2027 Master's in Data Science vs. Master's in Information Systems: Choosing Your Graduate Path
Co-Founder and Chief Data Scientist
A master's in data science and a master's in information systems can both lead to work with data, but they prepare students to solve different problems. Data science generally emphasizes statistical modeling and machine learning; information systems emphasizes how organizations select, build, govern, and use technology. If you are choosing between them, the useful question is not which title sounds more valuable. It is which curriculum closes the gap between your current skills and the work you want to do.
Key Things You Should Know
- Choose data science if you want substantial training in statistics, programming, and predictive modeling; choose information systems if you want to connect technology, processes, and organizational decisions.
- The U.S. Bureau of Labor Statistics (BLS) projects 35% employment growth for data scientists from 2025 to 2035, but that occupation's outlook does not describe every job available to graduates of either degree.
- Compare required courses, projects, total cost, and graduate destinations - not degree titles alone. Programs bearing the same name can differ substantially.
What is the difference between a master's in data science and information systems?
A data science master's typically centers on extracting and testing insights from data through statistics, computing, and modeling. An information systems master's typically centers on applying and managing technology within an organization, including databases, systems design, governance, and business processes. Both may teach SQL, analytics, and communication, so the distinction is one of emphasis rather than an absolute boundary.
The comparison below describes common program orientations, not requirements shared by every university.
| Decision criterion | Data science master's | Information systems master's |
| Central question | What can data explain or predict, and how reliable is the result? | What technology and processes will help an organization operate or decide effectively? |
| Typical technical emphasis | Statistics, programming, machine learning, and model evaluation | Databases, systems analysis, technology management, and implementation |
| Typical project output | An analysis, model, or data product with documented evaluation | A system proposal, implementation plan, process redesign, or technology solution |
| Best fit | Someone seeking hands-on analytical or modeling work | Someone seeking to connect technical systems with organizational needs |
If your goal falls between the two, data analytics programs at the master's level offer a third category to examine. Compare their required courses with both degrees rather than assuming "analytics" signals a fixed level of technical depth.
Which master's degree offers better career opportunities: data science or information systems?
Neither degree has universally better opportunities. A data science program is the more direct preparation for roles built around statistical analysis and machine learning. Information systems may be a stronger fit for roles focused on translating organizational needs into technology requirements, managing data systems, or improving how technology is used.
Your starting point matters. A software developer seeking deeper modeling skills may gain more from data science; an analyst who wants to lead systems implementations may gain more from information systems. Before comparing job forecasts, identify two target roles and read current postings: note which skills recur, which are merely preferred, and whether your existing experience already meets part of the requirement. That test is more useful than assuming a degree title qualifies you for a job.
What jobs can you get with a master's in data science versus information systems?
Data science graduates may pursue data scientist, machine learning, or advanced analytics roles when their coursework and project experience match the employer's requirements. Information systems graduates may pursue systems analyst, business systems analyst, IT project, database-related, or technology consulting roles. Both paths can lead to analytics work; job titles and qualifications vary by employer.
A useful distinction is the work product. If you want to build and validate a forecasting model, favor programs that require substantial modeling projects. If you want to define requirements, coordinate stakeholders, and improve an organization's systems, look for implementation and systems-analysis projects. For either path, a master's degree is not a substitute for evidence that you can do the target work.
How do data science and information systems curricula compare?
Read the required-course sequence before counting electives. A data science program may include probability, statistical inference, programming, machine learning, and a capstone. An information systems program may include database management, systems analysis, enterprise technology, project management, and governance. Individual programs can depart substantially from those patterns.
Look beyond course titles: a machine learning elective is not equivalent to a sequence that requires mathematical preparation, model evaluation, and an applied project. Likewise, a database course may teach query writing, system design, or both. If building data pipelines is a priority, examine project requirements and course descriptions alongside programs that compare AI master's degrees with data engineering content; the presence of "AI" or "data" in a title does not establish pipeline depth.
One common mistake is choosing the degree with the more technical-sounding title without checking its required work. Request syllabi or detailed course descriptions for the courses most important to your target role.
What are the admission requirements for each master's degree?
Requirements vary by institution. Data science programs may expect preparation in programming, calculus, linear algebra, or statistics; information systems programs may emphasize quantitative readiness, technology experience, business preparation, or a combination. Neither degree title establishes a universal prerequisite list, and applicants from other undergraduate fields should check how each program evaluates equivalent coursework and professional experience.
Separate admission eligibility from readiness to succeed. A program might admit you without a particular course, yet expect you to use its concepts immediately. Ask whether prerequisites must appear on a transcript, whether bridge courses are available, and whether they add time or cost. Application components also vary: a program's test or essay policy must be checked directly. If minimizing written application requirements matters, comparisons of ML master's admissions without an essay illustrate an alternative to investigate, not a rule for data science or information systems admissions.
How much do these master's programs cost and how long do they take?
There is no reliable field-wide price or completion time for either degree. Credit requirements, tuition rates, fees, course availability, and full- or part-time enrollment differ by program. A shorter program is not necessarily cheaper if its per-credit price is higher; a lower advertised tuition rate may exclude mandatory fees.
