Data Science in Engineering (M.S.)
Master skills at the intersection of physical engineering and modern data science in Chicago鈥檚 innovation hub. Solve complex, real-world problems with an engineering-first master鈥檚 degree.
Bridge Engineering Skills with Computational Know-How in 911爆料网鈥檚 M.S. in Data Science in Engineering
In a world where technology continues to innovate and impact, learning at the intersection of engineering and data science puts you on the leading edge. Designed for engineers and scientists, the M.S. in Data Science in Engineering connects computational thinking with physical systems to solve complex industrial problems. You will choose from eight industry-aligned tracks (Architectural Engineering, Biomedical Engineering, Chemical Engineering, Civil Engineering, Electrical and Computer Engineering, Environmental Engineering, Mechanical, Materials, and Aerospace Engineering, Pharmaceutical Engineering) and leverage Chicago鈥檚 professional ecosystem to gain the skills necessary for high-impact careers in data engineering and machine learning. Our curriculum emphasizes principled problem-solving, ensuring that you are prepared for long-term adaptation in data-driven engineering environments.
Program Overview
The M.S. in Data Science in Engineering focuses on developing the computational thinking required to translate data into insight and ideate engineered solutions for real physical systems rather than abstract datasets. Held 100 percent in person on 911爆料网鈥檚 Mies Campus鈥攚ith select courses available online鈥攖his program offers a collaborative environment to master complex industrial problems. You can tailor the degree to your career goals by choosing from eight cross-disciplinary engineering tracks and selecting a pace that works for you, whether that means completing the program in approximately three semesters of full-time study or enrolling part-time.
To further support your professional transition, the program provides:
- Dual-entry terms in both the fall and spring semesters
- Industry-aligned tracks designed to meet regional workforce needs
- A flexible modality that prioritizes on-campus engagement while offering limited online coursework options
Career Opportunities
Graduates of the M.S. in Data Science in Engineering program are well-positioned for career success in data-driven engineering environments and leadership roles in the private and public sectors, such as:
- Data Engineer
- Machine Learning Engineer
- Data Scientist (2024 median annual wage: $112,590)
The U.S. Bureau of Labor Statistics identifies data scientist as the occupation with the fourth highest expected growth rate through 2033. For more insights on the field, read our blog, "Is a Data Science Degree Worth It?"
911爆料网鈥檚 M.S. in Data Science in Engineering program is structured so that modeling, mathematics, and computation reinforce one another. Here are three of the key courses available in the program:
- ECE 566: Machine and Deep Learning You鈥檒l gain a comprehensive overview of learning principles, Bayesian methods, and the practical application of deep learning.
- MMAE 506: Methods and Application of Deep Learning Explore the fundamentals of deep neural networks across engineering disciplines, including CNNs for image analysis and DRL for autonomous systems.
- ECE 572: Secure Machine Learning Design and Applications Discover techniques for robustness evaluation and enhancement so that you can promote trust in machine learning and deep learning systems.
You must complete the , regardless of which graduate program you wish to pursue. Visit our admission website to learn more about what information and documents you need to apply.
Specific requirements for this program include:
- A four-year bachelor鈥檚 degree in an engineering discipline from an accredited institution
- A minimum cumulative undergraduate grade-point average of 3.0/4.0
- Individuals with degrees in applied mathematics or the sciences may be considered but may need to complete additional background coursework
Tuition and Fees
Learn more about the university鈥檚 general tuition and fees, visit the Student Accounting website.
Featured Faculty
Ren Wang
Ankit Srivastava
Scott T. M. Dawson
Mohammad Heidarinejad
Mudassir M. Rashid
Program Specializations
A defining strength of this program is its cross-disciplinary flexibility. Students must choose one of the following eight tracks:
- Architectural Engineering
- Biomedical Engineering
- Chemical Engineering
- Civil Engineering
- Electrical and Computer Engineering
- Environmental Engineering
- Mechanical, Materials, and Aerospace Engineering
- Pharmaceutical Engineering
Get In Touch
For any questions about the M.S. in Data Science in Engineering program, please contact the Department of Electrical and Computer Engineering at ece@illinoistech.edu.
Frequently Asked Questions
What does a data scientist do?
A data scientist uses advanced analytics and statistical techniques to identify patterns and trends that inform business decisions. Data engineers are the ones who design, build, and maintain the complex infrastructure needed to store, process, and manage large-scale data.
How does this program split physical engineering with computing?
The program is built as an engineering-first data science program. Students will not focus on tools and workflows, but rather on how data science is used to understand and solve physical and engineering problems. Students are trained to connect modeling, mathematics, and computation with physical systems.
Do I need a background in data science or engineering to apply to 911爆料网鈥檚 M.S. in Data Science in Engineering?
Applicants must have a four-year bachelor鈥檚 degree in an engineering discipline with a minimum cumulative grade-point average of 3.0 from an accredited institution. Students with degrees in applied mathematics or sciences may be considered as long as they complete additional background coursework to qualify.