2027 Easiest Online Data Science Doctorate Programs to Get Into: Admission Requirements, GPA, and Workarounds
Co-Founder and Chief Data Scientist
Online data science doctorates are becoming more accessible as universities build flexible doctoral tracks for working analysts, engineers, managers, and career changers. The timing matters: the U.S. Bureau of Labor Statistics' 2024 Occupational Outlook Handbook projects data scientist employment to grow 36% from 2023 to 2033, far faster than average.
This guide is for applicants worried about strict admissions, low GPAs, missing prerequisites, or GRE requirements. You will learn how to identify lower-barrier programs, compare trade-offs, and use legitimate admission workarounds without choosing a weak or unaccredited degree.
Key Things About the Easiest Online Data Science Doctorate Programs
- The easiest online data science doctorate programs are usually applied doctorates or professional doctorates with holistic admissions, rolling or cohort-based review, no GRE/GMAT requirement, and a typical minimum GPA around 3.0, though some schools review applicants with lower GPAs conditionally.
- Low-GPA applicants can improve admission odds by submitting a GPA addendum, emphasizing the last 60 credits or graduate GPA, completing 6 to 12 credits of recent quantitative coursework, or applying to programs that allow conditional admission after earning strong grades in initial doctoral courses.
- Applicants who want a less traditional completion path should look for accredited programs with applied dissertations, doctoral capstones, consulting projects, or practice-based research; these are not "easy," but they may be more manageable than a theory-heavy PhD dissertation for working professionals.
Are online Data Science doctorate programs competitive?
Online data science doctorate programs can be competitive, but they are often competitive in a different way than full-time, campus-based PhD programs. A research PhD may admit a small number of students based on faculty funding, research fit, and publication potential.
Many online professional doctorates, by contrast, evaluate whether applicants can succeed in advanced coursework, apply data science in practice, and complete a substantial project while working.
The biggest challenge is that most universities do not publish program-specific acceptance rates for online data science doctorates. That means applicants should not rely on a single "acceptance rate" number.
Instead, they should look at the admission model: rolling admissions, multiple start dates, no required faculty match, cohort size, conditional admission, and whether the degree is designed for practitioners rather than funded research apprenticeships.
The labor-market pressure behind these programs is real. The BLS projects 36% growth for data scientist jobs from 2023 to 2033, which helps explain why universities are expanding flexible doctoral and advanced analytics pathways. For students still comparing master's-level and doctoral-level ROI, reviewing affordable data scientist degree options can help clarify whether a doctorate is necessary for the roles they want.
This table summarizes how different admissions models affect competitiveness. Use it to understand why "easiest" usually means "more flexible and more practice-oriented," not academically effortless:
| Program type | Typical competitiveness | Why it may be easier or harder to enter | Best fit |
| Online applied doctorate in data science or analytics | Moderate | Often uses holistic review, professional experience, and applied goals instead of a strict research match | Working professionals seeking leadership, consulting, or applied analytics roles |
| Online PhD with data science specialization | Moderate to high | May still require research alignment, stronger academic writing, and a dissertation proposal | Applicants interested in research, teaching, or advanced methodology |
| Campus-based funded PhD | High | Limited funded seats, faculty supervision capacity, and research-lab fit often drive selectivity | Full-time students targeting academic or research-scientist pathways |
| DBA, DIT, or EdD with analytics concentration | Low to moderate | May accept applicants from management, IT, business analytics, or education data backgrounds | Professionals applying data science to business, technology, healthcare, or education systems |
The trade-off is important: a lower-barrier program may offer more flexibility, but it may not carry the same research prestige as a highly selective PhD. Applicants who want tenure-track academic roles, funded assistantships, or deep theoretical research should consider more competitive PhD options. Applicants who want promotion, executive analytics leadership, or applied research credibility may find an online professional doctorate more aligned with their goals.
What are the easiest online Data Science doctorate programs to get into?
The easiest online data science doctorate programs to get into are typically accredited, practitioner-focused programs that do not require a perfect academic record, a GRE score, or a narrowly matched undergraduate major. They may not always carry the exact title "Doctorate in Data Science."
Many accessible options appear as applied doctorates in analytics, information technology, business administration, computer science, or technology leadership with data science-heavy coursework.
