2027 Best Online Data Science Degree Master's Programs for Working Professionals
Co-Founder and Chief Data Scientist
Choosing an online data science master's program as a working professional is a practical ROI decision: Will the degree help you move into stronger analytics, machine learning, or data leadership roles without forcing you to pause your career? The answer depends less on marketing claims and more on accreditation, curriculum fit, scheduling flexibility, total cost, admissions requirements, and the career support available to online students.
Demand for data science master's degrees has surged, with enrollment increasing by over 25% annually in recent years. That growth has created more options, but also more variation in quality. Some programs are built for full-time students with predictable schedules; others are designed for professionals who need asynchronous coursework, part-time pacing, employer tuition reimbursement compatibility, and applied projects they can connect to current work.
This guide compares what matters most when evaluating online data science master's programs for working adults. You will learn how to assess program reputation, accreditation, GPA and prerequisite expectations, curriculum structure, time to completion, total expenses, financial aid, technical requirements, and likely career outcomes.
Key Things to Know About the Best Online Data Science Degree Master's Programs for Working Professionals
- Eligibility typically requires a bachelor's degree in a related field and relevant work experience; some programs also mandate prerequisite coursework or professional certifications to ensure preparedness.
- Application procedures often involve submitting transcripts, letters of recommendation, and personal statements, emphasizing career goals aligned with the program's focus and flexible scheduling.
- Institutional policies prioritize flexible curricula, strong alumni networks, and accredited program status, weighing factors such as cost, faculty expertise, and career support for optimal professional advancement.
What Are the Best Online Data Science Master's Programs Available for Working Professionals Today?
The best online data science master's programs for working professionals combine academic rigor with realistic scheduling. A strong program should teach statistical modeling, programming, machine learning, data management, and applied problem-solving while giving employed students enough flexibility to progress without leaving the workforce. Data science careers continue to show strong growth, with employment projected to rise by 36% from 2021 to 2031, reflecting robust demand for advanced expertise.
When comparing programs, look beyond brand recognition. The right choice depends on whether the curriculum matches your target role, whether the delivery format fits your work schedule, and whether the institution provides meaningful support to online learners.
- North Carolina State University: This fully online Master of Science in data science emphasizes interdisciplinary coursework across statistics, computer science, and domain expertise. It is a strong fit for professionals who want a quantitative program with part-time study options, faculty engagement, and access to an alumni network connected to technology industries.
- Indiana University Bloomington: The MS in data science offers two distinct tracks in data science and analytics. Its asynchronous classes can be useful for professionals with unpredictable schedules, while its accreditation and research profile add credibility for students who want a recognized graduate credential.
- University of Illinois Urbana-Champaign: The Master of Computer Science in data science is delivered via Coursera and emphasizes self-paced learning and interactive projects. It may appeal to students who want a computer science-centered data science pathway from an institution known for STEM program excellence and employer connections.
- University of Southern California: The MS in data science focuses on practical skills, real-world datasets, and capstone projects. Evening synchronous sessions and comprehensive student support can work well for professionals who prefer scheduled interaction rather than a fully self-paced model.
- Oregon State University: The Master of Science in data analytics offers modular formats and multiple start dates. Its applied learning model, industry partnerships, diverse faculty, and financial aid opportunities make it worth reviewing for students who want flexibility and career relevance.
| Program factor | Why it matters for working professionals | What to verify before applying |
| Delivery format | Determines whether coursework fits around work hours, travel, and family responsibilities. | Confirm whether classes are asynchronous, live, hybrid, or cohort-based. |
| Curriculum focus | Shapes your readiness for roles in analytics, machine learning, data engineering, or leadership. | Review required courses, electives, capstone expectations, and software tools. |
| Student support | Online students need access to advising, career coaching, tutoring, and technical help. | Ask whether online students receive the same services as campus students. |
| Career outcomes | Helps you judge whether the program has a track record of helping graduates advance. | Request employment rates, common job titles, and salary information when available. |
These programs were selected based on accreditation, curriculum quality, flexibility, faculty expertise, and proven positive student outcomes, all crucial factors for working professionals aiming to enhance their careers. If cost is a major concern, it can also help to compare how other online graduate fields present affordability information, such as resources on affordable online graduate programs.
What Accreditation Standards Should an Online Data Science Master's Program Meet?
