CDEI study plan
CDEI · Give each week a clear focus.
8 weeks · About 7 hours per week
Foundations of Data Ethics & Bias, Fairness, and Equity in Data
Define foundational data ethics concepts (e.g., consent, harm, autonomy, fairness) in your own words
Identify and classify different types of bias (historical, representation, measurement, algorithmic) with real-world examples
Explain how bias enters each stage of the data lifecycle (collection, preparation, modeling, deployment)
Compare at least three fairness metrics (e.g., demographic parity, equalized odds, individual fairness) and their trade-offs
Your checkpoint
A written glossary of 15 key terms with definitions and one example each, plus a bias-type identification table for 5 scenarios.
Ethical Frameworks and Theories & Data Privacy and Confidentiality
Summarize major ethical theories (utilitarianism, deontology, virtue ethics, care ethics, justice theory) and apply them to data scenarios
Distinguish between privacy, confidentiality, and security, and explain their relationships
Analyze privacy harms (e.g., re-identification, inference, surveillance) and mitigation strategies
Evaluate consent models (informed, broad, dynamic) and their limitations in data contexts
Your checkpoint
A case analysis (2–3 pages) applying two ethical frameworks to a privacy dilemma, with a justified recommendation.
Bias, Fairness, and Equity in Data & Data Governance and Stewardship
Design a fairness assessment plan for a hypothetical ML model, including metric selection and trade-off justification
Outline the components of a data governance framework (policies, roles, stewardship, data quality, metadata)
Distinguish data stewardship from data ownership and describe stewardship responsibilities
Map data governance controls to ethical principles (e.g., accountability, transparency)
Your checkpoint
A one-page fairness assessment template and a data governance RACI chart for a sample organization.
Foundations of Data Ethics & Data Privacy and Confidentiality & Bias, Fairness, and Equity in Data
Synthesize foundations, privacy, and bias concepts by analyzing an end-to-end data ethics case
Identify potential ethical failures in a given data project and propose preventive measures
Apply privacy-enhancing technologies (e.g., anonymization, differential privacy) appropriately
Evaluate the effectiveness of bias mitigation techniques (pre-, in-, post-processing)
Your checkpoint
A midterm-style case study report (3–4 pages) with a risk register and mitigation plan, self-graded against a rubric.
Transparency, Explainability, and Accountability & Regulatory and Legal Landscape for Data Ethics
Explain the principles of transparency, explainability, and accountability in data systems
Compare explainability methods (e.g., LIME, SHAP, interpretable models) and their appropriate use
Outline key regulatory frameworks (e.g., GDPR, CCPA, AI Act) and their ethical implications
Analyze the role of accountability mechanisms (audits, impact assessments, whistleblowing)
Your checkpoint
A regulatory comparison table (3 regulations) and a one-page explainability method selection guide.
Applied Data Ethics in AI and Analytics & Data Governance and Stewardship
Apply ethical principles to AI/analytics use cases (e.g., hiring, healthcare, lending)
Conduct an ethical impact assessment for an AI system
Integrate governance mechanisms into AI development lifecycle
Design an accountability structure for an AI project (roles, review boards, documentation)
Your checkpoint
An ethical impact assessment for a proposed AI system, including governance and accountability recommendations.
Foundations of Data Ethics & Ethical Frameworks and Theories & Data Privacy and Confidentiality & Bias, Fairness, and Equity in Data & Data Governance and Stewardship & Transparency, Explainability, and Accountability & Regulatory and Legal Landscape for Data Ethics & Applied Data Ethics in AI and Analytics
Review all domains using practice questions and identify weak areas
Create a personalized summary sheet of key concepts, frameworks, and regulations
Practice applying ethical frameworks to novel scenarios under time constraints
Revisit and reinforce challenging topics from earlier weeks
Your checkpoint
A comprehensive one-page cheat sheet and a log of 20+ practice questions with explanations for missed items.
Foundations of Data Ethics & Ethical Frameworks and Theories & Data Privacy and Confidentiality & Bias, Fairness, and Equity in Data & Data Governance and Stewardship & Transparency, Explainability, and Accountability & Regulatory and Legal Landscape for Data Ethics & Applied Data Ethics in AI and Analytics
Complete a full-length practice exam under timed conditions
Review incorrect answers and fill knowledge gaps
Simulate exam-day logistics and stress management
Finalize a quick-reference guide for last-minute review
Your checkpoint
A scored practice exam with a remediation plan for any domain scoring below 70%.
Milestones
Week 2: Score at least 80% on a self-made quiz covering Domains 1 and 4, and submit the privacy case analysis for self-review against a rubric.
Week 4: Complete the midterm case study report and score at least 75% on a mixed quiz of Domains 1, 3, and 4.
Week 6: Finish the ethical impact assessment and achieve at least 80% on a practice set covering Domains 6, 7, and 8.
Week 8: Pass a full-length practice exam with a score of 80% or higher overall and no domain below 70%.