Data Science for Chemical Engineering and Materials Science University of Minnesota

Overview

The Data Science for Chemical Engineering and Materials Science programme at the University of Minnesota provides a sophisticated blend of engineering principles and advanced computational techniques. This curriculum is designed to equip the next generation of professionals with the skills required to navigate and lead within increasingly digital industrial environments.

Students gain a robust foundation in statistical analysis, machine learning, and mathematical optimisation. The course structure ensures that these data-driven methodologies are applied directly to complex problems within biological, chemical, and materials engineering, fostering a unique interdisciplinary perspective that is highly valued by global employers.

Why Data Science for Chemical Engineering and Materials Science at the University of Minnesota?

This programme stands out as the first joint degree of its kind offered by the department, reflecting the university's commitment to innovative, cross-disciplinary education. Students benefit from access to high-performance computing resources and facilities within the College of Science and Engineering, which are essential for modern data analytics.

A significant feature of the programme is the optional capstone project, which allows students to conduct research under the guidance of distinguished faculty members. These projects often involve collaboration with industry advisors or other academic departments, providing practical experience with cutting-edge research and industrial applications.

Furthermore, the university facilitates strong professional connections through organised events and networking opportunities. These initiatives allow students to engage with industry leaders and peers, enhancing their understanding of current trends in automation, robotics, and systems engineering while building a valuable professional network.

Tuition Fee Breakdown

• International fee: USD 16776 per semester
• National fee: USD 16776 per semester
• Local fee: USD 10632 per semester

Visit the Fees and Funding section for a breakdown in your local currency.

Syllabus

The curriculum consists of 30 credits, including core computational units and specialised electives. Modules may include:

• Chemical Engineering Computational Courses
• Materials Science Computational Courses
• Computer Science
• Statistics
• Artificial Intelligence
• Automation and Robotics
• Data Analysis
• Business Analytics
• Control and Optimisation
• Systems Engineering
• High Performance Computing

Careers with Data Science for Chemical Engineering and Materials Science

Graduates are prepared for specialised roles that require a combination of engineering knowledge and data expertise. The interdisciplinary training provided by the university is specifically designed to meet the demands of the chemical, biotechnology, and materials industries, where digital transformation is a priority.

Potential career paths include roles in data analytics, systems engineering, and process automation. By mastering machine learning and statistical tools alongside engineering fundamentals, alumni are well-positioned to lead innovation in research and development, manufacturing, and computational design within various high-tech sectors.