An online data science master's program should be offered by an institution with recognized institutional accreditation. Accreditation matters because it affects employer recognition, transferability of credits, eligibility for federal financial aid, and the credibility of the degree if you later pursue doctoral study or professional advancement. As the demand for skilled computer and information research scientists grows 22% from 2020 to 2030, students should be especially careful about verifying program legitimacy before enrolling.
- Regional vs. programmatic accreditation: Regional accreditors such as the Southern Association of Colleges and Schools Commission on Colleges (SACSCOC) and the Higher Learning Commission (HLC) evaluate the quality of the institution as a whole. Programmatic accreditation is field-specific, but it is less common for data science than for fields such as nursing, education, or engineering.
- Credential recognition: Degrees from regionally accredited institutions are generally more widely accepted by employers, universities, and professional organizations. This is especially important if you plan to use the master's degree for promotion, a career change, or future graduate study.
- Financial aid eligibility: Students must attend an accredited institution to qualify for federal aid. If a school cannot clearly document recognized accreditation, treat that as a serious warning sign.
- Verification resources: Use the U.S. Department of Education's Database of Accredited Postsecondary Institutions and Programs and the Council for Higher Education Accreditation (CHEA) website to confirm accreditation status. Do not rely only on a school's advertising language.
- Red flags of diploma mills: Be cautious of institutions that promise unusually fast completion, provide vague accreditation claims, lack transparent faculty credentials, have no verifiable academic catalog, or avoid clear information about admissions and graduation requirements.
| Accreditation question | What a trustworthy program should provide |
| Who accredits the institution? | The school should name a recognized institutional accreditor and make that status easy to verify. |
| Is the online degree the same credential as the campus degree? | The transcript and diploma policies should be clearly explained by the university. |
| Can students use federal financial aid? | The school should explain FAFSA eligibility and financial aid procedures through an official aid office. |
| Are faculty credentials public? | Faculty pages should list relevant academic qualifications, research, or industry expertise. |
For working professionals considering flexible graduate education more broadly, resources on accelerated online doctoral pathways can also show how accreditation and program format influence credibility across online degrees.
What Is the Minimum GPA Requirement for an Online Data Science Master's Program?
Most online data science master's programs expect a minimum undergraduate GPA near 3.0 on a 4.0 scale. Some programs are more selective, while others use conditional admission for applicants who fall below the preferred GPA but show strong preparation through work experience, technical coursework, or certifications. Acceptance rates for STEM-related graduate programs hover around 40-50%, so applicants should treat GPA as important but not the only admissions factor.
- Typical GPA requirements: Many programs list a minimum GPA near 3.0 on a 4.0 scale. Applicants below that level may still have options, especially at programs with holistic admissions or conditional pathways.
- Holistic admissions review: Admissions committees often review GPA alongside professional experience, recommendation letters, statements of purpose, prerequisite coursework, and evidence of programming or analytics ability.
- Strengthening applications with a lower GPA: Applicants can improve their profile by completing relevant post-baccalaureate courses, earning analytics or programming certifications, building a portfolio, or clearly explaining career growth since their undergraduate studies.
- Selective vs. accessible programs: Highly competitive programs often require GPAs above 3.3, while more accessible options may accept GPAs as low as 2.8 and place greater emphasis on professional experience and demonstrated technical readiness.
How to evaluate your GPA risk
| Your academic profile | How to strengthen your application |
| GPA at or above the stated minimum | Focus on showing fit through your goals, recommendations, and relevant technical experience. |
| GPA slightly below the stated minimum | Contact admissions before applying and ask whether conditional admission is available. |
| Lower GPA but strong work experience | Use your statement and resume to connect professional projects to data science readiness. |
| Lower GPA and limited technical background | Complete prerequisite or bridge coursework before applying to show current preparation. |
A graduate from an online data science master's program shared that despite an initial GPA below the preferred threshold, persistence and strategic choices made a difference. “I focused on gaining certifications and framing my professional experience clearly in my application,” they explained. “The program's flexibility allowed me to complete coursework alongside full-time work, which was challenging but rewarding. Earning the degree noticeably boosted my confidence and opened doors in my career.” Their experience shows why applicants should not self-reject based on GPA alone, but they should address weaknesses directly and provide evidence of readiness.
Online data science master's programs usually expect applicants to have a bachelor's degree and prior preparation in quantitative reasoning, programming, and statistics. You do not always need an undergraduate degree specifically in data science, but you do need enough technical foundation to handle graduate-level coursework in machine learning, modeling, databases, and applied analytics.
