A bachelor’s in data science is a quantitative major built from three parts: a calculus-and-linear-algebra math core, a programming and databases core, and a statistical learning sequence that connects them. Schools package it as a BS in Data Science, a BS in Applied Data Science, a statistics or computer science major with a data science concentration, or occasionally a BA with a lighter math load. The package matters less than the required course list.
This page covers the degree titles you will see, the coursework that actually distinguishes a data science major from an analytics major, how transfer credit behaves in a math-heavy program, and what to verify before you enroll.
Quick answers
Can I get a bachelor’s degree in data science?
Yes. In IPEDS 2024, 208 institutions reported bachelor’s-or-higher completions in data science fields, and 182 of them reported offering distance-education programs (IPEDS 2024). Common titles are BS in Data Science, BS in Applied Data Science, and computer science or statistics majors with a data science concentration.
How is this different from a data analytics degree?
Data analytics centers on SQL, reporting, visualization, and business interpretation, with statistics kept at an applied level. Data science requires calculus, linear algebra, and a probability sequence, and expects you to write the code that builds a model rather than to operate a tool that reports one. The data science vs data analytics comparison walks through the course lists side by side.
How is this different from computer science?
Computer science is about building software and understanding computation: algorithms, systems, languages, and theory. Data science borrows the programming and databases part and replaces the systems depth with statistics and modeling. If you want to build applications rather than analyze data, computer science is the better fit.
How many credits is a bachelor’s degree?
Approximately 120 semester hours, typically four years full-time. The math sequence usually starts in year one, because probability, statistical learning, and machine learning all sit behind it. A late start on calculus is the most common cause of a fifth year in this major.
What programming languages will I learn?
Python and R dominate, usually with SQL as a required course or a substantial part of one. Some programs add a compiled language, Scala or Java for distributed processing, or a cloud platform sequence. Ask whether SQL is taught as a course or assumed as background, because it is the single most common technical screen in hiring.
Is a bachelor’s enough to get hired?
The Bureau of Labor Statistics lists a bachelor’s degree as the typical entry-level education for Data Scientists, and Data Scientists earned a median annual wage of $120,230 (Bureau of Labor Statistics, May 2025 OEWS). In practice many entry-level hires start with an analyst title and move across. A portfolio of finished projects does more to open the first door than the major title does.
At a Glance
- Common degree titles: BS in Data Science, BS in Applied Data Science, or a statistics or computer science major with a data science concentration
- Typical duration: 4 years full-time
- Credits: Approximately 120 semester hours
- Core skills: Probability and statistics, linear algebra, Python or R, SQL, machine learning, data visualization
- Accreditation: ABET accredits a small number of data science programs; institutional accreditation is the check that matters
For a full map of this program area, start here: Data Science Program Guide
Schools to compare
How We Rank Schools
Every school list on this site is ordered by the BOC Score, computed from the most
recent school-level data published by the U.S. Department of Education (College Scorecard and IPEDS).
To qualify, a school must be currently operating and accredited by an agency recognized by the
U.S. Department of Education. Each eligible school is then scored on five measures, percentile-ranked
against schools at the same credential level:
BOCSCORE
- Graduation rate 30%
- Median earnings, 10 years after entry 25%
- Average net price (lower is better) 20%
- Retention rate 15%
- Fully online availability 10%
Schools without enough outcome data appear after ranked schools, without a score.
Advertising never affects these rankings. Read the full methodology.
Graduation rate
93%
Median earnings, 10 yrs after entry
$102,772
Avg net price
$12,116/yr
TuitionContact school for pricing
Key stats
- Retention rate: 98%
- Programs offered: 64
Source:IPEDS · College Scorecard
Graduation rate
97%
Median earnings, 10 yrs after entry
$124,080
Avg net price
$13,807/yr
TuitionContact school for pricing
Key stats
- Retention rate: 98%
- Programs offered: 100
Source:IPEDS · College Scorecard
Acceptance rate
8%
Graduation rate
95%
Median earnings, 10 yrs after entry
$89,718
Avg net price
$13,370/yr
TuitionIn‑state$64,144
Out‑of‑state$64,144
Key stats
- Retention rate: 98%
- Programs offered: 78
Source:Accreditor: Southern Association of Colleges and Schools Commission on Colleges · IPEDS · College Scorecard
TuitionContact school for pricing
Key stats
- Retention rate: 96%
- Programs offered: 30
Source:IPEDS · College Scorecard
Acceptance rate
33%
Graduation rate
84%
Median earnings, 10 yrs after entry
$74,915
Avg net price
$16,109/yr
TuitionIn‑state$16,414
Out‑of‑state$50,614
Key stats
- Retention rate: 92%
- Programs offered: 81
Source:Accreditor: Western Association of Schools and Colleges Senior Colleges and University Commission · IPEDS · College Scorecard
Graduation rate
84%
Median earnings, 10 yrs after entry
$66,240
Avg net price
$20,781/yr
TuitionContact school for pricing
Key stats
- Retention rate: 88%
- Programs offered: 29
Source:IPEDS · College Scorecard
Graduation rate
91%
Median earnings, 10 yrs after entry
$64,027
Avg net price
$27,579/yr
TuitionContact school for pricing
Key stats
- Retention rate: 93%
- Programs offered: 54
Source:IPEDS · College Scorecard
Graduation rate
83%
Median earnings, 10 yrs after entry
$67,258
Avg net price
$28,651/yr
TuitionContact school for pricing
Key stats
- Retention rate: 88%
- Programs offered: 40
Source:IPEDS · College Scorecard
The main undergraduate pathways
Standalone data science major (BS)
The most direct route. A BS in Data Science devotes the major credits to a math sequence, a programming and databases sequence, statistical learning, and a capstone. What varies is where the program was born. A data science major grown out of a statistics department tends to be stronger on inference and weaker on software practice. One grown out of a computer science department tends to be the reverse. Neither is wrong; read the requirements and fill the gap with electives.
