Data Science vs. Data Analytics: How the Degrees Differ

"Data science" and "data analytics" are often used as if they meant the same thing, and the two fields do overlap. As degree programs, though, they are classified separately, and the difference usually shows up in how much statistics, machine learning and programming a curriculum requires. This guide explains how the two degree types differ, why this directory lists data science degrees and not analytics degrees, and how to read a program title before you apply.

Two separate program families

The U.S. Department of Education's Classification of Instructional Programs (CIP), maintained by the National Center for Education Statistics, gives each field its own code. Data Science (30.70) and Data Analytics (30.71) are separate series.

• Data Science, General (30.7001) is defined as a program that "focuses on the analysis of large scale data sources" from the perspectives of applied statistics, computer science, data storage, data modeling and mathematics. Listed instruction includes computer algorithms, programming, data management, data mining, mathematical modeling and statistics.
• Data Analytics, General (30.7101) is defined as a program that "prepares individuals to apply data science to generate insights from data and identify and predict trends." Its listed instruction includes databases, programming, inference, machine learning, optimization and visual analytics.

The wording is a useful starting point: data science is framed around the methods themselves, analytics around applying them to answer questions. The lists overlap, which is why course requirements tell you more than the field name.

Where the curricula tend to diverge

Because both labels cover a wide range, the practical question is how deep a given program goes in each area. When you compare programs, check these points in the official course list:

• Statistics. Does the program require probability and statistical inference courses with prerequisites, or a single applied statistics course?
• Machine learning. Is machine learning a required core course, with modeling, validation and testing, or an elective?
• Programming. Are you expected to write code in Python or R throughout, or mainly to use reporting and dashboard tools?
• Mathematics. Are calculus and linear algebra prerequisites or required courses?
• Reporting and business intelligence. How much of the core is visualization, dashboards and communicating results to decision makers? This is valuable work, but a core built mostly around it is closer to analytics.

NYU's Data Science BA is an example of the methods-heavy end. Its bulletin says the major "provides rigorous training in statistical modeling, machine learning, and data driven reasoning, grounded in computer science and mathematics."

Many programs combine both emphases. Kennesaw State's Master of Science in Data Science and Analytics is a 36-semester-hour program that says it prepares students "to be practicing data scientists and analytics professionals," and lists statistical and mathematical foundations and programming in R, Python and SAS among its highlights.

Why analytics degrees are not listed here

This directory covers data science degrees. A program is included when data science is the degree itself, when data science is named alongside another field in a single degree title, or when a related degree has an officially named data science concentration. Standalone degrees titled Data Analytics, Analytics or Business Analytics, without data science in the official title or a named data science option, are excluded. Our methodology page explains the rules in full.

The reason is consistency, not quality. Analytics degrees can be rigorous, and some cover machine learning in depth. But once a directory starts deciding which analytics programs are "data science enough," the listing stops being comparable. Using the official title and the catalog keeps the rule checkable.

Degrees whose title names both fields are included as joint degrees. In our directory (October 2026), 217 programs are listed: 144 dedicated data science degrees, 50 joint degrees and 23 concentrations within related degrees. The MS in Data Science and Analytics at Kennesaw State is one example of a joint title.

How to read a program title

The program name on a marketing page and the degree printed on your diploma are not always the same. Check the university catalog or bulletin for the awarded degree.

A few things to watch for:

• "Data science" in a course name is not a data science degree. A business or analytics degree may include a course called "Introduction to Data Science."
• Concentrations award the parent degree. A Data Science concentration in a computer science master's usually leads to a computer science diploma. See Data Science vs. Computer Science.
• Business analytics is its own comparison. If you are weighing a business school program, see Data Science vs. Business Analytics.

Careers and the BLS data

The Bureau of Labor Statistics publishes an occupation profile for data scientists. It says data scientists "use analytical tools and techniques to extract meaningful insights from data." Typical duties include collecting and analyzing data, creating, validating and testing algorithms and models, and presenting findings with data visualization.

On education, BLS says data scientists typically need at least a bachelor's degree in mathematics, statistics, computer science or a related field, and that some employers require or prefer a master's or doctoral degree. BLS reports a median annual wage for data scientists of $120,230 (May 2025), and projects employment to grow 35 percent from 2025 to 2035. These are national occupation figures. They are not outcomes for any program, and individual results vary.

The same BLS profile lists mathematics, statistics, computer science, business and engineering as common fields of degree, and says college coursework in computer science matters in addition to math and statistics. In other words, BLS describes the occupation by skills and fields of study, not by one degree title, so compare required courses as well as the degree name.

Choosing between the two

A data science degree is likely the better fit if you want to build and evaluate models, write code throughout your coursework, and keep doctoral study open. An analytics-centered program may suit you if you mainly want to answer business questions with existing tools and data. If you are unsure, joint degrees and the course checklist above are the most reliable guide.

To compare programs, browse master's programs and use the compare tool to view programs side by side.