Before applying, decide whether you truly need a doctorate. If your goal is to become a data scientist, analytics manager, or machine learning practitioner, an online masters in data science may be faster, cheaper, and sufficient. A doctorate makes more sense when you need advanced research credibility, senior leadership differentiation, university teaching eligibility, or the ability to lead complex analytics strategy.
The table below compares lower-barrier doctoral options by admission flexibility and career fit. It does not rank individual schools because requirements change frequently and acceptance rates are often unpublished:
| Lower-barrier option | Common degree titles | Admissions profile | Best reason to choose it | Key caution |
| Applied data science doctorate | Doctor of Data Science, DPS in Data Science, PhD in Data Science with applied track | Master's preferred or required, 3.0 GPA often expected, GRE commonly not required or waivable | You want the most direct doctoral credential in the field | Program availability is limited compared with master's programs |
| Information technology doctorate with analytics concentration | DIT, PhD in IT, Doctor of Information Technology | Often accepts IT, computer science, analytics, engineering, or systems professionals | You want to lead data platforms, AI governance, cybersecurity analytics, or enterprise systems | May be less statistics-intensive than a pure data science doctorate |
| Business doctorate with analytics concentration | DBA in Business Analytics, DBA in Data Analytics | Often values management experience, applied business problems, and quantitative readiness | You want executive, consulting, product, or strategy roles | May not be ideal for research-heavy machine learning careers |
| Education or healthcare analytics doctorate | EdD, DHA, or health informatics doctorate with analytics focus | Often open to professionals with domain expertise and evidence of quantitative preparation | You want to apply data science in education, healthcare, policy, or organizational improvement | Career transferability depends on curriculum depth and employer expectations |
Applicants looking for easier entry should prioritize programs with several flexibility signals. These are especially useful for students with a low GPA, degree mismatch, or long gap since college.
- Rolling admissions or multiple annual start dates instead of one annual PhD deadline.
- No required GRE or GMAT, or a clear test-waiver pathway.
- Conditional admission for applicants who can prove readiness in initial doctoral coursework.
- Applied dissertation, capstone, or doctoral project options instead of only a traditional dissertation.
- Bridge or leveling courses in statistics, programming, databases, or research methods.
- Acceptance of professional experience, certifications, portfolios, or prior graduate coursework as evidence of readiness.
An "easy to get into" doctorate is worth considering if it is regionally accredited, aligned with your career outcome, transparent about cost, and rigorous enough to be respected by employers. It is not worth choosing if it hides graduation requirements, has weak faculty credentials, lacks institutional accreditation, or pushes enrollment before reviewing your academic fit.
What is the minimum GPA requirement for online Data Science doctorate programs?
Most online data science doctorate programs expect a minimum GPA near 3.0, usually from the applicant's most recent degree or graduate coursework.
However, GPA policies vary. Some schools use a firm cutoff, while others review the last 60 credits, master's GPA, quantitative-course grades, or evidence that the applicant has improved academically since an earlier weak semester.
The table below shows common GPA rules and how they affect applicants. Use it to identify whether a program is truly accessible for your academic record before spending time and money on an application:
| GPA policy | What it usually means | Applicant impact | Accessibility level |
| Minimum 3.0 cumulative GPA | The school expects a B average across the relevant prior degree | Applicants below 3.0 may need a waiver, addendum, or conditional route | Moderate |
| Minimum 3.0 graduate GPA | The school focuses on master's-level performance | Helpful for applicants with a weak bachelor's GPA but strong master's record | Moderate to accessible |
| Last 60 credits reviewed | Recent upper-division or graduate performance receives more weight | Useful for applicants who improved late in college | Accessible |
| Holistic review below 3.0 | The school considers work experience, recent coursework, writing ability, and recommendations | Best fit for applicants with a clear explanation and strong professional evidence | Most accessible |
| Conditional admission | The applicant must earn strong grades in first doctoral courses to continue | Provides a probationary pathway but may carry financial and academic risk | Accessible with caution |
A low minimum GPA does not automatically mean a program is low quality. The more important question is whether the school has clear academic safeguards, such as doctoral writing support, quantitative leveling courses, transparent dismissal policies, and faculty review.
A program that admits broadly but offers little support can be harder to complete than a more selective program with stronger mentoring.