Statistics or computer science major with a data science concentration
Common at schools that did not build a separate major. You take the parent department’s core and add a block of data science courses. This is a solid route, and it has a practical advantage: the parent degree is a recognized credential on its own, which helps if you later want statistics graduate work or a software engineering job. The tradeoff is fewer applied modeling credits.
Applied or business-facing data science
Some programs sit inside a business school and trade calculus depth for domain courses in marketing, finance, or operations. These prepare you well for analytics-facing work and less well for modeling roles or graduate study. If the catalog has no linear algebra, treat the degree as an analytics degree with a data science name and plan accordingly.
Typical coursework in a bachelor’s program
| Course Topic |
What You Learn |
| Calculus and linear algebra |
Derivatives, integrals, matrices, and eigenvalues, the machinery under regression and dimensionality reduction |
| Probability and mathematical statistics |
Distributions, estimation, hypothesis testing, confidence intervals, and what a p-value does not mean |
| Programming for data science |
Python or R, control flow, functions, packages, and reproducible notebooks and scripts |
| Data structures and algorithms |
How your code behaves when the dataset stops fitting in memory |
| Databases and SQL |
Relational modeling, joins, aggregation, window functions, and query performance |
| Data wrangling and cleaning |
Missing values, joins across messy keys, schema drift, and why this consumes most of the job |
| Statistical learning and machine learning |
Regression, classification, cross-validation, regularization, trees, ensembles, clustering |
| Data visualization and communication |
Chart choice, uncertainty, and presenting a result to people who will not read the code |
| Ethics, privacy, and data governance |
Consent, de-identification, model bias, and what regulated data restricts |
| Capstone project |
An end-to-end analysis or model on real data, documented and defended |
Skills you may build
- Turning a vague business or research question into a specification you can actually test
- Writing SQL that returns the right rows, not merely rows
- Cleaning data without quietly deleting the signal
- Choosing a model for the question rather than for the syllabus, and validating it honestly
- Explaining uncertainty to a decision maker who wants one number
- Building a project you can put in front of an interviewer and defend line by line
Prerequisites and course sequencing
The math sequence gates almost everything. Machine learning sits behind probability, probability sits behind calculus, and applied courses sit behind programming. Before you enroll, confirm:
- Where math placement puts you, and whether precalculus is a prerequisite you must add
- How often linear algebra and probability are offered online, and in which terms
- Whether the machine learning course requires data structures or only introductory programming
- Whether SQL is a required course or an assumed skill
- Whether the capstone requires a prerequisite sequence you can finish on your timeline
If you plan to move faster than four years, compare accelerated data science programs. Compression is harder on the math sequence than on the applied courses, because proofs and problem sets do not shrink with the calendar.
Transfer credits and degree planning
Transfer credit can shorten this degree substantially, especially at the front end. General education, composition, calculus, and introductory programming transfer readily from community colleges. Upper-division statistical learning and capstone courses usually do not. Confirm with each school:
- The maximum number of transfer credits accepted
- The minimum grade required for a course to transfer
- Whether transferred calculus and statistics satisfy major prerequisites or only elective hours
- Whether a coding boot camp certificate carries any credit, which it usually does not
- Residency requirements, meaning a minimum number of credits completed at the institution
See Affordable Online Data Science Degrees for the cost drivers behind those decisions.
Accreditation and program quality checks
ABET’s Computing Accreditation Commission accredits a small number of data science bachelor’s programs. It is a genuine credential, but it is uncommon in this field and employers do not screen on it, so its absence tells you little. Verify that the institution holds accreditation from a recognized institutional accreditor through the U.S. Department of Education database.
Vendor certificates from cloud and analytics platforms are useful signals for specific tools, but they are not accreditation and they do not substitute for the math sequence. Institutional accreditation is the check that matters.
Building the portfolio while you study
Hiring in this field runs on demonstrated work. A transcript says you passed machine learning; a repository says you can build something that runs. Habits that pay off:
- Keep every substantial course project in version control, with a readme that states the question, the data, and the limitations
- Finish two or three projects well rather than starting eight, and include one that uses genuinely messy data
- Write at least one project end to end: acquisition, cleaning, modeling, evaluation, and a short written result
- Practice explaining a model to someone non-technical, because that conversation is a common interview stage
- Keep a SQL practice habit through the whole degree, because it is the most common technical screen
Bachelor’s vs a master’s in data science
A bachelor’s is the full undergraduate credential plus the quantitative core, and it is the typical entry-level education BLS lists for the occupation. An online master’s in data science is most valuable to career changers who already hold a degree in another field and need the statistics, the programming, and a portfolio in one structured package. It is also common for people moving from an analyst role toward modeling work.
Compare degree options:
Data verified: September 5, 2026.
Salary, employment, and tuition figures on this page are sourced from the U.S. Bureau of Labor Statistics (OEWS May 2025; Employment Projections 2024–2034) and the U.S. Department of Education College Scorecard (2023 cohort). The source agency and data year are cited inline with every statistic.
← Back to Best Online Data Science Degrees (2026)
