img

Woodcroft University was founded with a mission to make high-quality education accessible to learners everywhere. Built for the digital era, Woodcroft embraces innovation, flexibility, and global connection, offering students a modern academic experience grounded in excellence. Through immersive online learning, industry-aligned programs, and a diverse international community, Woodcroft continues to empower learners to grow, advance, and create meaningful impact in their fields. 

7901 4th St N, Suite 300,
St. Petersburg, FL 33702, USA info@woodcroftedu.us
Address

7901 4th St N, Suite 300,
St. Petersburg, FL 33702, USA

Email

info@woodcroftedu.us

Phone Number

+1 850-312-3081

Get More Info icon
Doctoral Program • EQF Level 8 3 Years (Full-Time) 4-Phase Structured Progression US / UK / EU Framework Aligned

PhD in Data Science — 3-Year Doctoral Syllabus (Four Phases)

A research-intensive doctoral curriculum aligned to major global doctoral standards: United States (structured PhD: coursework → qualifying → dissertation → defense), United Kingdom (doctoral training + upgrade/MPhil-to-PhD style confirmation + viva), and European Union (Bologna third-cycle doctorate at EQF Level 8).

Mode
Research-Intensive (Taught + Dissertation)
Assessment Model
Coursework, Qualifying/Upgrade, Dissertation, Viva/Defense
Research Output Expectation
Publishable-quality research contribution
Typical Structure
Months 1–9 • 10–15 • 16–30 • 31–36
US / UK / EU Doctoral Framework Alignment
Equivalencies shown for governance, progression, and award requirements.
Aligned to doctoral milestones
Framework Key Milestones How This 3-Year Program Maps
United States Coursework • Qualifying/Comprehensive Exam • Dissertation • Defense Phase I (coursework) → Phase II (qualifying + proposal) → Phase III (dissertation research) → Phase IV (defense)
United Kingdom Research Training • Upgrade/Confirmation • Thesis • Viva Voce Phase I (training) → Phase II (upgrade-style confirmation) → Phase III (main research) → Phase IV (viva + corrections)
European Union Bologna Third Cycle • Doctoral Training • Research • Defense (EQF Level 8) Phase I–II (training + candidacy) → Phase III (independent research) → Phase IV (defense + dissemination)
Note: This is a 3-year full-time structure. Extensions, part-time delivery, and resubmission rules should follow institutional doctoral regulations.
Doctoral Governance & Supervision
Standard doctoral oversight used across US/UK/EU institutions.
Committee-based review
  • Primary Supervisor: research direction, integrity, and progress monitoring.
  • Co-Supervisor (optional): domain and methods support.
  • Doctoral Review Committee: candidacy, progress, and defense eligibility.
  • Progress Reviews: scheduled milestone reporting (typically quarterly in Phase III).
  • Research Integrity: ethics, governance, reproducibility, originality.
Minimum Academic Standards (Program-Wide)
  • Research must demonstrate original contribution to Data Science.
  • Methods must be reproducible (versioned code + experiment logs).
  • Compliance with responsible AI, privacy, and ethics requirements.
  • Candidacy confirmation required before dissertation phase.
Four-Phase Program Structure (3 Years)
Detailed phase design, deliverables, and assessments (Months 1–36).
4 phases • 36 months
I
Phase I: Advanced Doctoral Coursework & Foundations
Months 1–9 US: Year-1 Coursework UK: Taught Doctoral Training EU: Doctoral Training

Establish advanced theoretical, mathematical, and computational depth for doctoral research. Emphasis is placed on learning theory, probabilistic modeling, reproducible research engineering, and research-grade evaluation.

Module Group A: Mathematical & Statistical Foundations
  • Linear algebra (spectral methods), multivariate calculus, optimization basics
  • Probability theory, stochastic processes, concentration inequalities (intro)
  • Bayesian inference, time series foundations, high-dimensional statistics (intro)
Module Group B: Machine Learning & Deep Learning (Advanced)
  • Learning theory (generalization), regularization, robustness, interpretability
  • Deep architectures (CNN/RNN/Transformers), representation learning
  • Experiment design, evaluation protocols, ablation studies
Module Group C: Data Systems & Research Engineering
  • Distributed processing concepts; scalable pipelines; experiment tracking
  • Reproducible research workflows; versioned artifacts; documentation
  • Scientific computing (Python/R); algorithmic complexity basics
Doctoral Research Skills (Mandatory)
  • Systematic literature review; gap identification; research questions
  • Academic writing & referencing; critical reading and synthesis
  • Research ethics, data governance, responsible AI
Phase I — Exit Requirements
  • Completion of coursework assessments (exams/projects/research memos).
  • Approved research area selection and initial direction statement.
  • Submission of a systematic literature review (mapping + annotated bibliography).
II
Phase II: Research Methodology & Qualifying / Upgrade Confirmation
Months 10–15 US: Qualifying/Comprehensive Exam UK: Upgrade/Confirmation EU: Candidacy Confirmation

Validate doctoral readiness through advanced research methodology, responsible AI compliance, and a defensible dissertation proposal. Confirmation is awarded via written examination and oral proposal defense.

