Where to Find the Best Data Science Master’s Programs

Data science is still hot. Yes, the applicant pool for each data scientist job listing is growing. Yes, it’s been almost a decade since the Harvard Business Review called data science the sexiest job of the 21st century. Granted, data science is no longer “the next big thing.” It is still big, however.

A lot has changed since the turn of the millennium. Among those changes: a big increase in the number of graduate programs dedicated to data science degrees. That’s a big part of why there are now so many more data scientists than there were even two years ago, when LinkedIn reported there were over 150,000 more data science jobs than there were qualified professionals to fill them. Competition for open positions is a lot fiercer than it once was.

What can that tell us about whether data science master’s degrees are worth it? Not enough, actually, because programs vary significantly from school to school. There are rigorous Master of Science in Data Science programs at top computer science schools. There are also data analytics degree programs billed as data science programs at plenty of unranked colleges and universities. It can be hard to tell them apart without a deep dive.

What we can conclude from the glut of data scientists is that if you want to work in this field, you’re going to need a master’s degree—full stop. Working your way into data science from analytics after completing a boot camp or a MOOC sequence won’t cut it in a job market where even smaller companies can afford to be more selective about whom they hire. Graduating from one of the top data science master’s programs can potentially give you a huge advantage in your job search.

In this article about the best data science master’s programs, we cover:

• What is a data science master’s, and who pursues this degree?
• What do students in Master of Data Science programs study?
• Which schools have the best data science master’s programs?
• What sets these programs apart from other data science master’s programs?
• Is it possible to earn a data science master’s degree online from a top school?
• What can I do with a Master of Data Science degree?
• Will enrolling in one of the best data science master’s programs boost my salary?
• Can I work in data science without earning this degree?

What is a data science master’s and who pursues this degree?

Dedicated data science master’s degree programs are for students who already have some experience in analytics and advanced technology or a background in IT, computer science, engineering, mathematics, or technology. They want to dive deeper into the applications of analytics. They also hope to gain industry experience and contacts that will help them land higher-paying data science positions. They’re passionate about finding new ways to leverage the vast quantities of untapped data collected across industries. In some cases, they aspire to transition into higher-ranking management and executive positions like Chief Data Officer or Chief Technology Officer.

According to the University of Virginia (Main Campus)’s program guide for its data science master’s program, students who choose this discipline “work at organizations like Amazon, Green Bay Packers, CIA, Capital One, Google, MITRE, Morgan Stanley, NIH, McKinsey, Workday, Deloitte, Meetup, and Northrop Grumman.” They have titles like “machine learning engineer, senior data analyst, data scientist, deep learning researcher, data engineer, analytics consultant, data science developer, and more.”

What do students in Master of Data Science programs study?

There isn’t yet a set curriculum in data science programs. Some programs devote more credit hours to programming and math, while others spend more time on business intelligence. However, that will almost certainly change as more colleges and universities develop dedicated data science master’s degree programs.

Until then, the coursework in most full-time and part-time Master of Data Science programs will likely continue to cover topics related to data analytics, data engineering, applied statistics, quantitative analysis, and business intelligence. Students in these programs take core courses like:

• Algorithms for Data Science: Focuses on algorithms specifically designed for solving data-intensive problems. Covers techniques for processing, analyzing, and extracting insights from large datasets.
• Applied Statistics & Experimental Design: Teaches statistical methods and principles for designing experiments. Includes practical applications in data collection, analysis, and interpretation.
• Computer Systems for Data Science: Covers the hardware and software systems necessary for data science, including distributed computing, parallel processing, and efficient data storage and retrieval.
• Data Interaction and Visual Analytics: Explores interactive tools and techniques for data visualization and analysis. Teaches how to create dynamic visualizations that allow users to explore and interpret complex data.
• Data Management for Data Science: Teaches principles of managing data, including data cleaning, integration, transformation, and storage. Focuses on handling large and diverse datasets.
• Data Mining Tools: Provides practical skills in using software tools for data mining. Covers algorithms and techniques for uncovering patterns and relationships in large datasets.
• Data Warehousing Strategies: Focuses on designing and implementing data warehouses. Covers topics like data modeling, ETL processes, and OLAP.
• Data Structures and Algorithms: An essential computer science course covering fundamental data structures (like trees, graphs, and hash tables) and algorithms for data processing and analysis.
• Exploratory Data Analysis and Visualization: Teaches techniques for exploring and visualizing data to uncover initial patterns, characteristics, and points of interest.
• Human-Centered Data Science: Focuses on the intersection of data science and human-computer interaction. Covers user-centric approaches to data collection, analysis, and visualization.
• Information Visualization: Teaches principles and techniques of visualizing complex information. Covers graphical standards and tools for creating effective and interpretable visualizations.
• Machine Learning for Data Science: Covers the basics of machine learning algorithms and their applications in data science, including supervised and unsupervised learning techniques.
• Massive Data Storage and Retrieval Tools: Focuses on tools and technologies for storing and retrieving large volumes of data. Includes distributed databases and big data technologies.
• Probability Theory: Covers the fundamentals of probability theory, a foundational aspect of statistical analysis and predictive modeling.
• Scalable Data Systems & Algorithms: Teaches how to design data systems and algorithms that can scale effectively with large, growing datasets.
• Statistical Inference and Modelling: Focuses on methods for drawing conclusions from data and building statistical models. Includes hypothesis testing and regression analysis.
• Statistical Machine Learning for Data Scientists: A more advanced course on machine learning, focusing on statistical methods and theory underlying ML algorithms.
• Statistical Methods & Probability: An introductory course covering basic concepts in statistics and probability, essential for data analysis.
• Software Design for Data Science: Teaches software design principles and best practices tailored for data science applications, including coding, version control, and software engineering techniques.
• Working with Massive Data Sets: Focuses on practical challenges and techniques for working with extremely large datasets, including data cleaning, processing, and analysis at scale.

