undergradly.
Major · Multi/Interdisciplinary Studies

Too new for five-year earnings data, with an unusually clear career pipeline

Data Science, General is new enough as a CIP-coded undergraduate major that five-year earnings data is not yet reportable — the data packet shows both overall and degree-level earnings as null. About 2,068 degrees are awarded each year across 205 colleges, and the career list is unusually strong: eight destinations, all at $78K+ with five bachelor's-entry roles clustered between $112K and $171K. Data scientists at $112,590 with 33.5% projected growth and software developers at $133,080 with 15.8% growth anchor the pipeline. The major's newness is its defining characteristic — enrollment and graduate numbers are growing rapidly, and the long-run earnings profile is still taking shape.

Schools offering
205
Annual completions
2,068
Typical degree level
Associate's + Bachelor's
Median earnings (5yr)
—

About this major

Data Science, General is an interdisciplinary major explicitly built around the analysis of large-scale data sources. The CIP definition names applied statistics, computer science, data storage, data representation, data modeling, mathematics, and statistics as the foundations, with instruction in computer algorithms, programming, data management, data mining, information policy, information retrieval, mathematical modeling, quantitative analysis, trend spotting, and visual analytics. The program sits in the Multi/Interdisciplinary Studies concentration because it intentionally spans traditional departmental boundaries — most Data Science programs draw coursework from computer science, statistics, and sometimes business or engineering departments, with a required capstone applied project.

The major is new by CIP standards. About 2,068 degrees are awarded each year across 205 colleges, and the program is growing rapidly. The University of Wisconsin-Madison leads at 246 annual completions, followed by New York University (159), Virginia Tech (158), Pennsylvania State University (122), and the University of Michigan-Ann Arbor (103). The University of Maryland Global Campus (81) and Arizona State University Digital Immersion (57) represent substantial online-first programs, and Worcester Polytechnic Institute (44) brings the engineering-school perspective. The related-majors data confirms adjacency to Computer Science (50,272 annual completions — roughly 25 times larger), Information Technology (20,296), Mathematics, General (18,758), Cybersecurity and Information Assurance (17,632), Econometrics and Quantitative Economics (15,101), Information Science/Studies (10,334), and Computer Engineering, General (9,312). Many of those feeder majors currently dominate the labor-market pipeline that Data Science programs explicitly target.

The earnings picture is unusual because of the data-vintage issue: five-year earnings are reportable as null for both associate's and bachelor's completers. The CIP code is new enough that few graduates have been in the labor market for the full five-year window this site tracks. The career list compensates by showing eight destinations, all at $78K or above and five bachelor's-entry at $100K or more. Data scientists (SOC 15-2051) at $112,590 median with 33.5% projected growth anchor the list — among the fastest-growing roles in the entire economy, and one of the few where bachelor's is listed as typical entry education. Software developers at $133,080 with 15.8% growth, database architects at $135,980 with 8.7% growth, and the mid-career management roles (computer and information systems managers at $171,200 and natural sciences managers at $161,180) complete the bachelor's-accessible picture. Master's-entry roles include computer and information research scientists at $140,910 with 19.7% growth and statisticians at $103,300 with 8.5% growth. Adjacent-program earnings proxies (Computer Science and Statistics) report five-year bachelor's medians at or above $95K, and the skill floor for the named career destinations (Python, R, SQL, machine learning, SQL) is identical regardless of the specific CIP code on the transcript.

Section 3 · Careers

Where this major leads

Occupations most often associated with this major, from the federal BLS+O*NET crosswalk. Job-growth projections and median wages are national.

Occupation Median wage Job growth Typical education
Software developers $133k +15.8% Bachelor's degree
Computer and information systems managers $171k +15.2% Bachelor's degree
Data scientists $113k +33.5% Bachelor's degree
Postsecondary teachers, all other $78k +1.8% Doctoral or professional degree
Natural sciences managers $161k +3.7% Bachelor's degree
Database architects $136k +8.7% Bachelor's degree
Computer and information research scientists $141k +19.7% Master's degree
Statisticians $103k +8.5% Master's degree
Section 4 · Earnings

Earnings at a glance

Median graduate earnings from the federal College Scorecard, 5 and 10 years out.

