Required 15 · Electives 9 · Thesis 12
Designed for deeper research and knowledge creation.Curriculum architecture
A shared foundation.
A direction you can shape.
The 36-credit curriculum establishes a shared foundation in research, mathematics, data workflow, visualisation and machine learning before opening electives across business, AI systems, data engineering and computational science.
Required 15 · Electives 15 · Independent study 6
Designed for applied work on a system or real-context problem.Required foundation
A 15-credit core: from real questions to evaluated AI systems
One-credit modules develop focused capabilities that connect into a larger end-to-end workflow.
DIA 601
Research Methodology
2 credits
DIA 602
Seminar in Data Innovation and Artificial Intelligence
1 credit
DIA 603
Mathematics and Statistics Foundation for Artificial Intelligence
3 credits
DIA 60401
Data Acquisition and Preparation
1 credit
DIA 60402
Data Exploration and Visualization
1 credit
DIA 60403
Data Analytics and Predictive Modeling
1 credit
DIA 60501
Design Psychology and Information Theory
1 credit
DIA 60502
Advanced Visualization
1 credit
DIA 60503
Strategic Data Storytelling
1 credit
DIA 60601
Foundations of Machine Learning
1 credit
DIA 60602
Model Evaluation and Improvement
1 credit
DIA 60603
Artificial Intelligence in Practice
1 credit
Elective clusters
Choose depth around the problems you want to solve
Actual offerings depend on the study plan, enrolment and semester availability. This list presents the curriculum’s approved subject scope.
Business and Digital Transformation
DIA 611Information Retrieval and Knowledge ManagementRetrieve, structure and reuse organisational knowledge systematically.
DIA 61201Data Architecture and Metrics DesignDesign data models, metrics and semantic layers around shared definitions.
DIA 61202Diagnostic Business AnalyticsDiagnose business outcomes and distinguish signals from symptoms.
DIA 61203Data Strategy and GovernancePlan ownership, quality, access and responsible use of data.
DIA 613Time Series Analysis and ForecastingForecast time-dependent data while accounting for seasonality and uncertainty.
DIA 614Applied Multivariate Statistical AnalysisAnalyse multivariate relationships in complex datasets.
DIA 615Optimization in Finance and EngineeringApply optimisation to allocation, risk and cost decisions.
DIA 616Blockchain TechnologyUnderstand distributed-ledger architecture and evaluate appropriate use cases.
AI Systems and Automation
DIA 62101Data Architecture and Pipeline ProcessingDesign data architecture and pipelines from source to consumer.
DIA 62102Automation with DevOps ToolsAutomate repeatable build, test and deployment workflows with DevOps.
DIA 62103CI/CD Pipelines and Data OperationsOperate data systems with CI/CD, monitoring and operational controls.
DIA 622Deep LearningBuild and evaluate neural networks for suitable data and problems.
DIA 623Computer Vision and Video AnalyticsDevelop image and video analytics while auditing perception errors.
DIA 624Natural Language Processing and Text MiningTurn language into analysable representations for classification and discovery.
DIA 625Big Data AnalyticsProcess large-scale data with attention to scale, latency and resources.
DIA 626Advanced Analytics and Cloud ComputingDesign cloud analytics workloads around performance and cost.
DIA 627Online Intelligent Systems and Recommender SystemsBuild recommender and adaptive systems from evolving behaviour.
Computational Science
DIA 631BioinformaticsApply computation to biological data and biological questions.
DIA 632Computational BiologyModel mechanisms and complex biological systems.
DIA 633CheminformaticsUse chemical structures and properties to support discovery.
DIA 634Computational ChemistrySimulate and analyse chemical systems computationally.
DIA 635Computational PhysicsUse numerical methods and simulation to study physical phenomena.
DIA 636Quantum ComputingExplore quantum information principles and quantum-algorithm thinking.
Illustrative coursework
Work that demonstrates thinking and execution
Briefs and datasets may change by instructor and semester. These examples show possible learning formats, not guaranteed project assignments.
Customer analytics and data storytelling
Prepare data, build segmentation, design visualisations and connect metrics to a decision-ready recommendation.
Evidence-based demand forecasting
Establish a baseline, inspect seasonality, compare models and report uncertainty for operational planning.
Thai-language text classification
Build a labelled dataset, test representations and models, then audit bias, error patterns and limits of use.
An organisation-ready data pipeline
Connect multiple sources with quality checks, orchestration, version control, automated tests and monitoring.
AI prototype for a scientific problem
Translate a domain problem into a computational task, choose data and evaluation, build a prototype and define its applicability domain.
Independent study or thesis
Develop a real question or problem, plan and execute the work, analyse results and communicate them ethically and defensibly.
Study alongside work
An illustrative after-work timetable
An illustrative pattern uses Tuesday and Thursday evenings plus a full Saturday for workshops, studios or projects. Actual schedules may differ by course and official announcement.
Plan beyond contact hours
Beyond class meetings, allow time for reading, data preparation, coding, teamwork, advisor meetings and thesis or independent-study development. Actual workload varies by course and prior preparation.
Capabilities the program develops
From analysis to responsible, communicable systems
Frame problems systematically
Connect context, objectives, data and success criteria.
Choose methods with reason
Evaluate data, assumptions, baselines and methodological trade-offs.
Build and evaluate
Develop prototypes or systems with defensible experiments and evidence.
Communicate across roles
Explain results appropriately to specialists, leaders and stakeholders.
Use data and AI responsibly
Consider ethics, privacy, transparency and impact.
Collaborate through delivery
Plan, share roles, assure quality and improve through feedback.
Before applying