If your GPA is below the stated requirement, do not assume you are disqualified. Ask whether the program recalculates GPA, excludes repeated courses, emphasizes graduate work, or allows a probationary start. Many applicants lose an opportunity simply because they do not ask how the GPA policy is applied.
Yes, some applicants can get accepted into an online data science doctorate program with a low GPA, especially if they show recent academic readiness and strong professional evidence. The key is to make the admissions committee comfortable with one question: can you handle doctoral-level quantitative work, research, and writing now?
A GPA below 3.0 is most damaging when it appears unexplained, recent, or connected to the exact skills needed in the doctorate, such as statistics, programming, linear algebra, databases, or research methods. It is less damaging when the weak grades are old, unrelated, followed by stronger graduate performance, or offset by substantial applied analytics work.
Applicants who need academic repair can also consider targeted undergraduate or post-baccalaureate coursework. For example, completing affordable programming, algorithms, or database courses through a cheapest online computer science degree pathway may help demonstrate readiness, especially for students whose prior transcripts lack technical depth.
The table below explains common low-GPA evidence categories. It helps you decide which type of proof will best offset your weakness:
| Low-GPA issue | Evidence that can reduce concern | Why it matters |
| Old undergraduate GPA below 3.0 | Strong master's GPA, recent graduate certificates, or upper-division technical coursework | Shows your current ability is stronger than your older record |
| Weak quantitative grades | Recent A or B grades in statistics, Python, R, SQL, machine learning, or research methods | Directly addresses readiness for data science coursework |
| Interrupted academic history | Brief GPA addendum explaining documented circumstances and recovery | Helps the committee interpret the record fairly |
| No graduate degree | Professional portfolio, certifications, publications, analytics projects, or supervisor recommendations | Provides alternate evidence of doctoral potential |
| Below-cutoff GPA at a strict school | Conditional admission request or application to a holistic-review program | Redirects the application toward schools with realistic flexibility |
A strong low-GPA strategy should be specific, concise, and evidence-based. These steps are most useful when you are close to the cutoff or have a clear upward trend:
- Request an unofficial pre-application review from admissions and ask whether the program uses cumulative GPA, graduate GPA, or last 60-credit GPA.
- Write a GPA addendum that explains the cause of weak grades without blaming others and focuses on what changed academically or professionally.
- Complete recent graded coursework in statistics, programming, machine learning, databases, or research methods before applying.
- Ask recommenders to comment directly on your analytical ability, persistence, writing skill, and readiness for doctoral work.
- Use your statement of purpose to connect your work experience to a realistic doctoral project, not just to explain why you want the title "doctor."
Avoid overexplaining. A GPA addendum should usually be short and factual. The main application should still emphasize readiness, fit, and contribution to the program.
Many online data science doctorate programs do not require GRE or GMAT scores, and others allow waivers. This is especially common in professional doctorates designed for working adults. Still, policies vary by school and can change from one admission cycle to the next, so applicants should verify the catalog, application portal, and admissions advisor's written guidance.
There is no single national database that reports GRE or GMAT waiver adoption rates specifically for online data science doctorates. That is an important limitation for applicants. Instead of searching for a universal statistic, focus on the waiver language used by each school: "not required," "optional," "waived with master's degree," "waived with professional experience," or "required unless waived by the program."
The table below explains the most common test-policy categories. It can help you avoid applying to a program that quietly still expects scores from applicants with weaker academic records:
| Testing policy | What it means | Who benefits most | Risk to watch |
| GRE/GMAT not required | Scores are not part of the standard application | Working professionals, low-test-score applicants, and career changers | The school may weigh GPA, writing, and experience more heavily |
| Test optional | Applicants may submit scores but are not required to do so | Applicants with strong scores that offset a weaker GPA | Weak scores can hurt if submitted unnecessarily |
| Waiver with master's degree | A completed graduate degree can replace standardized testing | Applicants with strong graduate performance | Some schools still require a minimum graduate GPA |
| Waiver with experience | Significant professional work can support a waiver request | Analytics managers, engineers, data architects, and technical leaders | Experience must be documented clearly |
| Required unless waived | Testing is the default, but exceptions may be approved | Applicants who can justify a waiver before applying | Do not assume approval without written confirmation |
If you want to bypass the GRE or GMAT, treat the waiver request as part of your admissions strategy. The strongest waiver requests replace test scores with more relevant evidence:
- Use a completed master's degree or graduate certificate with strong grades as proof of academic readiness.