- Undergraduate degree in a related field: Many programs prefer applicants with a bachelor's degree in computer science, statistics, mathematics, engineering, economics, or another STEM discipline. These backgrounds typically provide the quantitative foundation needed for advanced data science study.
- Prerequisite coursework: Common prerequisites include calculus, linear algebra, probability and statistics, and programming fundamentals. These courses support the mathematical and computational work used throughout the degree.
- Bridge or leveling courses: Some programs offer introductory or remedial coursework for students from adjacent fields. This can help applicants from business, social science, health, or other backgrounds move into data science without repeating a full undergraduate program.
- Professional experience: Relevant work in programming, analytics, database management, reporting, business intelligence, or data-related operations can sometimes help offset missing academic prerequisites, especially in programs designed for working professionals.
- Technical skills demonstration: Programs may ask applicants to show proficiency in Python, R, or SQL through coursework, certifications, projects, or professional experience.
Prerequisite checklist before applying
| Preparation area | Why it matters | How to document it |
| Programming | Data science coursework often requires writing code, manipulating datasets, and building models. | Course transcripts, work projects, certifications, or a portfolio. |
| Statistics | Statistical reasoning supports modeling, inference, experimentation, and evaluation. | Completed statistics coursework or applied analytics experience. |
| Mathematics | Calculus and linear algebra support machine learning and optimization concepts. | Undergraduate coursework or approved bridge courses. |
| Data tools | SQL, R, Python, and visualization tools are commonly used in graduate assignments. | Professional experience, project samples, or technical assessments. |
Review prerequisites early because missing requirements can delay admission or increase your total cost. If you are comparing the long-term value of different educational paths, it may also be useful to review broader information on college majors associated with higher earnings.
An online data science master's curriculum typically combines statistics, programming, databases, machine learning, data visualization, and applied projects. For working professionals, the best curriculum is not simply the most technical one; it is the one that builds skills aligned with your target role while offering a pace and workload you can sustain.
- Core courses: Foundational subjects usually include statistics, machine learning, data mining, and database systems. These courses build the technical base needed to collect, clean, analyze, model, and interpret data.
- Elective tracks: Many programs allow specialization in business analytics, artificial intelligence, big data engineering, or related areas. Electives are important because data science roles vary widely across industries and job functions.
- Capstone and thesis options: Most programs include a culminating project or thesis. A capstone is often more applied and can be useful for building a portfolio, while a thesis may be better suited for students considering research-focused roles or future doctoral study.
- Practicum or internship: Some programs include practicums or internships that provide exposure to workplace data problems. Working professionals should ask whether current employment projects can satisfy applied learning requirements.
- Flexible formats: Courses may run in modular formats such as 8-week terms, weekend intensives, or fully asynchronous delivery. Shorter terms can accelerate progress but may feel demanding when paired with full-time work.
- Curriculum models: Traditional credit-hour programs follow structured course sequences and time-based progression. Competency-based education (CBE) allows students to advance by demonstrating mastery, which may benefit professionals with existing experience.
- Advising and syllabi review: Before enrolling, review detailed syllabi and speak with an academic advisor. Confirm the workload, programming expectations, group project requirements, exam format, and availability of electives.
| Curriculum component | Best for | Potential drawback |
| Machine learning and AI electives | Students targeting modeling, automation, or machine learning engineering roles. | May require stronger math and programming preparation. |
| Business analytics electives | Professionals aiming for analytics leadership, strategy, or decision-support roles. | May be less technical than some data science positions require. |
| Data engineering coursework | Students interested in pipelines, databases, cloud systems, and scalable data infrastructure. | Can be highly technical and tool-dependent. |
| Capstone project | Professionals who want portfolio evidence for employers. | May require significant time management near the end of the program. |
A graduate shared that balancing a full-time job and study was challenging but manageable because of asynchronous modules. “I really appreciated being able to watch lectures and complete assignments on my own schedule—it made tackling difficult topics manageable without feeling overwhelmed.” The capstone project, they noted, connected the coursework to practical workplace problems and improved their confidence in applying data science skills on the job.
Most online data science master's programs take 12 to 18 months for full-time students and 2 to 3 years for part-time students. Accelerated options that allow completion in less than 12 months exist, but they are less common because data science coursework can be technically demanding. For working professionals, the fastest option is not always the best option if it creates unsustainable weekly workload or limits time for applied projects.