Earnings data is not yet available at this major's level of granularity. See the concentration hub for family-level figures.

Who this major is for

Students interested in applied quantitative work on real datasets, who want interdisciplinary breadth across statistics, computer science, and applied analytics rather than deep specialization in any single one, and who will consistently invest time in programming practice, portfolio projects, and internships are the strongest fit. Targeting the bachelor's-entry data scientist role ($112,590, 33.5% growth) gives the curriculum an obvious anchor, and selecting programs with required machine-learning, SQL, and applied-capstone coursework meaningfully shapes employability. Students who thrive on ambiguity — messy real datasets, open-ended business questions, results that need to be communicated to non-technical stakeholders — often fit better here than in more theoretically pure CS or Statistics programs.

Reconsider this major if the intended career is in deep software engineering, computer systems research, or algorithmic theory, because Computer Science provides stronger foundations in those areas and the 50,272-completions-per-year scale means the employer network is much broader. If the primary interest is statistical inference and experimental design — biostatistics, clinical trials, government statistical work — Statistics, General (3,379 completions) provides more theoretical depth. If the target is specifically the business-analytics rather than technical-data-science side, Business Analytics (1,032 completions, in Business) is the business-school version with stronger enterprise-system exposure. Finally, scrutinize the specific program carefully. Data Science programs vary enormously in their balance of computer science, statistics, and applied projects, and the $100K+ career destinations in the list require genuine programming fluency (Python, R, SQL minimum) plus machine-learning exposure plus a portfolio of real-data projects — skills that some programs build deeply into the required curriculum and others treat as optional electives. The major label is less informative about career readiness than the actual coursework, which should be evaluated course by course before enrollment.

Section 6 · Where to study

Top colleges for Data Science, General

Ranked by annual completions at the associate's or bachelor's level — a proxy for program scale.

College Location Assoc. Bach. Total
University of Wisconsin-Madison Madison, WI 0 246 246
New York University New York, NY 0 159 159
Virginia Polytechnic Institute and State University Blacksburg, VA 0 158 158
Pennsylvania State University-Main Campus University Park, PA 0 122 122
University of Michigan-Ann Arbor Ann Arbor, MI 0 103 103
University of Maryland Global Campus Adelphi, MD 0 81 81
Arizona State University Digital Immersion Scottsdale, AZ 0 57 57
Arizona State University Campus Immersion Tempe, AZ 0 47 47
University of North Carolina at Charlotte Charlotte, NC 0 44 44
Worcester Polytechnic Institute Worcester, MA 0 44 44
Section 7 · Explore sideways

