2027 Data Science Master's Admissions for Applicants with Multiple Bachelor's Degrees
Co-Founder and Chief Data Scientist
Having multiple bachelor's degrees can strengthen a data science master's application, but the number of degrees is less important than the preparation they demonstrate. If your studies span different fields, you need to show how they add up to readiness for graduate-level mathematics, programming, and analysis.
This guide will help you assess that readiness, identify gaps, and decide which programs are worth applying to.
Key Things You Should Know
- Multiple bachelor's degrees generally do not prevent you from applying; disclose every institution attended and check each program's transcript policy.
- Assess your coursework across both degrees: a second degree does not automatically satisfy requirements in calculus, linear algebra, statistics, or programming.
- In May 2025, the U.S. median annual wage for data scientists was $120,230, according to the Bureau of Labor Statistics (BLS). That is a labor-market benchmark, not a predicted salary for master's graduates.
What is a data science master's degree, and who should pursue one?
A data science master's typically combines statistical reasoning, computing, and the use of data to answer practical questions. Programs differ: some emphasize mathematical modeling, while others focus more on applied analytics, software, or a particular industry.
For someone with two bachelor's degrees, the best reason to pursue a master's is a specific remaining gap that graduate study can address. A statistics graduate with substantial programming experience may need a different curriculum from a humanities graduate whose second degree introduced computing but little advanced mathematics. If your existing coursework and work already support your target role, compare the degree's added value with less extensive study before enrolling.
Can applicants with multiple bachelor's degrees apply to data science master's programs?
Yes. Holding more than one bachelor's degree is not, by itself, a general barrier to applying. Eligibility and evaluation rules belong to the individual graduate program. Admissions staff may look across both degrees for relevant coursework, but you should not assume they will combine grades, waive prerequisites, or favor the newer degree.
Present the degrees as a coherent academic history rather than asking reviewers to infer the connection. Explain what each contributed - for example, one may provide subject-matter knowledge and the other quantitative training - and point to the courses or projects that establish current readiness. If the degrees overlap, explain why the additional study was useful without portraying the second credential alone as proof of technical proficiency.
Avoid a common application mistake: submit transcripts from every institution the application requires, even if a course did not count toward either degree. Before paying an application fee, ask whether the program will consider prerequisite coursework completed under either bachelor's degree and whether any courses have age or minimum-grade rules.
What are the admission requirements for a data science master's program?
Requirements vary by school and by admissions cycle. A program may request transcripts, recommendations, a statement of purpose, a résumé, and evidence of quantitative or programming preparation. Some specify prerequisite courses or minimum grades; others evaluate preparation more holistically. Test policies also vary, so check the current application instructions rather than relying on an older summary.
For applicants with multiple degrees, the critical distinction is between evidence of readiness and formal eligibility. A work project may demonstrate Python ability, for example, without replacing a required, credit-bearing programming course. Likewise, strong grades in a second degree may help explain academic progress without changing how a school calculates an application GPA.
Read each program's current requirements and ask admissions which transcripts and grades its reviewers use, whether courses from both degrees can meet prerequisites, and whether unfinished prerequisites may be completed before enrollment. The answers will tell you whether to apply now, complete coursework first, or choose a program with a different entry pathway.
Which prerequisite courses prepare applicants for graduate data science study?
Start with the coursework required by the programs on your shortlist. Calculus, linear algebra, probability, statistics, and programming are common areas to check, but their required level and acceptable substitutes differ. A course title alone may not settle the question: an introductory statistics course and a mathematical probability course develop different kinds of preparation.
Map courses from both bachelor's degrees against each program's stated requirements. Keep the course descriptions and, where useful, syllabi for classes with unfamiliar titles. Then address the most consequential gap: for example, a strong subject-matter background will not remove the need to learn programming if a program expects students to code from the outset.
Self-study, projects, and short courses can make you better prepared and give you examples to discuss in an application. They fulfill a formal prerequisite only if the program accepts them. If the gap is substantial, compare programs offering foundation coursework rather than assuming you must earn another bachelor's degree. Applicants considering an AI-focused alternative can also compare AI master's bridge options, while confirming that those pathways cover the statistics and data work their goals require.
How do online and campus-based data science master's programs compare?
Delivery format does not determine whether your two degrees qualify you for admission. It does affect how you will close skill gaps, get help with difficult material, and complete collaborative work. Compare the actual schedules and support offered by programs, not just their online or campus labels.
The table highlights format differences that matter most when you are entering with coursework from more than one discipline. Individual programs may combine features of both formats.
| Consideration | Online program | Campus-based program |
| Schedule | May offer more location flexibility, but classes can still have live sessions and deadlines. | Typically requires attendance at set times and travel to campus. |
| Academic support | May provide virtual office hours and tutoring; access and timing vary. | May make in-person help easier to use; availability still varies. |
| Projects and networking | Remote collaboration can broaden access but may require careful scheduling. | In-person interaction may be valuable, particularly for group work and local connections. |
If you are working while strengthening prerequisites, ask about weekly course load, live attendance, and the maximum time allowed to finish. Online data science master's duration comparisons can help you frame the pacing question, but confirm the current schedule directly with each program.
What courses and technical skills are taught in a data science master's program?