- Document professional analytics experience with job titles, project scope, tools used, and measurable business or research impact.
- Include technical certifications only when they are relevant to the curriculum, such as cloud analytics, machine learning, database, or statistical programming credentials.
- Ask whether a writing sample, interview, or prerequisite course can substitute for test scores.
- Get the waiver decision in writing before paying application fees when possible.
Submitting optional scores makes sense only when they strengthen your file. If your GRE quantitative score is strong and your GPA is weak, it may help. If your scores are average and the program is truly test optional, your application may be stronger without them.
Prior professional experience is not always required, but it is often highly valuable for online data science doctorate admissions. Applied and professional doctorates are built around real problems, so admissions committees may view industry experience as evidence that you can define a meaningful research question, manage ambiguity, and apply advanced methods in practice.
Experience matters most when the applicant's academic profile is imperfect. A senior analyst with a 2.9 undergraduate GPA, strong recent coursework, and documented machine learning projects may be more compelling for an applied doctorate than a 4.0 student with no practical exposure to data systems, stakeholders, or messy real-world datasets.
The 2024 BLS Occupational Outlook Handbook reports a median annual wage of $108,020 for data scientists, based on May 2023 wage data. This does not mean a doctorate automatically leads to that salary, but it shows why applicants often use advanced credentials to compete for senior, specialized, or leadership-oriented analytics roles.
The table below shows how different kinds of experience are usually interpreted in doctoral admissions. Use it to decide what to emphasize in your resume and statement of purpose:
| Experience type | Admissions value | Best evidence to provide | Most relevant doctorate path |
| Data science or machine learning role | Very strong | Modeling projects, deployment work, technical stack, measurable impact | Data science, AI, analytics, or computer science doctorate |
| Business analytics or BI role | Strong | Dashboards, forecasting, decision-support systems, stakeholder outcomes | DBA, analytics, information systems, or applied data science doctorate |
| Software engineering or data engineering | Strong | Pipelines, databases, cloud systems, distributed computing, production systems | IT, computer science, data engineering, or applied analytics doctorate |
| Domain expert using data | Moderate to strong | Healthcare, education, finance, public-sector, or operations analytics projects | Applied, interdisciplinary, or domain-focused doctorate |
| No direct analytics experience | Limited | Recent technical coursework, portfolio projects, research writing, prerequisites | Bridge-friendly or foundational doctorate pathway |
Professional experience is not a substitute for every prerequisite. If you have never taken statistics or programming, admissions may still require leveling courses. But experience can help explain why the doctorate is a logical next step and why your research project would matter outside the classroom.
Yes, non-data science majors can qualify for some online data science doctorate programs, but they usually need to prove quantitative and technical readiness. Common feeder backgrounds include computer science, statistics, mathematics, engineering, information systems, economics, business analytics, operations research, public health, education research, and social science methods.
The biggest issue is not the name of your major. It is whether your transcript and experience show enough preparation in programming, statistics, databases, research methods, and applied problem-solving. A psychology major with graduate statistics and Python experience may be better prepared than a computer science major who has never studied inference, modeling, or research design.
Applicants with a major mismatch can use bridge coursework or accelerated prerequisite pathways to close gaps before applying. For example, an accelerated computer science degree online can be useful for students who need structured preparation in programming, algorithms, systems, and databases before attempting doctoral-level data science coursework.
The table below compares common mismatch solutions. It is meant to help you understand which option fits your preparation level before you contact admissions:
| Option | Best for | What it demonstrates | Main limitation |
| Leveling courses | Applicants missing one or two prerequisites | Readiness in specific areas such as statistics, Python, SQL, or research methods | May delay full doctoral enrollment |
| Bridge program | Applicants from adjacent fields | Structured transition into data science foundations | Can add cost and time |
| Graduate certificate | Applicants needing recent graded evidence | Current academic ability at the graduate level | Credits may not always transfer |
| Prior Learning Assessment | Experienced professionals with documented learning outside college | Relevant skills gained through work, training, or certifications | Policies vary widely and may not apply to doctoral core courses |
| Professional portfolio | Applicants with applied projects but limited formal coursework | Ability to solve real data problems using relevant tools | Usually supplements rather than replaces prerequisites |
If you plan to use Prior Learning Assessment or portfolio evidence, prepare it before the application deadline. Admissions and credit-review teams need clear documentation, not a general claim that you have "worked with data."
