Why does this program exist?
To develop people who can connect data, technology and domain knowledge—from framing a problem and preparing data to building AI, evaluating results and communicating for decisions.
About DIAI
Turn data and AI into systems that create meaningful outcomes.
DIAI is a 36-credit Master of Science program from the Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi.
It integrates Data Science, Artificial Intelligence, Data Engineering, applied mathematics, statistics, research and domain knowledge for contextual, real-world problems.
- Award
- Master of Science
- Credits
- 36
- Learning model
- Hybrid + OBE
- Completion plans
- Thesis / Independent study
Learning by design
How we carefully connect the curriculum with real-world problems
How is it different from short tool training?
Graduate study covers principles, research methods, experimentation, reliability, ethics and system integration—not knowledge tied to one software package or model generation.
How will I learn?
Through hybrid, case-based, project-based and outcome-based learning. Learners explain decisions, build, evaluate limitations and improve work through feedback.
Who is it for?
Graduates and professionals from computing, mathematics, statistics, science, engineering, business, finance or other fields who want to work seriously with data and AI.
What preparation helps?
Logical thinking, willingness to program, and readiness to strengthen mathematics and statistics are valuable. The amount of preparation depends on prior experience and pathway.
What should graduates be able to do?
Frame problems, choose suitable data and methods, develop analytics or AI systems, evaluate with evidence, communicate across roles and work with ethical and societal awareness.
Program design principles
Grounded in principles. Ready for changing tools.
Systematic thinking
See the full data lifecycle and the relationship between technology, people, process and impact.
Theory connected to practice
Use principles to explain why a method is suitable, rather than only following a tool workflow.
Evidence before confidence
Use baselines, experiments, validation and error analysis to support conclusions.
Responsible data and AI
Consider privacy, governance, transparency, fairness and appropriate limits of use.
Rigorous by design
Flexible for professionals. Rigorous enough for meaningful growth.
Working-professional scheduling improves access, while quality still depends on reading, experimentation, coding, collaboration and sustained project development.
- The illustrative Tuesday–Thursday evening and full-Saturday rhythm must be confirmed against each semester’s official schedule.
- Not every elective is offered in every semester.
- Learners may need different levels of preparation in programming, mathematics or statistics.
- Thesis and independent study require a defensible scope, evidence, evaluation and communication.
Capabilities and career directions
Graduate with capabilities that travel across roles
Roles depend on prior experience, portfolio and chosen depth. Directions aligned with the curriculum include:
Analytics & Decision
Data Analyst, Business Intelligence and decision-support roles.
AI & Applied Research
Data Scientist, Applied AI Researcher and evidence-led experimentation roles.
Data Systems
Data Engineer, AI Solution Developer and production-oriented data-system roles.
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