Data Science, Analytics and Engineering (Human-Centered Applications), MS

Program Overview

This program in Data Science with a concentration in Human Systems Engineering combines data science skills with an understanding of human systems. It offers courses in human factors engineering, cognitive science, and data analytics, preparing students for careers in data science, human factors engineering, and related fields. The program emphasizes interdisciplinary collaboration and addresses ethical considerations in AI and data science.

Program Outline

Outline:

Concentration:

• Human Systems Engineering (HSE)
• Methods and Tools in Applied Cognitive Science (3 credit hours)
• Intermediate Statistics for Human Systems Engineering (3 credit hours)
• Data Analytics: Modeling Human Subjects Data (3 credit hours)
• Foundations of Human Systems Engineering (3 credit hours)

Required Core (9 credit hours):

• Theory of Statistics II: Inference (3 credit hours)
• Probability and Random Processes (3 credit hours) or Statistics for Data Analysts (3 credit hours)
• Data Processing at Scale (3 credit hours)
• Distributed Database Systems (3 credit hours) or Advanced Database Management Systems (3 credit hours)
• One from the following:
• Data Mining (3 credit hours)
• Statistical Machine Learning (3 credit hours)
• Statistical Machine Learning: From Theory to Practice (3 credit hours)
• Statistical Learning for Data Mining (3 credit hours)
• Analyzing Big Data (3 credit hours)
• Advanced Big Data Analytics/AI (3 credit hours)
• Applied Machine Learning for Mechanical Engineers (3 credit hours)
• Statistical Machine Learning (3 credit hours)

Electives (6 or 9 credit hours):

• Students can choose additional courses to complete the program requirements.

Culminating Experience (3 or 6 credit hours):

• Students can choose between a data science capstone project (3 credit hours) or a thesis (6 credit hours).

Careers:

• Data scientists
• Clinical data managers
• Computer and information research scientists
• Human factors engineers and ergonomists
• Statisticians

Other:

• The program focuses on developing data science skills with an understanding of human systems.
• The program features interdisciplinary courses where students can work with colleagues to solve client-driven data science problems.
• The program aims to address issues like biases in machine learning, human feedback integration, ethical considerations, and AI's role in supporting human weaknesses.