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HUMAN FACTORS OF DATA SCIENCE AND MACHINE LEARNING

ISYE 562
Course Description

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.

Prerequisties

(ISYE 210 , ECE 331 , MATH/STAT 310 , STAT 312 , or STAT 340 ), graduate/professional standing, or member of Engineering Guest Students

Satisfies
Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.72

-0.8% from Historical

Completion Rate
96%

-2.72% from Historical

A Rate
72%

3.25% from Historical

Class Size
25

-34.21% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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