Most data science graduate programs include a capstone course, research project, practicum, or internship requirements that allow students to address real-world problems related to computational data science. At Stanford University (which offers a Master of Science in Statistics with a data science track), for example, students must complete a capstone project, independent master’s-level research, or project labs in data science and analytics.

Which schools have the best data science master’s programs?

Some of the best master’s in data science programs can be found at:

• Carnegie Mellon University’s School of Computer Science
• Columbia University’s Data Science Institute
• DePaul University’s College of Computing and Digital Media
• Harvard University’s John A. Paulson School of Engineering & Applied Sciences
• New York University’s Center for Data Science
• Rutgers University – New Brunswick’s School of Arts and Sciences
• Stevens Institute of Technology
• Tufts University’s School of Engineering
• University of Minnesota – Twin Cities’s
• University of Rochester’s Goergen Institute for Data Science
• University of Virginia’s School of Data Science
• University of Washington’s College of Science & Engineering

You should also look into graduate schools with strong Master of Science in Computer Science programs or Master of Science in Business Analytics programs that offer a data science track. These programs are often identical in scope to data science master’s programs. It’s also possible to earn a Master of Business Administration in Data Science (University of Michigan – Ann Arbor’s Stephen M. Ross School of Business offers one). These programs are a good option for potential data scientists who prefer working on the management side to the tech side.

What sets these programs apart from other data science master’s programs?

Master of Science in Data Science programs are still relatively rare. While data analytics programs and applied statistics programs often cover a lot of the same material, the best dedicated data science programs do have some features that set them apart.

First, the best data science master’s programs tend to be geared toward passionate professionals who already have robust analytics and statistics knowledge. At some colleges and universities, applicants must know multiple programming languages (e.g., Python, SQL, and R) and have an academic background and work experience involving information science, information systems, cloud computing, computer science, analytics, or statistics. Many programs only accept students who already have a working knowledge of the programs and tools data scientists typically use.

Second, the best data science master’s programs offer focused concentrations designed to give newly minted data scientists deep domain knowledge. Examples of specializations data science students can choose from include:

• Big Data analytics
• Computational methods
• Healthcare
• Hospitality
• Mathematics and data
• Marketing
• Natural language processing
• Physics

Third, the top data science programs tend to be offered not through business schools—as is often the case with data analytics master’s degrees—but through engineering schools, computer science schools, and data science schools.

Finally, the best data science master’s programs are offered by colleges and universities that can provide students with robust career support before and after graduation. They have relationships with employers and researchers. These make it easy for students to find high-value internship placements and make the professional connections that lead future opportunities.

Is it possible to earn a data science master’s degree online from a top school?

You can study data science online at the master’s degree level, but you’ll only have a handful of programs to choose from if you want to enroll at a top school. There are strong online data science master’s degree programs at:

• DePaul University, which also offers its Master of Science in Data Science program online
• George Mason University, which offers an online Master’s in Data Analytics Engineering
• University of Southern California, which offers an online Master of Science in Applied Data Science
• University of Virginia, which offers a Master of Science in Data Science

The curriculum in online data science master’s degree programs at these schools covers the same material as on-campus programs. However, you may miss out on valuable experiential learning opportunities and events designed to connect students with powerful industry insiders. You’ll learn everything you need to know to become a data scientist in an online program, but getting a job after graduation may be more difficult—even if you graduate from a relatively prestigious school.

What can I do with a Master of Data Science degree?