Research Design & Causal Inference
  • Experimental vs observational designs; threats to validity
  • Causal graphs and estimation strategies
  • Simulation design and power considerations
Responsible AI, Privacy & Governance
  • Bias, fairness, transparency, accountability, auditability
  • Privacy-preserving analytics (overview)
  • Governance: documentation, approvals, compliance
Optimization & Intelligent Systems (Advanced)
  • Convex optimization foundations; constrained methods
  • Reinforcement learning foundations and evaluation
  • Multi-objective optimization; evolutionary computation (overview)
Scholarly Communication & Proposal Craft
  • Publishing pipeline, positioning contributions, peer review
  • Research impact metrics; dissemination strategies
  • Proposal feasibility, timelines, and risk mitigation
Qualifying / Upgrade Examination (Mandatory)
  • Written Component: theory + methods + specialization exam.
  • Proposal Defense: oral presentation + committee questioning.
  • Outcome: Pass / Pass with revisions / Resubmission required.
  • Result: Formal doctoral candidacy status upon approval.
III
Phase III: Dissertation Research & Development
Months 16–30 US: Dissertation Research UK: Main Research Phase EU: Independent Research

Execute original research to generate publishable knowledge. Focus includes dataset strategy, experimentation, algorithmic innovation, robust evaluation, reproducibility artifacts, and dissertation chapter drafting.

Research Execution (Core)
  • Dataset strategy: sourcing/collection, governance, documentation
  • Experiment design: baselines, ablations, robustness
  • Reproducibility pack: versioning, environments, logs
Evaluation & Validation (Doctoral Standard)
  • Metrics, confidence, significance, and error analysis
  • Benchmarking with fair comparisons and constraints
  • Interpretability, bias testing, and safety (as applicable)
Writing & Dissemination (Recommended)
  • At least one submission-ready manuscript (journal/conference)
  • Seminars, presentations, and peer feedback loops
  • Thesis chapters drafted alongside experiments
Periodic Progress Reviews
  • Quarterly committee check-ins (or per policy)
  • Milestone reports: results, risks, next plan
  • Ethics/data governance revalidation if scope changes
Phase III — Exit Requirements
  • Committee confirmation of a substantive original contribution.
  • Dissertation draft completion (full structure + results chapters).
  • Pre-submission checks: reproducibility pack + originality scan.
IV
Phase IV: Thesis Submission, Viva/Defense & Dissemination
Months 31–36 US: Dissertation Defense UK: Viva Voce EU: Doctoral Defense

Final submission, oral examination, and post-defense corrections culminating in the award of the PhD. Focus is on contribution, methodological rigor, defensible conclusions, and dissemination.

Thesis Submission (Pre-Defense)
  • Format compliance and dissertation checklist verification
  • Originality verification and citation audit
  • Artifact submission: code, experiment logs, appendices
Viva / Defense (Oral Examination)
  • Contribution statement: novelty, impact, limitations
  • Method defense: assumptions, design choices, validity
  • Results defense: evidence, generalization, robustness
Corrections & Final Approval
  • Minor or major corrections (per examiner decision)
  • Final committee sign-off and archival submission
  • Completion verification and award processing
Dissemination (Expected)
  • Conference/journal submission or publication plan
  • Research seminar or public colloquium presentation
  • Knowledge transfer statement (academic/industry relevance)
Program Completion Criteria
  • Accepted dissertation submission and successful viva/defense outcome.
  • All corrections completed within examiner timelines.
  • Final archival submission (thesis + required artifacts).
Program Learning Outcomes (Doctoral / EQF Level 8)
Expected capabilities demonstrated on completion.
Research leadership
  • Design and execute independent research with original contribution to Data Science.
  • Demonstrate advanced mastery of statistical, computational, and AI methods.
  • Apply rigorous evaluation: validity, robustness, reproducibility, and transparency.
  • Produce publication-grade outputs and communicate results to expert audiences.
  • Operate within responsible AI, privacy, and ethical research requirements.
Career & Research Pathways
Indicative roles aligned with doctoral-level expertise.
Academia + Industry
  • University Faculty / Research Fellow / Postdoctoral Researcher
  • Research Scientist (AI / ML / Data Science)
  • Principal Data Scientist / Applied Research Lead
  • AI Governance, Ethics & Policy Specialist
  • Innovation Lab Lead / Advanced Analytics Consultant
Doctoral outcomes depend on candidate performance, research quality, and institutional examination/award regulations.