CSCI 4730 - Material Informatics and Data Science

Introduction to data science and machine learning, with case studies in discovery of structure-property relationships and new materials from experimental and computational data. Brief review of required background in linear algebra and statistics with hands-on exercises in Python. Data science topics: model fitting, clustering, dimensionality reduction, ontologies, Bayesian inference, and design of experiments.

Prerequisite: ENGR 2600; or equivalent

When Offered: FALL TERM, EVEN YEARS

Cross Listed: MTLE 4730 and MTLE 6730. Students can only earn credit for one of these courses.

Co-Listed: CSCI 6730

Graded: GRADED

Credit Hours: 3