Because data scientists have the skills and knowledge to work on various projects involving reporting, dashboarding, machine learning, data analysis, cyber security, and robotics, they can work in many roles. Some careers you might pursue after graduating with this degree include:

• Big Data solutions architect: This professional designs and manages large-scale data processing systems and databases. They create the architecture that helps analyze and process big data from multiple sources, ensuring scalability, efficiency, and security.
• Business intelligence analyst: They analyze complex data sets to identify business and market trends. By using data analytics and business intelligence tools, they help organizations make informed decisions to improve efficiency and profitability.
• Business systems analyst: This role involves analyzing and improving computer systems and business processes. They bridge the gap between IT and business, ensuring that data systems meet the needs and goals of the organization.
• Chief Data Officer: A C-level executive responsible for an organization’s data management strategy, governance, and policy. The CDO ensures data quality and accessibility and drives innovation and business value through data analytics.
• Chief Information Officer: As a top executive, the CIO is responsible for the overall IT strategy and computer systems required to support the organization’s goals and operations, including managing big data initiatives.
• Data engineer: Data engineers build and maintain the infrastructure and tools for handling large amounts of data. They focus on the practical application of data collection and processing, preparing big data for analytical or operational uses.
• Data mining analyst: This role involves examining large databases to generate new information and insights. They use data mining techniques to discover patterns and relationships in data that can be used for business decisions.
• Data modeler: Data modelers design and set up data management systems to support data science and analytics. They create data models that define how data is stored, consumed, integrated, and managed by different data entities and IT systems.
• Data scientist: Data scientists use advanced statistical techniques and machine learning to analyze and interpret complex data. They develop models and algorithms to predict outcomes or uncover insights, aiding in strategic decision-making.
• Data strategist: This role involves developing strategies to use data effectively for business objectives. They assess data needs, define data governance, and guide the implementation of data systems and policies.
• Data visualization developer: They specialize in turning complex data sets into understandable, engaging, and interactive visual formats. Their work helps others comprehend the data and its implications more easily.
• Data warehouse analyst: Responsible for managing the storage and retrieval of large amounts of data in data warehouses. They ensure that data is stored efficiently and is easily accessible for analysis.
• Machine learning engineer: Specialized in designing and building machine learning systems and algorithms. They work with large data sets to develop models that can learn and make predictions or decisions without being explicitly programmed.

The roles outlined above don’t represent all the opportunities open to you after you earn a data science master’s degree. As one Reddit commenter put it: “The problem with the term ‘data scientist’ is that it is very loosely defined and covers many types of jobs. These jobs are not new, they have just been rebranded and cramped into one sexy title.” According to the poster, these jobs entail “two types of tasks: reporting and data visualization, [and] improving processes using math-heavy techniques. We used to call the former ‘business intelligence,’ ‘data analysis,’ ‘analytics’…” The second group “used to be called ‘machine learning specialist,’ ‘statistician,’ ‘actuary,’ and ‘operations researcher.'”

Clearly, getting a master’s degree in data science can lead you down many career pathways. You don’t have to stay in the data science silo to profit from this degree.

Will enrolling in one of the best data science master’s programs boost my salary?

You’ll almost certainly earn more in this field after graduating from a top master’s program in data science… or data analytics, data engineering, applied statistics, or business intelligence. How much more isn’t exactly clear. The average data scientist salary might be about $99,000. But navigate away from PayScale to Glassdoor or Indeed or Salary.com, and you’ll find published averages closer to $113,000, $123,000, and $130,000. On the other hand, some data science jobs pay $70,000 or less.

This discrepancy is easy to explain. Data scientists tend to be well-paid. Still, there are plenty of companies that still don’t see exactly how data science translates into dollars, and so don’t want to pay top dollar for a data scientist. There are still more open data science positions than there are qualified data scientists, but opportunities aren’t evenly distributed. In areas where there’s a glut of data scientists, employers can pay less. On the other hand, data scientists in major metro areas tend to earn more, with the highest salaries concentrated in cities in California, Arizona, Idaho, New York, Mississippi, and Oregon. Experience matters, too; it may be that some salary surveys attracted more experienced respondents than others.

In other words, graduating from a dedicated data science program may not turn you into one of the top earners in this field—but it can’t hurt. The best data science master’s programs offer robust career support and access to large, active alumni networks that can lead to lucrative opportunities. Also, having a famous-name school on your diploma is never a bad thing.

Can I work in data science without earning this degree?

Because there’s a shortage lack of fully qualified data science professionals, plenty of companies are willing to hire data scientists without advanced degrees. That said, if you go into this field, be prepared to compete for jobs against people with master’s degrees and PhDs. According to some sources, 88 percent of data scientists have master’s degrees and almost half have PhDs. That might not matter in areas where data scientists are scarce. In areas where major employers recognize the value of Big Data, however, not having graduated from at least a data science master’s program may be a real handicap. Think carefully before deciding to forgo an advanced degree in favor of a data science boot camp or certificate program.

At the same time, don’t merely assume that graduating from a master’s program—even if it is one of the best data science master’s programs—will set you up for life. Because technology is always evolving, becoming a data scientist involves more than just getting a degree. Data science degree programs evolve much more slowly than artificial intelligence, predictive analytics, programming languages, and data visualization tools do. Even the top data science master’s programs won’t necessarily teach you everything you’ll need to know to become a data scientist. At least some courses in those programs may be out-of-date by the time their students actually have their diplomas in hand. In other words, a degree in data science can launch your career, but that career will involve a lifetime of learning.

(Updated on January 9, 2024)

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