorous. That doesn’t mean they are completely worthless.”

Note the last point. You may find that you’re in a position where a degree in computer science feels too heavy on theory and breadth courses and a statistics degree would take you away from industry applications and practical AI work. That’s when an MSDS may be the ideal solution.

You’ll have an easier time with hiring committees if you’re accepted into a data science & AI program from a school with a prestigious reputation in computer science (e.g. UC Berkeley, Cornell, etc.) and you really concentrate on taking full advantage of the university’s research & career resources while you’re studying.

Which Path is Better for an MS in Data Science & AI—Online or On-Campus?

It’s different strokes for different folks. On-campus master’s degrees in data science & AI are usually technically challenging, with plenty of opportunities to get involved in the department’s artificial intelligence research and labs. These programs often prioritize candidates who have an undergraduate degree in computing or a related field.

Online degrees in data science & AI may be better for career changers and folks without a lot of background in computer science & statistics. As an online learner, you’re going to have to create your own opportunities. That means being proactive about working with the school’s corporate partners and finding ways to structure your capstone and internships to appeal to employers.

The easiest way to get a grip on this question is to compare Northwestern’s Master of Science (MS) in Machine Learning and Data Science (MLDS) with its Online Master of Science in Data Science – Artificial Intelligence. You’ll immediately spot the contrasts.

What are the Admissions Requirements for an MS in Data Science & Artificial Intelligence?

• Undergraduate Degree: Schools will typically be looking for candidates with a bachelor’s degree in computer science, statistics, engineering, mathematics, or a closely related discipline. However, there are plenty of universities that are willing to consider folks from other fields as long as they can prove that they have completed prerequisites.
• Prerequisites: MSDS candidates will usually need to have knowledge of programming languages (e.g. R and Python) and college-level mathematics (e.g. statistics, calculus, and linear algebra). Many schools will offer prerequisite courses in these areas. Southern Methodist University even runs an online Data Science Boot Camp.
• GPA: The standard baseline GPA is 3.0 for undergraduate work. However, candidates at top-tier schools may be averaging 3.3-3.5 or higher.
• Test Scores: GRE scores may or may not be required—many schools did away with this requirement during the COVID-19 pandemic and never went back. International candidates with limited English skills may need to submit TOEFL or IELTS scores.
• Work Experience: Relevant work experience in analytics, statistical & data science roles may not be necessary for admission, but any proof of it will help to strengthen your application!

How Much Can I Earn with a Master’s in Data Science & Artificial Intelligence?

We’ve listed potential job titles for data scientists with AI skills in the Career Paths section. Pick a role and then use generative AI to scan salary estimates in current job listings. Be sure to narrow it down by geographic area and weigh those numbers up against the cost of living.

The Bureau of Labor Statistics (BLS) keeps a close eye on wage & employment data for data scientists. We’re particularly fond of the BLS because it breaks down these data by state and metropolitan area:

• Washington State, California, Virginia, and the New York area still continue to pay top dollar for data scientists (though they can be expensive places to live). Intriguingly, Wyoming and Idaho also have high annual mean wages.
• When you look at the state employment maps, you can see the gradual movement of data scientists from the West Coast to interior tech hubs such as Utah and Colorado.

Salary sites like Payscale can also help give you broad estimates, but remember that the numbers will depend on your level of experience. MAANG companies may pay $110,000 to $200,000, but an entry-level data scientist could be looking at something more like $85,000 as a starter salary.

What is an R1 Research University?

R1 is a designation that’s given by the Carnegie Commission on Higher Education. It means that it has been classified as a doctoral university with very high research activity. In 2025, this will mean that a university has at least $50 million in research expenditures and grants at least 70 research doctorates.

R1 research universities generally have a strong reputation amongst employers. But this may not be as critical if you’re currently employed and using the MSDS to qualify for a job promotion or pay rise.