Program Overview
Do you want to be that engineer? You know the one. They’re confidently utilizing data analytics to transform big data into informed, high-impact actions. All in a day’s work, especially when they’ve received an exemplary education from one of the leading graduate engineering schools according to U.S. News & World Report.
With UW–Madison’s online master’s degree in engineering data analytics program*, you’ll have access to the essential framework that’ll help you become the engineer you aspire to be. Our 30-credit online program offers a distinctive blend of data science education specifically tailored to engineering, along with the essential skills required for project leadership and effective team management.
Through a truly interdisciplinary set of courses weaving electrical, computer, mechanical and industrial systems engineering with library and information sciences and business, you’ll discover how to collect and verify data and use it to tell compelling stories that can drive impactful decisions.
*The diploma will read: Master of Engineering: Engineering. The transcript will read: Major: Engineering Option: Engineering Data Analytics.
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What You’ll Learn
With up-to-date courses and experienced and dynamic professors at your—virtual—side, you can expect to learn the following (and more):
- Machine learning and predictive analytics.
- Statistical methods and decision science.
- Visualization tools and techniques.
- Optimization of products, processes, research, design, testing and operations.
- Leadership and communication skills to effectively manage change.
Why Choose UW–Madison?
With so many universities to choose from, how do you determine the right choice for your unique needs? At UW–Madison, we understand the challenges that come with juggling work and school. That’s why we’ve created a program catered to working professionals interested in pursuing higher education and pushing the boundaries of engineering.
There are numerous benefits awaiting future Badgers in this innovative online program:
- Smaller class sizes mean deeper connections with your fellow classmates and professors — not to mention more engaging and enriching discussions.
- As one of the pioneers of online learning in the 1990s, the University of Wisconsin–Madison has perfected the art of the digital learning platform.
- Our program provides the flexibility to work where you want and when you want, whether you travel for work or have an unpredictable work schedule.
- Our engineering programs utilize industry advisory boards, so we know what companies want and need from their employees. No matter what industry you’re in, we can provide the skills to set you up for success in your field.
Course Topics
The 30-credit online master’s degree in engineering data analytics consists of 15 core credits in data analytics and 15 elective credits consisting of additional data science courses or other online engineering and professional development courses.
You can also customize your courses to your chosen career path and personal interests so that you’re able to gain the necessary skills to advance within your industry. No matter which courses you select, the MEDA curriculum will provide the knowledge and application of the latest best practices and innovations. Some of the course topics you can choose from include:
- Industrial data analytics
- Machine learning
- Computing concepts
- Design optimization
- Data visualization
- Applied temporal data analytics
- Technical project management
- Engineering courses in leadership, manufacturing, polymers and sustainable systems
For more details, you can visit Courses tab above.
Advanced Career Opportunities
Whether you’re interested in becoming a data engineer, a risk analyst or something else entirely, our online program’s broad career track allows you to diversify your career options to make yourself a more in-demand asset in the workforce. Other potential job opportunities include:
- Data scientist
- Business analyst
- Machine learning engineer
- Research and development engineer
- Optimization engineer
- Quality assurance engineer
- Private consultant
Where Our Alumni Work
With a graduate degree from UW-Madison, you will open the door to new and exciting career opportunities from a wide variety of employers, not limited to:
- 3M
- Harley-Davidson
- Intel Corporation
- John Deere
- NHL
Webinar
In this webinar, join Program Director Susan Ottmann and Graduate Advisor Justin Kyle Bush for a conversation about the master’s in Engineering Data Analytics program, including curriculum, application process and potential career paths.
Select five courses (15 credits) from the following list:
| Course Code | Course Title | Credits |
| ISyE 412 | Fundamentals of Industrial Data Analytics |
3 |
| ISyE/ME 512 | Inspection, Quality Control and Reliability |
3 |
| ISyE 603 | Special Topics in Engineering Analytics and Operations Research (Topic: Applied Temporal Data Analytics) |
3 |
| ISyE 649 | Interactive Data Analytics | 3 |
| ISyE 620* | Simulation Modeling and Analysis | 3 |
| LIS 751* | Database Design for Information Professionals |
3 |
| ME 459 | Computing Concepts for Applications in Engineering |
3 |
| ME/COMP SCI/ ECE 532 |
Matrix Methods in Machine Learning | 3 |
| ME 548 | Introduction to Design Optimization | 3 |
| ME/COMP SCI/ ECE/EMA/ EP 759 |
High Performance Computing for Applications in Engineering |
3 |
*These courses are only offered on-campus at this time.
Electives
Students choose 15 elective credits from courses numbered 300 and above within Engineering Management, Manufacturing Systems, Polymer Engineering, and Sustainable Systems Engineering in consultation with their advisor. Students may also select from the concentrations listed below.
