HUMAN FACTORS OF DATA SCIENCE AND MACHINE LEARNING
An examination of the "human side" of data science. Issues of bias, fairness, trust, and understandability. Unique characteristics of behavioral data, such as representative sampling, human adaptation, and grouped data. Practical skills in behavioral data analytics with a focus on important conceptual, design, and ethical issues specific to behavioral data. Survey of machine learning techniques including supervised learning, unsupervised learning, reinforcement learning, deep learning, and text analysis. Methods are contextualized through engineering case studies.
Prerequisites & course information
Requisites: ( I SY E 210 , B M E 325 , E C E 331 , MATH/STAT 310 , 410, STAT 312 , or 340 ), graduate/professional standing, or member of Engineering Guest Students Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Consider human values and behavior in developing and using machine learning models Audience: Both Grad & Undergrad 2. Apply a human-centered design process to data and algorithm-intensive products Audience: Both Grad & Undergrad 3. Describe the basic elements of machine learning and their limits Audience: Both Grad & Undergrad 4. Analyze a case study of machine learning deployed by a company and discuss strengths and limits of the deployment using the concepts of the course. Audience: Graduate Learning Outcomes: 1. Consider human values and behavior in developing and using machine learning models Audience: Both Grad & Undergrad 2. Apply a human-centered design process to data and algorithm-intensive products Audience: Both Grad & Undergrad 3. Describe the basic elements of machine learning and their limits Audience: Both Grad & Undergrad 4. Analyze a case study of machine learning deployed by a company and discuss strengths and limits of the deployment using the concepts of the course. Audience: Graduate
Sources
- Course catalogue ↗2026-2027