ETHICS OF DATA FOR ENGINEERS

Prerequisites & course information

Requisites: ( I SY E 521 , 562 , M E/COMP SCI/E C E 532 , or 539 ) and ( E C E 331 , MATH/STAT 309 , STAT 311 , MATH 331 , or STAT/MATH 431 ), or graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2025 Learning Outcomes: 1. Describe the importance of ethical data science/engineering Audience: Both Grad & Undergrad 2. Identify challenges of trustworthy data use in engineering such as fairness, privacy, and robustness Audience: Both Grad & Undergrad 3. Apply the definitions of trustworthy data engineering to real-world datasets Audience: Both Grad & Undergrad 4. Analyze the data analysis pipelines and evaluate the trustworthiness of their outcomes Audience: Both Grad & Undergrad 5. Create proper data analysis pipelines with ethical considerations Audience: Both Grad & Undergrad 6. Implement cutting-edge techniques to enhance the fairness, privacy, and robustness of data analysis processes Audience: Graduate 7. Conduct independent research on emerging challenges in ethical data engineering Audience: Graduate Learning Outcomes: 1. Describe the importance of ethical data science/engineering Audience: Both Grad & Undergrad 2. Identify challenges of trustworthy data use in engineering such as fairness, privacy, and robustness Audience: Both Grad & Undergrad 3. Apply the definitions of trustworthy data engineering to real-world datasets Audience: Both Grad & Undergrad 4. Analyze the data analysis pipelines and evaluate the trustworthiness of their outcomes Audience: Both Grad & Undergrad 5. Create proper data analysis pipelines with ethical considerations Audience: Both Grad & Undergrad 6. Implement cutting-edge techniques to enhance the fairness, privacy, and robustness of data analysis processes Audience: Graduate 7. Conduct independent research on emerging challenges in ethical data engineering Audience: Graduate