Top 5 Master Programs for Professionals Comparing AI Tracks and Data Science Tracks in 2026

In 2026, choosing between AI and data science is less about hype and more about fit: math depth, deployment expectations, and how much time you can realistically commit each week. In the U.S., the BLS projects 34% growth for data scientist roles (2024 to 2034), so the demand is real, but so is the skills gap. At the same time, many organizations now report regular use of generative AI, which raises the bar on applied ML and production thinking.

How We Selected These Master’s Programs

• Focus on rigorous foundations in statistics, ML, and programming
• Clear outcomes tied to real project work, not only lectures
• Curriculum depth that supports real modeling decisions and evaluation
• Flexibility that works for full-time professionals
• Recognized credentials that carry weight in hiring and internal mobility

Overview: Best Master’s Programs to Compare in 2026

5 Master Programs for Comparing AI and Data Science Tracks in 2026

1. MS in Artificial Intelligence and Machine Learning (Online) – Walsh College (via Great Learning)

Overview
If your goal is an AI-forward role, this MS in Artificial Intelligence track is built around applied ML and modern AI workflows, including GenAI coverage. The structure emphasizes repeated practice through projects and case studies, so you build both modeling skill and implementation confidence.

• Delivery & Duration: Online, 2 years
• Credentials: Master’s degree from Walsh College; 30 semester credit hours listed for the program
• Instructional Quality & Design: Curriculum notes, modules on ChatGPT and Generative AI, plus core foundations like Python and applied statistics
• Support: Mentorship and career support elements, such as mock interviews and alumni connect, are highlighted

Key Outcomes / Strengths

• Build fluency through 12 hands-on projects and 30+ case studies
• Complete a capstone at the end of each year
• Develop practical skill coverage across Python, SQL, applied statistics, deep learning, and GenAI topics

2. Master of Science in Machine Learning and AI – upGrad (LJMU)

Overview
This option works well if you want ML depth with a clear applied pathway. It is centered on real deliverables, and the program page emphasizes a strong practice component, which matters when you are comparing curriculum depth against time.

• Delivery & Duration: Online, 18 months; the page also references 750+ hours of learning
• Credentials: Master’s degree pathway is promoted on the program page (LJMU partnership positioning)
• Instructional Quality & Design: Mentions coverage across deep learning, NLP, and cloud topics, plus newer focus areas like MLOps and GenAI specialization tracks
• Support: Noted cohort style and outcome framing on the page; verify specifics (like career services) directly during enrollment discussions

Key Outcomes / Strengths

• Practice volume is a major theme: case studies and capstone projects are highlighted
• Track options include MLOps and GenAI specializations
• Good fit if you want “production-minded ML” rather than only model theory

3. Master of Data Science (Global) Program – Deakin University (via Great Learning)

Overview
For professionals comparing AI vs DS, this is a strong middle ground: a masters in data science degree pathway with an applied AI and ML build-up, then deeper DS units. It is particularly relevant if you want to strengthen statistics and ML foundations while also getting breadth across real-world analytics domains.

• Delivery & Duration: Online, 12+12 months (pathway year plus master’s year)
• Credentials: Master’s degree pathway under Deakin branding on the program page
• Instructional Quality & Design: Deakin-year units listed include Engineering AI Solutions, Mathematics for Artificial Intelligence, Machine Learning, and Modern Data Science, which signals real curriculum depth beyond surface coverage
• Support: The program emphasizes portfolio-building through projects and structured learning milestones

Key Outcomes / Strengths

• 11 hands-on projects plus 60+ case studies across 22+ domains
• Strong foundation coverage in Python, ML, applied statistics, deployment concepts, and DS workflows
• Useful if you want both modeling rigor and the ability to explain decisions to non-technical stakeholders

4. Online Master of Science (MSc) in Data Science – MAHE (Online Manipal)

Overview
If your constraint is weekly bandwidth, this program’s structure is straightforward: a two-year DS master’s with consistent pacing. The program page highlights a mix of ML, statistics, and applied DS areas, which suits professionals who want steady progress and a clear semester plan.

• Delivery & Duration: Online, 24 months (4 semesters)
• Credentials: Master of Science (MSc) in Data Science from MAHE (as positioned on the program page)
• Instructional Quality & Design: Coverage includes ML, big data analytics, statistics, data visualization, and computer vision
• Support: Learner support, recorded classes, and mentor help are referenced through program content and testimonials

Key Outcomes / Strengths

• Clear DS foundation stack: stats, plus ML, plus analytics tooling
• Built for working professionals who need flexibility without losing structure

5. Online MSc in Data Science – Amity Online

Overview
This option is best for professionals who want a clean DS curriculum with a support layer around career readiness. It is less about niche AI tracks and more about building reliable DS competency that you can apply across business problems.

• Delivery & Duration: Online, 2 years
• Credentials: Online MSc in Data Science (as stated on the program page)
• Instructional Quality & Design: Program positioning centers on DS fundamentals and practical skill-building
• Support: The page references structured support services tied to employability and learner assistance

Key Outcomes / Strengths

• Good for professionals who want a predictable DS learning path with institutional packaging
• Works as a practical stepping stone into analytics, DS, and adjacent roles where modeling fundamentals matter

Final Thoughts

When you compare AI and data science tracks honestly, the deciding factor is usually not ambition. It is whether you want to spend more time on model building and deployment patterns, or on broader data workflows, experimentation, and decision support.

If your goal leans toward AI-heavy roles, prioritize programs that repeatedly force you to ship projects and defend modeling choices, not just complete quizzes. If your end goal is an MSc in artificial intelligence, the best choice is a program that fits your weekly bandwidth and still gives you enough reps to produce a portfolio you can stand behind.