Related majors to explore

Computer Science

50,272 completions/yr

Biological and Physical Sciences

25,374 completions/yr

Information Technology

20,296 completions/yr

Mathematics, General

18,758 completions/yr

Computer and Information Systems Security/Auditing/Information Assurance

17,632 completions/yr

Econometrics and Quantitative Economics

15,101 completions/yr

Information Science/Studies

10,334 completions/yr

Computer Engineering, General

9,312 completions/yr
Section 9 · Frequently asked

Common questions

Why does the data packet show null earnings?
Because the CIP code (30.7001) was added recently enough that few bachelor's completers have been in the labor market for the full five-year earnings window that this site reports. Earnings data is based on what graduates of this specific major actually earn five years out, and a major that began producing meaningful graduate cohorts only in the past few years will not yet have the denominator for a reportable median. This is a data-vintage issue rather than a signal about outcomes. Adjacent proxies are useful: Computer Science (50,272 completions, five-year median above $100K at many programs) and Statistics, General ($95,197 bachelor's median) both feed the same career SOC codes that dominate this data packet's career list. The most directly comparable earnings signal is the career-list wages themselves — five of the eight careers are bachelor's-entry and pay above $100K, and the data scientist role specifically lists bachelor's as typical education.
How is Data Science different from Computer Science or Statistics?
The three majors overlap substantially but have different centers of gravity. Computer Science (50,272 annual completions — nearly 25x larger than this major) is the broad foundational computing major: algorithms, programming, systems, software engineering, plus electives in specializations like AI, data systems, or theory. Statistics, General (3,379 completions) is the quantitative inference major: probability, experimental design, regression, applied statistical methods. Data Science, General explicitly bridges the two — the CIP definition names applied statistics, computer science, data storage, data modeling, data mining, and visual analytics as the interdisciplinary foundations. In practice, Data Science programs typically require less pure computer-science theory than CS majors and less theoretical statistics than Statistics majors, replacing those with machine learning, data engineering, and applied projects. Employers increasingly recognize all three paths for data scientist, ML engineer, and analyst roles, but the specific program curricula vary enormously — some Data Science degrees lean computer-science-heavy, others lean statistics-heavy, and prospective students should evaluate coursework carefully.
What do the eight careers on the list actually look like in practice?
The career list is unusually strong. Bachelor's-entry roles: data scientists at $112,590 with 33.5% projected growth (among the fastest-growing in the economy), software developers at $133,080 with 15.8% growth, computer and information systems managers at $171,200 with 15.2% growth (typically reached mid-career after 5–10 years), database architects at $135,980 with 8.7% growth, and natural sciences managers at $161,180 with 3.7% growth (mid-career). Master's-entry roles: computer and information research scientists at $140,910 with 19.7% growth, and statisticians at $103,300 with 8.5% growth. The doctoral-entry role: postsecondary teachers, all other at $78,490. The aggregate picture is stronger than for almost any other major on this site — five bachelor's-accessible careers paying above $100K with multiple at double-digit growth rates. The real question for students is which of those roles the specific program prepares them for; data-scientist and ML-engineer roles expect demonstrated Python/R/SQL fluency and portfolio projects that curricula alone do not always provide.
Which schools lead the program and what does the mix signal?
University of Wisconsin-Madison leads at 246 completions, followed by NYU (159), Virginia Tech (158), Penn State (122), and the University of Michigan-Ann Arbor (103). University of Maryland Global Campus (81) and Arizona State University Digital Immersion (57) are notable online programs, and Worcester Polytechnic Institute (44) brings the engineering-school perspective. The mix signals two things. First, leading research universities are building Data Science as a flagship interdisciplinary undergraduate major, often housed in a combined school of data science or spanning computer science, statistics, and business departments. Second, online-first institutions (UMGC, ASU Digital Immersion) are leaning heavily into the major as a working-adult career-change credential. Program quality varies dramatically. Prospective students should evaluate whether the curriculum requires substantial programming (Python, R, SQL minimum), whether machine learning and experimental design are required courses rather than electives, and whether capstone projects involve real datasets and industry partners.
Is this major a good choice for students uncertain about computer science specifically?
It can be, but with caveats. Data Science programs often present themselves as a more approachable route into the tech labor market than Computer Science, and the CIP definition's emphasis on interdisciplinary breadth supports that positioning. The caveat is that the highest-paying career destinations — data scientist, software developer, database architect — require genuine programming fluency that cannot be avoided regardless of the major's label. A Data Science bachelor's that covers Python and R at an intermediate level, plus SQL, plus at least one substantial applied-ML project, positions graduates for bachelor's-entry data-science roles. A Data Science bachelor's heavy on statistics and visualization but light on programming may leave graduates competing with Statistics and Business Analytics majors for a narrower set of analyst roles at $60K–$85K rather than the $100K+ roles in the career list. The underlying skill requirements do not vary by major name; what varies is how much of the skill-building happens inside the curriculum versus outside it.