Applications in Data and Materials Science

Description from DukeHub

AI principles will be applied to a series of materials science example problems, each taught in a module by an expert in materials science or data science. Each module will span 2-3 weeks, demonstrating an array of data science/AI methods in unique materials case studies in advancing discovery or design principles. Prerequisites: ME 221 or equivalent, introductory machine learning course.

Skills

• programmingdepartment: COMPSCI
• data and statisticsdescription: "data science" and 1 more
• ai and machine learningdescription: "ai" and 2 more

AI-resilient: 0 of 5 0/5

• No: Physical, lab or field work
• No: Live work with people
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

More in COMPSCI

• Introduction to Computer Science COMPSCI 101L
• Data Structures and Algorithms COMPSCI 201
• Introduction to Computer Systems COMPSCI 210D
• Everything Data COMPSCI 216
• Discrete Math for Computer Science COMPSCI 230
• Computer Architecture COMPSCI 250D

All COMPSCI courses · All of Graduate School