Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor of Elec. Eng. and Computer Science
Data Science Ethics
What You'll Learn
- Apply an ethical framework to decisions about collecting, analyzing, and using data.
- Evaluate issues involving informed consent, data ownership, privacy, and anonymity.
- Identify how data quality, model design, and analytical choices can create misleading or harmful results.
- Analyze algorithmic bias, fairness, surveillance, and other societal consequences of data-driven systems.
10 Modules
20 Hours
2 hrs per module (approx.)
About Data Science Ethics
"Data Science Ethics" is a beginner-friendly online course that examines the ethical responsibilities involved in collecting, analyzing, sharing, and using data. You will learn a practical framework for evaluating data-driven decisions and considering their effects on individuals, organizations, and society.
The course explores informed consent, data ownership, privacy, anonymity, data validity, algorithmic bias and fairness, surveillance, predictive policing, and facial recognition. Through real-world case studies, you will examine how choices about data and algorithms can produce unintended consequences—even when the analysis appears technically correct.
You will also examine how ethical principles can inform professional standards and evaluate codes of ethics. No previous data-science or programming experience is required. The course is taught by University of Michigan computer science professor H. V. Jagadish and is relevant to data professionals, analysts, technologists, policymakers, students, and anyone affected by data-driven decisions.
Skills You'll Gain
- Data Analysis
- Data Ethics
- Data Quality Assessment
- Policy Analysis
- Risk Management
What You'll Earn
- Certificate of Completion
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Data Science Ethics establishes a shared ethical foundation using a utilitarian framework to evaluate right and wrong in data-driven decision making. Learners examine informed consent, data ownership, privacy, algorithmic fairness, and societal consequences of data science, culminating in the creation and evaluation of professional codes of ethics.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: What Are Ethics?
- Exploring Ethics
- Data Science Ethics
Module 2: History, Concept of Informed Consent
- Human Subjects Research and Informed Consent
- Limitations of Informed Consent
Module 3: Data Ownership
- Introduction to Data Ownership
- Limits on Recording and Use
Module 4: Privacy
- Introduction to Privacy
- History of Privacy
- Degrees of Privacy
- Modern Privacy Risks
Module 5: Anonymity
- Introduction to Anonymity
- De-identification Has Limited Value
Module 6: Data Validity
- Introduction to Validity
- Choice of Attributes and Measures
- Errors in Data Processing
- Errors in Model Design
- Managing Change
Module 7: Algorithmic Fairness
- Introduction to Algorithmic Fairness
- Correct But Misleading Results
- P Hacking
Module 8: Societal Consequences
- Introduction to Societal Impact
- Ossification
- Surveillance
Module 9: Code of Ethics
- The Need for a Code of Ethics
Grading Policy
To pass this course and earn the certificate, learners must achieve an overall course grade of 80% or higher. Module review quizzes are worth 70% of the final grade, and a case study is worth 30%.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
No prior experience required