- Flexible enrollment options: Part-time and full-time tracks let students choose a pace that fits work demands, family responsibilities, and financial planning. A realistic pace can help prevent burnout.
- Credit transfer and prior learning: Some programs allow transfer credits from relevant prior graduate study or recognition of prior learning. These options can reduce required coursework and shorten time to completion.
- Dual enrollment opportunities: In some cases, students may be able to complete prerequisite or complementary courses in a way that streamlines their academic path.
- Common delays: Work travel, family obligations, financial interruptions, course sequencing, and changes in program requirements can extend graduation timelines.
- Realistic planning: Before enrolling, estimate weekly study hours, identify heavy work seasons, and ask whether students can pause or reduce course load without penalty.
- Enrollment trends: Nearly 60% of online graduate students in STEM fields study part-time, reflecting the need for programs that support extended and flexible completion windows.
| Enrollment pace | Typical completion time | Best fit |
| Full-time | 12 to 18 months | Students who can dedicate significant weekly time to coursework. |
| Part-time | 2 to 3 years | Working professionals balancing employment and personal responsibilities. |
| Accelerated | Less than 12 months | Students with strong preparation and substantial available study time. |
Ask each program how often required courses are offered. A program may advertise flexible pacing, but if a required course is available only at limited times, a missed term can delay graduation.
Online data science master's programs typically charge $600 to $1,500 per credit hour. Most programs require between 30 and 36 credit hours, which places total tuition between $18,000 and $54,000. Many online programs use a flat tuition rate for all students regardless of in-state or out-of-state residency, which can benefit professionals who do not live near the university.
Tuition is only part of the total cost. Working professionals should also budget for mandatory technology fees, often ranging from $100 to $500 per semester, along with possible thesis submission fees, comprehensive exam charges, textbooks, specialized software licenses, and professional association subscriptions. Admissions to data science master's programs have increased by approximately 15% annually, so applicants should compare costs carefully rather than assuming higher tuition always signals better outcomes.
- Flat online tuition rates: Many online programs charge the same rate to all online enrollees, which can reduce uncertainty for students comparing public universities outside their home state.
- Credit hour requirements: Because programs usually require 30 to 36 credit hours, total tuition is heavily influenced by the per-credit price.
- Additional fees: Technology fees between $100 and $500 per semester and other program charges can increase the actual cost beyond advertised tuition.
- Indirect costs: Textbooks, software, cloud computing tools, exam proctoring, and subscriptions to organizations such as the Data Science Association can add several hundred dollars annually.
- Cost estimation tools: Use the College Scorecard and university Net Price Calculators to estimate program costs and account for possible aid.
| Cost category | Amount stated | What to ask the program |
| Tuition per credit | $600 to $1,500 per credit hour | Is tuition fixed for the full program or subject to annual increases? |
| Credit requirement | 30 to 36 credit hours | Are foundation or bridge courses included in this total? |
| Total tuition | $18,000 to $54,000 | Does this include all required courses and capstone credits? |
| Technology fees | $100 to $500 per semester | Are software, cloud platforms, or proctoring fees included? |
Working professionals should compare total program cost against expected career benefits, employer tuition assistance, and the ability to continue earning while enrolled. For another example of how graduate affordability can be evaluated, review resources on the most affordable online master's degrees.
Working professionals can fund an online data science master's degree through federal loans, scholarships, fellowships, employer tuition reimbursement, and, in some cases, assistantships. The best approach is to build a funding plan before enrolling because reimbursement rules, scholarship deadlines, and loan limits can affect when and how you pay.
- Federal loans: Graduate students may use Direct Unsubsidized Loans and Graduate PLUS Loans regardless of financial need. Eligibility requires completing the Free Application for Federal Student Aid (FAFSA) each year. Submitting the FAFSA early can improve access to available support.
- Scholarships and fellowships: Awards may be based on academic achievement, field of study, professional goals, or demographic factors. Resources such as Fastweb and Scholarships.com compile scholarships ranging from $1,000 to $10,000. Professional organizations may also offer fellowships, often with winter or spring application periods.
- Employer tuition reimbursement: Many employers provide tuition assistance when the degree relates to the employee's current role or future business value. Before enrolling, confirm annual limits, eligible programs, grade requirements, repayment obligations, and documentation deadlines.
- Graduate assistantships and fellowships: These are less common for online students than for on-campus students, but some programs may offer limited opportunities. Ask the program coordinator directly rather than assuming online students are ineligible.