- List each relevant skill, such as Python, R, SQL, machine learning, visualization, cloud platforms, or research design.
- Attach evidence, such as project summaries, sanitized dashboards, Git repositories, certificates, employer training records, or publications.
- Map each item to a specific prerequisite or course outcome in the doctoral curriculum.
- Ask whether PLA can reduce prerequisites, award transfer credit, or only strengthen admission review.
- Confirm whether doctoral residency, dissertation, capstone, or research credits are excluded from PLA.
Bridge courses are usually better when you lack foundational knowledge. PLA is usually better when you already have the knowledge and can document it. Do not use PLA to avoid learning skills you will need later in the doctorate.
Online data science doctorate programs can be less competitive than on-campus programs, but only in certain ways. They may be easier to access because they are designed for part-time students, use multiple start dates, and do not always depend on a single faculty member's lab funding. However, the coursework, research expectations, and final project can still be demanding.
The most selective on-campus PhD programs often admit students based on research fit, available supervision, assistantship funding, and publication potential.
Online professional doctorates typically focus more on whether the applicant has the academic foundation, work discipline, and applied problem to complete the degree while balancing employment.
The table below compares online and on-campus routes. Use it to decide whether easier admission is worth the trade-off for your goals:
| Factor | Online doctorate | On-campus doctorate | Decision point |
| Admissions model | Often holistic, rolling, or cohort-based | Often annual, faculty-driven, and research-fit dependent | Online may be easier for working professionals |
| Funding | Often self-funded, employer-funded, or loan-funded | May include assistantships or fellowships, especially in PhD programs | Campus may be better if full funding is essential |
| Schedule | Designed for part-time or flexible study | Often full-time or residency-heavy | Online fits employed students better |
| Research intensity | Often applied or practice-based | Often theory-building or lab-based | Choose based on career target, not convenience alone |
| Networking | Virtual cohorts, residencies, employer-based projects | In-person labs, seminars, teaching, and campus research culture | Campus may be stronger for academic careers |
The right choice depends on your intended outcome. Choose an easier-access online doctorate if you are already employed, need schedule flexibility, and want to apply data science to organizational problems. Choose a more competitive on-campus PhD if you want intensive research training, funded study, lab access, or a stronger path toward tenure-track academic roles.
Do not assume online means less rigorous. A well-designed online doctorate may require advanced statistics, machine learning, research design, publication-quality writing, residencies, and a defended final project. The easier part is often access, not completion.
Yes, some online data science doctorate programs do not require a traditional dissertation in the classic PhD sense. Instead, they may use an applied dissertation, doctoral capstone, portfolio, consulting project, design-based research project, or practice-improvement study. These models can be more accessible for working professionals because they connect the final requirement to real organizational data problems.
A non-traditional final project is not a shortcut. It still requires research design, literature review, data collection or analysis, ethical review when applicable, faculty approval, and a formal defense or presentation. The difference is that the output may focus on solving or evaluating a practical problem rather than producing a purely theoretical contribution.
The table below explains common doctoral completion models. Use it to match the final requirement with your strengths and career goals:
| Completion model | Typical output | Best fit | Important caution |
| Traditional dissertation | Original research study with theoretical or methodological contribution | Academic, research, or PhD-oriented careers | Can require a longer topic-development and committee process |
| Applied dissertation | Research study addressing a real practice problem | Practitioners who want scholarly depth and workplace relevance | Still requires rigorous research methods |
| Doctoral capstone | Applied project, product, intervention, model, or evaluation | Working professionals solving organizational analytics problems | May be less suitable for research-intensive academic roles |
| Portfolio doctorate | Integrated body of projects, papers, and reflective analysis | Experienced professionals with multiple advanced projects | Assessment standards must be clearly documented |
| Consulting or client-based project | Data-driven solution for an organization or stakeholder | Analytics leaders, consultants, and product strategists | Data access, confidentiality, and permissions can be challenging |
Capstone-based pathways are often a better fit if you want to improve a business process, evaluate an AI model, build a data governance framework, or measure the impact of an analytics intervention. A traditional dissertation is often better if you want to publish in academic journals, pursue a faculty role, or contribute to statistical or machine learning theory.