Look for a sequence that builds from your existing skills into the work you want to do. Relevant subjects may include statistical inference, databases, data engineering, machine learning, experimental design, visualization, and communicating results. The mix and depth vary considerably; the presence of "machine learning" in a course list does not show how much programming or mathematical reasoning students actually perform.
Review course descriptions and sample projects for evidence that students clean real data, select and evaluate methods, explain uncertainty, and address privacy or bias where relevant. Applicants with two degrees can use a project or capstone to bring their backgrounds together - for example, applying sound statistical methods to a field they already know - provided the program permits that topic and offers suitable guidance.
If responsible AI work is central to your goal, inspect required courses rather than relying on an ethics-related elective title. You can review program comparisons for AI ethics master's tracks as a separate route, then compare their technical and governance depth with data science programs.
How long does a data science master's take, and how much does it cost?
Time to completion depends on required credits, course sequencing, enrollment pace, and whether you must take preparatory classes before or during the program. A previous bachelor's degree does not automatically shorten a master's: graduate programs set their own policies for transfer credit and waivers.
Calculate cost from the current tuition schedule and the number of credits you expect to take. Add mandatory fees, materials or software, travel if required, and the cost of any prerequisite courses. If you will reduce work hours, consider that lost income separately. Do not treat an advertised per-credit price as the total cost.
Ask each school for a current estimate based on your enrollment plan, including whether prerequisite courses receive financial aid or count toward the degree. If cost is driving you toward an AI rather than data science credential, online AI degrees with lower tuition offer another comparison point; evaluate their full costs and curriculum, not tuition alone.
How can applicants evaluate accredited and reputable data science programs?
Verify the institution's accreditation through the accreditor or an official U.S. Department of Education resource, and check whether the school is authorized to enroll students where you live if you plan to study online. Institutional accreditation is an important baseline; it does not establish that a particular data science curriculum fits your preparation or career goal.
For applicants with multiple degrees, reputation is less useful than evidence about fit. Examine required courses, faculty expertise, opportunities for substantial projects, academic support, and the program's written prerequisite and transfer-credit policies. If a school advertises career outcomes, look for the graduating cohort, response rate, and definition of employment behind the claim. Treat an undisclosed outcome as unknown, not as evidence of success or failure.
What careers and salaries can graduates expect with a data science master's?
Potential paths include data scientist and other analytics or machine-learning roles, depending on your technical skills and prior experience. Your first bachelor's degree may remain useful as domain knowledge: it can help you define meaningful problems, interpret results, or work with specialists in that field. Employers will still assess your ability to do the technical work.
The BLS reports a $120,230 median annual wage for U.S. data scientists in May 2025 and projects 35% employment growth from 2025 to 2035. These are occupation-wide figures, not starting salaries or placement rates for master's graduates. They support exploring the field, but they cannot establish what an additional degree would cause you to earn.
Compare likely roles with your current pay, experience, and location before estimating a return on the degree. Pay also varies by employer and field; data science degree earnings by sector can inform that comparison without substituting for evidence about a specific program's graduates.
What certifications and professional standards apply to data science careers?
There is no single professional license generally required to work as a data scientist in the United States. Employers may value certifications in particular cloud platforms, analytics tools, or technologies, but those credentials are not universal substitutes for graduate-level preparation or demonstrable project work.
Choose an additional certification only when it matches tools used in your target role. More broadly, look for training in reproducible analysis, responsible handling of data, clear documentation, and honest communication of model limitations. If your earlier degree leads you toward a regulated field, check the rules governing that field separately; a data science master's does not itself confer another profession's license.
Other Things You Should Know About Data Science
Not necessarily. A double major can result in one degree with two majors, while two separately awarded credentials are two degrees. Use the credential names shown on your official transcript and application.
Do not assume so. Programs may review all transcripts even when they calculate a particular GPA from selected coursework. Ask how the school handles multiple undergraduate records.
Choose people who can give specific evidence of your readiness for the program. A professor from either degree or a relevant work supervisor may be appropriate if the program accepts that type of reference.
Usually, first identify the exact missing prerequisites and ask whether targeted courses would satisfy them. A further degree is a much larger commitment and should serve a goal beyond adding another credential to your application.
References
- Evaluation of Foreign Degrees - United States Department of State https://www.state.gov/global-community-liaison-office/family-member-employment/family-member-employment-in-the-d-c-area/evaluation-of-foreign-degrees
- Computer Science Majors | nyu.edu https://cs.nyu.edu/home/undergrad/major_programs.html
- Frequently Asked Questions - Undergraduate Admissions https://admissions.ncsu.edu/connect/frequently-asked-questions/
- M.S. admissions | Data Science https://cse.umn.edu/datascience/graduate-admission-requirements
- Second Degree | Stony Brook University https://www.stonybrook.edu/undergraduate-admissions/transfer/second-degree.html
- Basic Admission Requirements for Postbaccalaureate Applicants | Undergraduate Admissions | Oregon State University https://admissions.oregonstate.edu/basic-admission-requirements-postbaccalaureate-applicants
- Criteria for Accrediting Computing Programs, 2025 - 2026 | ABET https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-computing-programs-2025-2026/