Questions to ask before relying on employer benefits
- Does the employer require pre-approval before the course begins?
- Is reimbursement paid upfront or only after grades are submitted?
- Are there minimum grade requirements?
- Does the employer require repayment if you leave the company?
- Is the program eligible if it is fully online?
Because financial aid and employer policies can be technical, keep written documentation of every award, reimbursement approval, and payment deadline. This protects you from unexpected out-of-pocket costs during the program.
Online data science master's programs support working professionals by offering asynchronous coursework, evening or weekend live sessions, part-time pacing, cohort structures, recorded lectures, and remote student services. The key is to choose a format that matches how you actually work and learn, not just the format that sounds most convenient.
- Synchronous, asynchronous, and hybrid formats: Synchronous courses provide live interaction, often in evening or weekend sessions. Asynchronous courses allow students to watch lectures and complete assignments on their own schedule. Hybrid models combine both, which can offer flexibility while preserving real-time discussion.
- Cohort-based learning: Cohorts can improve accountability and peer support, but they may offer less flexibility if all students move through the same sequence together.
- Recorded lectures and mobile accessibility: Recorded sessions allow students to revisit complex material, while mobile-friendly platforms can make it easier to review content during breaks or travel.
- Specialized support services: Strong programs provide online academic advising, career coaching, tutoring, technical support, library access, and peer mentoring for non-traditional students.
- Learning management system trials: Some programs offer demos, webinars, or open houses. Testing the learning platform before enrolling can reveal whether the program's technology fits your study habits.
| Format | Advantages | Trade-offs |
| Asynchronous | Maximum scheduling flexibility for changing work hours. | Requires strong self-discipline and time management. |
| Synchronous | Real-time interaction with faculty and classmates. | Less flexible for students with travel, shift work, or caregiving duties. |
| Hybrid | Balances independent work with scheduled engagement. | May still require attendance at fixed times. |
| Cohort-based | Creates structure, networking, and peer accountability. | May be harder to accelerate or slow down. |
Before applying, ask for a sample weekly schedule. A program may be “online” but still require live attendance, group meetings, proctored exams, or intensive project deadlines that can conflict with work obligations.
Students in online data science master's programs need a reliable computer, stable internet, and access to programming, database, visualization, and cloud-based tools. Because coursework can involve large datasets, modeling, and virtual collaboration, technical readiness is not optional; it affects your ability to complete assignments efficiently.
- Hardware and operating system: Programs typically require a modern computer running Windows 10 or later, macOS 10.15 or later, or a current Linux distribution. At least 8GB of RAM is necessary, though 16GB is recommended for smoother data processing and multitasking.
- Internet speed: A stable high-speed connection of 25 Mbps or higher is important for streaming lectures, participating in virtual meetings, downloading datasets, and accessing cloud resources.
- Software and tools: Programs often require Python, R, SQL, Jupyter notebooks, Tableau, or AWS platforms. Some schools provide licensed software or virtual lab access, while others expect students to purchase or subscribe independently.
- Accessibility and ADA compliance: Most programs conform to ADA guidelines through captioned videos, screen-reader compatibility, alternative text formats, and other accommodations for students with disabilities.
- Self-assessment: Before enrolling, compare your current laptop, internet connection, and software access against the program's stated requirements. Upgrading early can prevent problems during technical courses.
| Requirement | Minimum or common expectation stated | Why it matters |
| Operating system | Windows 10 or later, macOS 10.15 or later, or a current Linux distribution | Ensures compatibility with required programming and analytics tools. |
| Memory | At least 8GB of RAM; 16GB recommended | Supports multitasking, modeling, and work with larger datasets. |
| Internet | 25 Mbps or higher | Supports streaming, cloud tools, downloads, and live class participation. |
| Software | Python, R, SQL, Jupyter notebooks, Tableau, or AWS platforms | Forms the technical foundation for assignments and projects. |
Professionals comparing technical programs may also find it useful to review affordable online cybersecurity degree options, since cybersecurity and data science programs often have overlapping technology and flexibility requirements.
An online data science master's degree can support advancement into roles such as data scientist, machine learning engineer, analytics manager, and technical specialist, but outcomes depend on prior experience, portfolio quality, industry, location, and the program's employer connections. The degree can strengthen your credentials, but it does not guarantee a specific job title or salary.
- Upward mobility and job titles: Graduates often move into advanced roles such as data scientist, machine learning engineer, or analytics manager. These positions may require stronger modeling, programming, and analytical judgment than many bachelor's-level roles.