For a fair comparison, calculate each program's expected total using its published requirements and current charges. Include tuition, mandatory fees, any required materials or campus visits, and the cost of prerequisite courses. Then check whether your intended pace is possible given course sequencing and whether employer assistance or scholarships apply. Treat forgone earnings or reduced work hours as a separate opportunity cost, not as tuition. Do not assume a later salary increase will repay the degree: outcomes depend on your prior experience, role, employer, and location.
Should you choose an online or campus-based program?
Choose the format that lets you complete the coursework and projects your target role requires. Online study can reduce travel and make continued employment easier; campus study may make in-person collaboration and access to local recruiting more convenient. Neither format, by itself, establishes curriculum quality or career outcomes.
For online programs, confirm whether classes meet live, how group work is scheduled, whether exams require a particular time or location, and whether any campus visits are mandatory. For campus programs, confirm course meeting times and how often required classes run. These details matter especially if you will work while enrolled; data science master's study options for the workforce can help frame the scheduling questions to ask. Also verify that an online institution can enroll students in your state before applying.
How do salaries and job outlook compare between the two fields?
BLS reports a $120,230 median annual wage for data scientists in May 2025 and projects 35% employment growth from 2025 to 2035. Those figures describe an occupation, not the earnings or placement rate of data science master's graduates. They also cannot be compared directly with "information systems" as if it were a single occupation: that degree can lead to several jobs with different pay and outlooks.
Compare the specific occupations you would realistically pursue after each program, using the same location and experience level. Industry can also change the salary context; a data science graduate salary guide by industry can help you identify distinctions worth investigating. Be wary of any program that presents an occupational median as a guaranteed graduate salary or reports an average without explaining whose outcomes were counted.
Do either of these master's degrees require licensing or certification?
Neither degree, by itself, leads to a general U.S. license to practice data science or information systems. Some employers may value certifications tied to a particular platform, security function, or project-management method, but those are separate from the master's degree and depend on the job. Regulated positions or industries can impose additional requirements. Check target job postings before paying for a certification simply because a program advertises it.
How can you choose an accredited and reputable program?
Verify the institution's current accreditation through the accreditor or the U.S. Department of Education, then assess the specific program on its own merits. Institutional accreditation does not mean every master's has the same curriculum, support, or results. Rankings can be a starting point, but they should not replace evidence relevant to your career goal.
Use the same questions for every program on your shortlist. They expose differences that marketing descriptions can hide.
- Which required courses and projects directly develop the skills in my target job postings?
- What is the full expected cost at my planned pace, including required fees and prerequisites?
- Can I see recent, clearly defined graduate outcomes - and who was included or excluded?
- What advising, project support, and career services can students in my chosen format actually use?
Choose data science when its required analytical depth matches the work you want and the preparation you can undertake. Choose information systems when systems, implementation, and organizational decision-making are closer to your goal. If two programs both appear suitable, let their verified coursework, project opportunities, cost, and schedule - not the degree label - break the tie.
Other Things You Should Know About Data Science
Possibly, but do not assume credits will transfer. The receiving program decides which courses count, and different prerequisites or required sequences may extend your studies. Ask both programs for a written assessment before switching.
Electives can narrow the gap, but availability and prerequisites matter. Compare completed course sequences and projects, not elective names alone, if you need substantial statistics or machine learning preparation.
Try a small version of the work: build and evaluate a model for data science, or document requirements and design a data-enabled process improvement for information systems. Notice which problems you want to investigate more deeply.
Not necessarily. Relevant work, targeted courses, and demonstrable projects may address a specific skills gap. Consider another degree when the roles you want consistently require deeper preparation that you cannot reasonably gain through a narrower route.
References
- Postgraduate Employment Outcomes of Undergraduate and Graduate Public Health Students: A Scoping Review | nih.gov https://pmc.ncbi.nlm.nih.gov/articles/PMC8579388/
- Business Analytics vs. Information Systems - UC Online https://www.online.uc.edu/blog/business-analytics-vs-information-systems-whats-the-difference.html
- What You Can Do with a Master's in Data Science and Artificial Intelligence | Harvard Extension School https://extension.harvard.edu/blog/what-you-can-do-with-a-masters-in-data-science-and-artificial-intelligence/
- What Can You Do with a Master's Degree in Data Science? | UCLA MSOL Data Science https://www.msol.ucla.edu/data-science-engineering/articles/what-can-you-do-with-masters-degree-data-science/
- Business analytics vs data science: Which career is right for you? | Uncover IE https://www.ie.edu/uncover-ie/business-analytics-vs-data-science-master-in-business-analytics-and-data-science/
- Exploring the Costs of Online Degrees | Boston University https://www.bu.edu/online/2025/10/17/exploring-the-costs-of-online-degrees/
- 5 Reasons why an MS in Information Systems can be your best career move | cmich.edu https://www.cmich.edu/blog/all-things-higher-ed/5-reasons-why-an-ms-in-information-systems-could-be-your-best-career-move
- MIS vs. Data Science - What's the Right Choice? | AdmitEDGE Blog https://www.admitedge.com/blog/mis-vs-data-science-whats-the-right-choice/
- Best Master's in Information Systems Degrees | computerscience.org https://www.computerscience.org/degrees/masters/information-systems/
