An applied Swiss-inspired curriculum combining algorithmic rigour, systems architecture, digital trust and practical engineering capability for modern technology leaders.
The DBA in Artificial Intelligence Strategy at Brookfield School of Computing is engineered to provide deep competency across core computer science, systems design, and targeted domain applications. Students develop fluency in software craftsmanship, distributed architectures, algorithm optimization, and data governance.
Instruction balances rigorous theoretical foundations with practical code repositories, laboratory simulations, and ethical technology review aligned with modern international computing standards.
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DBA101 | Epistemology & Philosophy of Applied Business Research in Artificial Intelligence Strategy | Theory | Examine positivism, constructivism, critical realism and pragmatic epistemology in executive business research in Artificial Intelligence Strategy. |
| DBA102 | Strategic Leadership, Governance & Corporate Direction in Artificial Intelligence Strategy | Theory | Formulate executive governance models, boardroom strategic alignment and C-suite technology oversight in Artificial Intelligence Strategy. |
| DBA103 | Advanced Qualitative Research Design & Executive Interview Lab in Artificial Intelligence Strategy | Practical | Design semi-structured executive interview protocols, ethical consent mechanisms and qualitative sampling matrices in Artificial Intelligence Strategy. |
| DBA104 | Quantitative Methodologies & Econometrics in Artificial Intelligence Strategy | Theory | Derive multiple linear regression, panel data estimators, structural equation modeling (SEM) and factor analysis in Artificial Intelligence Strategy. |
| DBA105 | Applied Statistical Modeling & Corporate Decision Analytics in Artificial Intelligence Strategy | Practical | Analyze corporate survey and financial data using SPSS, R or SmartPLS to validate measurement models in Artificial Intelligence Strategy. |
| DBA106 | Systematic Literature Review & Conceptual Frameworks in Artificial Intelligence Strategy | Theory | Execute rigorous PRISMA systematic literature reviews and synthesize conceptual frameworks in Artificial Intelligence Strategy. |
| DBA107 | Economics of Digital Innovation & Platform Disruption in Artificial Intelligence Strategy | Theory | Analyze platform economics, multi-sided markets, switching costs, competitive moats and technological disruption in Artificial Intelligence Strategy. |
| DBA108 | Executive Data Analytics & Decision Support Systems in Artificial Intelligence Strategy | Practical | Build executive decision analytics models and corporate scenario simulations to inform strategic resource allocation in Artificial Intelligence Strategy. |
| DBA109 | Ethical Governance & Boardroom Accountability in Artificial Intelligence Strategy | Theory | Evaluate fiduciary responsibilities, stakeholder capitalism, corporate ESG metrics and executive research ethics in Artificial Intelligence Strategy. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DBA201 | Applied Doctoral Research Proposal Defense in Artificial Intelligence Strategy | Theory | Formally defend the doctoral research proposal, problem statement, methodology and contributions before faculty in Artificial Intelligence Strategy. |
| DBA202 | Corporate Fieldwork Pilot Study & Executive Methodology in Artificial Intelligence Strategy | Practical | Conduct preliminary pilot fieldwork, test survey instruments and refine qualitative interview protocols in Artificial Intelligence Strategy. |
| DBA203 | Strategic Risk Management, Compliance & Cyber Resilience in Artificial Intelligence Strategy | Theory | Evaluate enterprise risk management (COSO ERM), operational cyber resilience and corporate liability in Artificial Intelligence Strategy. |
| DBA204 | Survey Instrument Engineering & Psychometric Validation in Artificial Intelligence Strategy | Practical | Validate psychometric survey scales, Cronbach's alpha reliability, convergent validity and discriminant validity in Artificial Intelligence Strategy. |
| DBA205 | Organizational Change, Agility & Technology Adoption in Artificial Intelligence Strategy | Theory | Analyze Technology Acceptance Model (TAM/UTAUT), dynamic capabilities theory and organizational agility in Artificial Intelligence Strategy. |
| DBA206 | Corporate Case Study Development & Qualitative Coding in Artificial Intelligence Strategy | Practical | Execute thematic coding, grounded theory analysis and cross-case synthesis using NVivo / ATLAS.ti software in Artificial Intelligence Strategy. |
| DBA207 | Swiss & International Corporate Governance Regulations in Artificial Intelligence Strategy | Theory | Audit corporate compliance against Swiss Code of Best Practice for Corporate Governance and international standards in Artificial Intelligence Strategy. |
| DBA208 | Mid-Candidature Doctoral Review & Progress Defense in Artificial Intelligence Strategy | Theory | Defend mid-candidature empirical milestones and demonstrate significant primary data collection progress in Artificial Intelligence Strategy. |
| DBA209 | Executive Seminar: Business Impact of Research in Artificial Intelligence Strategy | Practical | Synthesize academic findings into actionable C-suite executive transformation playbooks and board presentations in Artificial Intelligence Strategy. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DBA301 | Applied Doctoral Field Investigation & Data Collection in Artificial Intelligence Strategy | Practical | Execute multi-organization primary data collection gathering quantitative survey metrics and executive interviews in Artificial Intelligence Strategy. |
| DBA302 | Empirical Data Triangulation & Cross-Case Synthesis in Artificial Intelligence Strategy | Practical | Triangulate qualitative findings with quantitative survey data and secondary financial corporate metrics in Artificial Intelligence Strategy. |
| DBA303 | Organizational Intervention & Transformation Analysis in Artificial Intelligence Strategy | Theory | Measure business impact, ROI realization, organizational productivity shifts and strategic performance metrics in Artificial Intelligence Strategy. |
| DBA304 | Doctoral Dissertation Drafting: Methodological Synthesis in Artificial Intelligence Strategy | Practical | Author dissertation chapters detailing research design, data collection rigor and analytical procedures in Artificial Intelligence Strategy. |
| DBA305 | Doctoral Dissertation Drafting: Strategic Implications in Artificial Intelligence Strategy | Theory | Synthesize theoretical implications for business scholarship and actionable strategic guidance for executives in Artificial Intelligence Strategy. |
| DBA306 | Peer Review Workshop & Executive Working Paper Series in Artificial Intelligence Strategy | Practical | Present findings at executive doctoral colloquia, collect peer reviews and prepare executive working papers in Artificial Intelligence Strategy. |
| DBA307 | Final Executive Dissertation Submission & Quality Review in Artificial Intelligence Strategy | Practical | Finalize the complete executive doctoral dissertation manuscript and pass institutional originality review in Artificial Intelligence Strategy. |
| DBA308 | Preparation for Doctoral Viva Voce & Oral Defense in Artificial Intelligence Strategy | Theory | Rehearse doctoral dissertation defense, defend methodological choices and articulate executive contributions in Artificial Intelligence Strategy. |
| DBA309 | Final DBA Dissertation Defense & Executive Dissemination in Artificial Intelligence Strategy | Practical | Successfully defend the DBA dissertation in oral viva voce examination and disseminate executive findings in Artificial Intelligence Strategy. |
Master’s degree in management or technology with senior executive leadership experience. Applied research concept required.
Brookfield School of Computing is an independent private education institution in formation. Academic programmes, entry requirements, fees and awards are subject to institutional review and applicable Swiss legal regulations.