- Salary advantages: Median salaries for those with online data science master's degrees are significantly higher than those for bachelor's holders. Starting salaries reported range between $90,000 and $120,000, with variation by industry and geographic location.
- Career advancement within fields: Professionals who remain in their current industry may use the degree to pursue promotions, technical leadership, or stronger salary negotiations because they can combine domain expertise with advanced analytics skills.
- Industry transitions: Career changers may initially face lower salary midpoints while they build experience in data-focused roles. However, applied projects, networking, and targeted internships or practicums can help strengthen their transition.
- Graduation outcome transparency: Prospective students should request employment rates, median starting salaries, common employers, and examples of graduate job titles from admissions or career services offices.
| Career goal | Program features to prioritize |
| Move into data science from another field | Bridge coursework, portfolio projects, career coaching, and practical capstones. |
| Advance within analytics | Machine learning, statistics, business analytics electives, and leadership-oriented projects. |
| Target machine learning roles | Advanced programming, modeling, AI electives, and strong mathematical preparation. |
| Become an analytics manager | Applied analytics, communication, visualization, project management, and stakeholder-focused coursework. |
For the clearest ROI picture, compare each program's total cost with its documented outcomes. Ask whether salary data is based on all graduates, survey respondents only, or a smaller subgroup, and confirm whether online students are included in the reported results.
What Graduates Say About Getting Accepted in Online Data Science Degree Master's Programs for Working Professionals
- Paxton: "Applying for the online data science master's program tailored for working professionals was a game-changer for me. I appreciated how the program's flexible schedule allowed me to maintain my full-time job while advancing my education. Navigating the admissions process was straightforward thanks to clear guidelines, making the entire experience exciting and manageable."
- Ameer: "Choosing the right accredited institution offering a data science master's program online was a daunting task initially. I spent significant time comparing universities to ensure the program matched my career goals and had reputable accreditation. Reflecting on this journey, I now value how this careful research shaped a fulfilling academic experience and set me up for success in the field."
- Nathan: "Relying on official academic catalogs, accrediting agencies, and federal education resources helped me better understand the rigorous requirements of an online data science master's degree. This approach demanded a disciplined, professional mindset and made me confident in my decision to pursue this path. The clarity and credibility these resources provided were invaluable throughout my studies."
Other Things You Should Know About Data Science Degrees
Most programs require a completed application, official transcripts, a résumé or CV, a statement of purpose, and one to three letters of recommendation. Some also ask for GRE scores, proof of English proficiency for international applicants, and a short interview before admission decisions are made.
Online data science master's programs in 2027 are designed with flexibility to accommodate the busy schedules of working professionals. Options often include part-time enrollment, asynchronous learning, and customizable course schedules. These features allow students to balance career, personal commitments, and education effectively.
In 2027, working professionals can access financial aid options like federal student loans, school-specific scholarships, and grants specifically designed for online learners. Check with individual programs for unique scholarships tailored for data science students in online master's courses.
- Where Can a Master's in Data Science Take You? Top Career Paths Explained https://www.ebu.ac/blog/241-Where-Can-a-Masters-in-Data-Science-Take-You-Top-Career-Paths-Explained-blog.php
- 17 Data Science Scholarships https://www.mastersindatascience.org/resources/data-science-scholarships/
- Data science salaries and outlook: how much can I earn with an MSDS? https://onlinesoe.tufts.edu/news-events/news/data-science-salaries-and-outlook-how-much-can-i-earn-msds
- 15 Essential Skills You’ll Gain in an Online Master’s in Data Science https://www.ucumberlands.edu/blog/essential-skills-learned-in-an-online-masters-data-science
- Master in Big Data Science https://www.kozminski.edu.pl/en/programs/graduate-programs-master/master-big-data-science
- Scholarships for Online M.Sc. Programs: Financial Aid for Students https://amityonline.com/blog/scholarships-available-for-online-msc-programs
- Master of Data Science https://www.sci.pitt.edu/academics/masters-degrees/data-science
- 18 Data Science Tools to Consider Using in 2026 | TechTarget https://www.techtarget.com/searchbusinessanalytics/feature/15-data-science-tools-to-consider-using
- Program Details https://www.eastern.edu/academics/graduate-programs/ms-data-science
- Online Master’s Degree: Data Analytics https://www.umgc.edu/online-degrees/masters/data-analytics
