Post-baccalaureate Certificate in Data Science for Chemical and Materials Engineering

This program addresses the need for upskilling of industry scientists and engineers to be able to respond to the rapidly increasing industry demands for AI and data science competency. Through four courses, this certificate provides essential foundational knowledge in data science concepts and tools and their application to chemical and materials engineering. The program is also open to recent graduates in chemical engineering, materials science, and related disciplines who wish to augment their domain expertise with a data science one. Current graduate students may also participate in the certificate program.

Curriculum

The certificate program is a 12 credit program which can be completed in as little as a single semester. To complete the certificate, students must complete 2 courses from the Department of Chemical Engineering and Materials Science, 1 course from the Department of Computer Science and Engineering, and 1 course from the School of Statistics.

Chemical Engineering and Materials Science (choose 2)

Course CodeCourse NameCreditsAvailability

CHEN/MATS 5801

Optimization in Chemical and Energy Systems Engineering

3 credits

Every Fall

CHEN/MATS 5802

Applied Machine Learning in Chemical Engineering and Materials Science

3 credits

Every Spring

CHEN/MATS 8201

Applied Math

3 credits

Every Fall

CHEN/MATS 8553

Data-Driven Molecular Simulation

3 credits

Periodically

Computer Science (choose 1)

Course Code

Course Name

Credits

Availability

CSCI 5521

Machine Learning Fundamentals

3 credits

Every Fall & Spring

CSCI 5523

Intro to Data Mining

3 credits

Periodic Fall & Spring

CSCI 5707

Principles of Database Systems

3 credits

Every Fall

Statistics (choose 1)

Course Code

Course Name

Credits

Availability

STAT 5052

Statistical and Machine Learning

3 credits

Periodic Fall & Spring

STAT 5302

Applied Regression Analysis

4 credits

Every Fall, Spring, & Summer

STAT 5303

Designing Experiments

4 credits

Every Fall, Spring, & Summer

Note that most courses are offered in person. To learn more, read more about coursework requirements or review course times and availability.

Application Requirements

We will review applications on a rolling basis for consideration to start in the program in the fall or spring semester. All application materials should be submitted on the application portal, and we cannot review your application until all of the required materials are submitted.

Required application materials

  • Application for Admission submitted via the application portal
  • Resume/CV
  • Transcripts - Unofficial transcripts from all post-secondary institutions you have attended are required. Please follow the admissions application instructions regarding transcripts.
  • Statement of Purpose - Communicate your preparation for advanced study in the chosen field including relevant coursework, research, or industrial experience. Outline your immediate educational and long-range career objectives in relation to the chosen field. Length is ideally less than or equal to 2 pages.
  • 2 Letters of Recommendation
  • Proof of English Language Proficiency - The University of Minnesota policy requires international and domestic applicants whose first language is not English to provide proof of English Language Proficiency as a condition of admission. CEMS follows the minimum requirements for the ELP on TOEFL, IELTS, or MELAB. If you think you might be exempt from submitting an ELP score, please refer to the English Language Proficiency section of the Graduate School website.
  • GRE is not required and if a score is submitted, we will not review out of fairness to other applicants.

Complete this brief inquiry form to learn more about our Post-baccalaureate Certificate in Data Science for Chemical and Materials Engineering.

Contact [email protected] with any questions.

Race wearing a blue sweater

Race Cleveland

Academic Advisor

[email protected] 
Prodromos Headshot

Prodromos Daoutidis

Director of M.S. in Data Science

[email protected]