U of M flowers

MS in Data Science

The M.S. in Data Science for Chemical Engineering and Materials Science degree bridges disciplinary expertise in chemical engineering and materials science with data and computational science. It aims to educate the next generation of chemical engineers and materials scientists that will be able to work seamlessly with digital technologies.

The program core provides fundamental knowledge of statistical and data analysis, machine learning, and optimization, as well as their application in chemical, biological, and materials science and engineering problems. Elective courses allow students to specialize in artificial intelligence, high performance computing, systems engineering, automation and robotics, or data analytics, depending on their specific interests and needs. Students have the option to complete a capstone project under the supervision of CEMS faculty, possibly in collaboration with faculty from other departments or industry advisors.

FEATURES:

• The program is offered in coordination with the Department of Computer Science and Engineering and the School of Statistics.

• First joint Chemical Engineering and Materials Science and Engineering degree offered by CEMS.

• Provides interdisciplinary training and skills that are highly sought after in the chemical, biotechnology, and materials industries.

• Capstone project option provides an opportunity to work with world-class CEMS faculty on cutting-edge projects drawn from academic research and/or industrial practice.

PROGRAM CURRICULUM:

The Master of Science in Data Science for Chemical Engineering & Materials Science is a 30 credit program, which can be completed in one academic year if full-time.

• 9 credits of Chemical Engineering & Materials Science (ChEn or MatS) computational courses

• 9 credits (minimum) from Computer Science & Engineering (CSci) or Statistics (Stats)

The remaining credits are from elective courses spanning the subjects of artificial intelligence, automation and robotics, data analysis, business analytics, and control and optimization.

Kacey Gregerson headshot

Kacey Gregerson

Academic Advisor

kgregers@umn.edu
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Prodromos Daoutidis

Director of M.S. in Data Science

daout001@umn.edu