Before enrolling, ask how many checkpoints the final project includes. Programs may advertise a capstone but still require a proposal defense, institutional review board approval, multiple research courses, committee approval, and a final defense. That structure is not bad, but you should understand it before assuming the program will be faster.
How can students increase their chances of getting into a Data Science doctorate program?
Students can increase their chances of admission by applying strategically rather than broadly. The strongest applicants do not simply look for the easiest program; they match their academic record, work history, technical preparation, and career goal to the right admissions model.
Use this sequence to build a stronger application before submitting. It is especially important if you have a low GPA, no GRE score, a non-data science major, or limited research experience:
- Verify institutional accreditation first, preferably regional accreditation recognized by the U.S. Department of Education or the Council for Higher Education Accreditation.
- Choose the right doctorate type: applied doctorate for practice leadership, PhD for research careers, DBA or DIT for business or technology leadership, and domain-specific doctorate for healthcare, education, or policy analytics.
- Ask admissions how GPA is calculated, whether conditional admission is available, and whether the last 60 credits or graduate GPA can carry more weight.
- Complete missing prerequisites before applying if the program does not offer leveling courses.
- Prepare a focused statement of purpose that identifies a realistic data science problem, the population or organization affected, and why the program's curriculum fits the project.
- Select recommenders who can speak to doctoral readiness, not just job performance or character.
- Submit a resume that quantifies analytics impact, such as models deployed, systems improved, decisions supported, costs reduced, risks identified, or teams led.
- Request GRE, GMAT, prerequisite, or GPA waivers early and keep written confirmation.
- Compare total cost, transfer credit, residency requirements, dissertation or capstone structure, and expected time to completion before enrolling.
Applicants often weaken their own odds by focusing only on convenience. These common mistakes can lead to rejection, poor fit, or a doctorate that does not support the intended career outcome:
- Applying without verifying regional or recognized institutional accreditation.
- Assuming "online" means lower academic expectations.
- Ignoring GPA addendum opportunities when the transcript clearly needs context.
- Choosing a high-acceptance program without checking faculty expertise in data science.
- Submitting a generic personal statement that does not identify a doctoral-level problem.
- Relying on professional experience while leaving prerequisite gaps unresolved.
- Missing transfer credit, Prior Learning Assessment, or employer tuition benefit policies.
- Choosing a capstone doctorate when the desired career requires a research PhD.
- Failing to ask whether residencies, synchronous sessions, or dissertation defenses require travel or fixed scheduling.
Before you apply, ask admissions advisors direct questions. These questions help reveal whether the program is genuinely accessible and whether it is a good investment.
- What GPA calculation does the admissions committee use for applicants with multiple degrees?
- Is conditional admission available for applicants below the stated GPA requirement?
- Are GRE or GMAT scores required, optional, or waivable, and what documentation is needed?
- Which prerequisites must be completed before enrollment, and which can be completed during the program?
- Can professional experience, certifications, or portfolio work reduce prerequisites or support admission?
- Is the final requirement a traditional dissertation, applied dissertation, capstone, or another model?
- What is the typical time to completion for part-time students who are working full time?
- What are the total tuition, fees, residency costs, and continuation fees after coursework?
- What support is available for doctoral writing, statistics, research design, and dissertation or capstone completion?
The best admissions workaround is not hiding weaknesses. It is showing that the weakness has been resolved, offset, or made irrelevant by stronger recent evidence. A transparent, well-documented application is more persuasive than an application that hopes the committee will overlook missing information.
Other Things You Should Know About Data Science
Many online data science doctorates take about three to five years, depending on transfer credits, enrollment pace, dissertation or capstone progress, and whether the student studies part time. Programs with heavy research requirements may take longer.
Costs vary widely by institution, credit load, fees, residency requirements, and whether the student receives employer tuition support. Applicants should compare total program cost, not just per-credit tuition, because dissertation continuation fees and residencies can change the final price.
It can be worth it for senior analytics leadership, research-oriented industry roles, consulting, AI governance, or university teaching goals. It may not be necessary for entry-level or mid-level data science roles where a strong master's degree, portfolio, and experience may be enough.
Applicants should first verify institutional accreditation through a recognized accreditor. Programmatic accreditation is less standardized in data science than in fields like nursing or counseling, so curriculum quality, faculty expertise, employer relevance, and institutional reputation also matter.
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