An applied Swiss-inspired curriculum combining algorithmic rigour, systems architecture, digital trust and practical engineering capability for modern technology leaders.
The Executive Doctorate in Data Science Leadership 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 |
|---|---|---|---|
| DOC101 | Philosophy of Technical Sciences & Epistemology in Data Science Leadership | Theory | Examine computational ontology, paradigm shifts, falsificationism and scientific epistemology in Data Science Leadership. |
| DOC102 | Doctoral Research Methodology & Design Science Research in Data Science Leadership | Theory | Formulate Hevner's Design Science Research guidelines, rigor vs relevance cycles and doctoral artifact design in Data Science Leadership. |
| DOC103 | Empirical Research Formulation & Quantitative Inquiry in Data Science Leadership | Practical | Design formal empirical investigations, controlled computational experiments and statistical hypothesis test suites in Data Science Leadership. |
| DOC104 | Theoretical Frameworks & Advanced Systems Modeling in Data Science Leadership | Theory | Deconstruct axiomatic systems models, state transition systems and formal verification in Data Science Leadership. |
| DOC105 | Computational Experimentation & Statistical Verification in Data Science Leadership | Practical | Execute Monte Carlo simulations, power calculations and non-parametric statistical tests in Data Science Leadership using R and Python. |
| DOC106 | Critical Literature Synthesis & State-of-the-Art Analysis in Data Science Leadership | Theory | Perform systematic literature reviews (PRISMA), meta-analyses and theoretical gap identification in Data Science Leadership. |
| DOC107 | Systems Epistemology & Architectural Thinking in Data Science Leadership | Theory | Evaluate socio-technical systems theory, emergence in complex adaptive architectures and structural decoupling in Data Science Leadership. |
| DOC108 | Research Ethics, Responsible Innovation & Swiss Standards in Data Science Leadership | Practical | Audit doctoral research protocols against Swiss human research ethics, algorithmic safety and data governance in Data Science Leadership. |
| DOC109 | Doctoral Research Scoping & Initial Proposal Portfolio in Data Science Leadership | Theory | Author the preliminary doctoral scoping portfolio defining novel research questions, significance and scope in Data Science Leadership. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DOC201 | Advanced Doctoral Proposal Defense & Technical Roadmap in Data Science Leadership | Theory | Defend the comprehensive doctoral research proposal, methodology and experimental roadmap in Data Science Leadership before a faculty panel. |
| DOC202 | Empirical Prototyping & Research Artifact Engineering in Data Science Leadership | Practical | Construct the foundational computational artifact, reference architecture and experimental software testbed in Data Science Leadership. |
| DOC203 | Advanced Statistical Learning & Model Evaluation in Data Science Leadership | Theory | Derive generalization bounds, cross-validation asymptotics and high-dimensional estimation theory in Data Science Leadership. |
| DOC204 | Field Experimentation & Data Collection Methodology in Data Science Leadership | Practical | Deploy telemetry collectors, empirical logging probes and instrumentation across production testbeds in Data Science Leadership. |
| DOC205 | Enterprise Systems Integration & Sociotechnical Impact Analysis in Data Science Leadership | Theory | Analyze organizational adoption dynamics, human-in-the-loop interfaces and sociotechnical integration barriers in Data Science Leadership. |
| DOC206 | Intellectual Property, Open Research & Technical Dissemination in Data Science Leadership | Practical | Manage doctoral software patent disclosures, dual-licensing models and open-source scientific repository governance in Data Science Leadership. |
| DOC207 | Technology Innovation Strategy & Industrial Research Collaboration in Data Science Leadership | Theory | Structure university-industry research partnerships, industrial consortium agreements and technology transfer in Data Science Leadership. |
| DOC208 | Mid-Candidature Doctoral Milestone & Progress Review in Data Science Leadership | Theory | Successfully pass the comprehensive mid-candidature doctoral review demonstrating substantial empirical progress in Data Science Leadership. |
| DOC209 | Applied Research Colloquium & Working Paper Series in Data Science Leadership | Practical | Present working research papers, collect expert peer critique and author academic pre-prints in Data Science Leadership. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DOC301 | Doctoral Fieldwork & Experimental Artifact Evaluation in Data Science Leadership | Practical | Conduct exhaustive empirical fieldwork and evaluate the research artifact across multi-organization deployments in Data Science Leadership. |
| DOC302 | Comprehensive Data Analysis & Computational Verification in Data Science Leadership | Practical | Perform advanced statistical triangulation, sensitivity analysis and algorithmic verification of empirical data in Data Science Leadership. |
| DOC303 | Doctoral Dissertation Drafting: Architectural & Empirical Synthesis in Data Science Leadership | Theory | Draft dissertation core chapters: theoretical foundation, artifact architecture and empirical findings in Data Science Leadership. |
| DOC304 | Doctoral Dissertation Drafting: Results Validation & Benchmarking in Data Science Leadership | Practical | Author dissertation comparative benchmark chapters validating novel contributions against state-of-the-art baselines in Data Science Leadership. |
| DOC305 | Academic Journal Manuscript & Peer Review Preparation in Data Science Leadership | Theory | Prepare high-impact peer-reviewed journal submissions conforming to top-tier ACM/IEEE editorial standards in Data Science Leadership. |
| DOC306 | Industry Technology Transfer & Executive Briefing in Data Science Leadership | Practical | Synthesize academic findings into actionable executive advisories and commercial technology transfer briefs in Data Science Leadership. |
| DOC307 | Final Doctoral Dissertation Submission & Quality Audit in Data Science Leadership | Practical | Complete the full academic dissertation manuscript and pass formal institutional originality audits in Data Science Leadership. |
| DOC308 | Doctoral Oral Defense (Viva Voce) Preparation in Data Science Leadership | Theory | Master academic rhetorical defense, anticipate committee objections and articulate unique scholarly contributions in Data Science Leadership. |
| DOC309 | Final Executive Doctoral Defense & Research Dissemination in Data Science Leadership | Practical | Successfully defend the doctoral dissertation in public viva voce examination and disseminate scholarly findings in Data Science Leadership. |
Master’s degree in computing or related quantitative discipline with significant technology leadership experience. Research concept proposal 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.