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Leadership Concentration
| Course Code | Course Title | Credits |
| EPD 611 | Engineering Economics and Management | 3 |
| EPD 612 | Technical Project Management | 3 |
| EPD 619 | Fostering and Leading Innovation | 3 |
Manufacturing Concentration
| Course Code | Course Title | Credits |
| ISyE 615 | Production Systems Control | 3 |
| ISyE 618 | Quality Engineering and Quality Management | 3 |
| ISyE/ME 641 | Design and Analysis of Manufacturing Systems | 3 |
Sustainable Systems Concentration
| Course Code | Course Title | Credits |
| EPD 660 | Core Competencies of Sustainability | 3 |
| EPD 690 | Special Topics: Distributed Renewable System Design | 3 |
| OTM 770 | Sustainable Approaches to System Improvement | 3 |
Additional Elective Courses Available
| Course Code | Course Title | Credits |
| ISyE/ME 512 | Inspection, Quality Control and Reliability | 3 |
| EPD 455 | Python for Applications in Engineering | 1 |
| EPD 614 | Marketing for Technical Professionals | 3 |
| EPD 637 | Polymer Characterization | 3 |
| EPD 678 | Supply Chain Management for Engineers | 3 |
| EPD 706 | Leading and Managing Organizational Change | 3 |
| EPD 708 | Fundamentals of Innovation Through Design Thinking | 3 |
| EPD 783 | Leading and Managing Teams Effectively | 3 |
| ME 446 | Automatic Controls | 3 |
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Admission Requirements
- A Bachelor of Science (BS) from a program accredited by the Accreditation Board for Engineering and Technology (ABET) or the equivalent. *
- An upper-division GPA of 3.00 or a master’s degree with a cumulative GPA of 3.00.
- Registration as a professional engineer by examination, if achieved, should be documented to support your application.
The GRE is not required. However, you may submit your results if you feel it will improve your chances of qualifying for the program.
*Equivalency to an ABET accredited program: Applicants who do not hold a bachelor’s degree from an ABET-accredited program may also qualify for admission to the program. Such applicants must have a BS in science, technology, or a related field with sufficient coursework and professional experience to demonstrate proficiency in engineering practice OR at least 16 credits of math and science coursework.
All applicants are advised to determine whether this program meets the requirements for licensure in the state where they live. See the National Society of Professional Engineers website for contact information for state licensing boards.
International Admissions Requirements
- A degree comparable to an approved U.S. bachelor’s degree.
- Academic performance comparable to a 3.00 for an undergraduate or master’s degree.
- Applicants whose native language is not English must provide scores from the Test of English as a Foreign Language (TOEFL). The minimum acceptable score on the TOEFL is 580 on the written version, 243 on the computer version or 92 on the Internet version.
If you’re ready to level up your skillset and ignite your career, apply today.
Tuition
UW–Madison’s tuition costs for the engineering data analytics program are as follows:
- Per credit
Tuition is $1,300 per credit, payable at the beginning of each semester.
- Total tuition
The total tuition for this program is $39,000.*
*This total does not include textbooks or course-specific software. Software required for courses is typically available by UW–Madison or in educational versions at substantial discounts.
Faculty
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Academic Director: Sinan Tas, PhD
Contact: tas@wisc.edu
Dr. Tas is the director of masters programs in the Department of Industrial & Systems Engineering at UW–Madison. He holds a PhD in industrial and systems engineering and multiple master’s degrees in fields including health systems engineering and international relations-security.He has extensive teaching experience in both graduate and undergraduate courses, specifically in the areas of optimization, decision and risk analysis and capstone projects. He is a certified project manager with practical experience in operations research, operations management and risk analysis projects with organizations including Argonne National Laboratories, the Department of Energy, the Department of Homeland Security, the Wisconsin Department of Health Services and UW Health.
Dr. Tas has a broad range of research and teaching interests related to engineering, including energy, information systems, homeland security, quality, production systems, operations and supply chain management, complex infrastructure systems and health care.
Graduate Advisor: Libby Miller, MEd
Contact: studentservices@interpro.wisc.edu
Libby is the graduate academic advisor for the following online graduate engineering programs:
- Manufacturing Systems Engineering (MSE)
- Engineering Data Analytics (MEDA)
- Power Engineering (ECE)
- Power Conversion and Control Capstone Certificate (PCC)
Prior to her role at UW-Madison, Libby served as a Graduate Advisor for the Electrical & Computer Engineering Department at UC Santa Cruz and as Admissions Coordinator for the College of Sciences & Mathematics at Belmont University. Before that, Libby was an Enrollment Adviser for the University of Wisconsin Extended Campus online degree programs. Throughout her career in education, she has facilitated graduate student advising, provided departmental leadership and administrative support and coordinated a variety of large events that support student success. Libby understands the challenges and requirements of success in graduate education and hopes to assist students in achieving this significant accomplishment.
John Lee, PhD
Contact: jdlee@engr.wisc.edu
Jeff Linderoth, PhD
Contact: linderoth@wisc.edu
Kaibo Liu, PhD
Contact: kliu8@wisc.edu
Dr. Liu is an esteemed engineering professor specializing in system informatics and data analytics. His research focuses on utilizing the data fusion approach to model, monitor, diagnose, forecast and make decisions in complex systems. His work has garnered widespread recognition across multiple research communities, including quality, statistics, reliability and data mining.Notably, Dr. Liu has received several prestigious awards, including the Outstanding Young Manufacturing Engineer Award by SME, the Feigenbaum Medal Award by ASQ and the Dr. Hamed K. Eldin Outstanding Early Career IE in Academia Award by IISE. He has also been the recipient of several best paper awards from INFORMS and ISERC, as well as featured articles in IIE and INFORMS magazines. Dr. Liu’s research has been supported by esteemed organizations such as NSF, ONR, AFOSR, DOE and industry partners.
He holds a bachelor’s degree in industrial engineering and engineering management from the Hong Kong University of Science and Technology and a master’s degree in statistics and a doctorate degree in industrial engineering from Georgia Institute of Technology.
Dan Negrut, PhD
Contact: negrut@wisc.edu
Dr. Negrut is the Mead Witter Foundation Professor in the mechanical engineering department. His research and teachings focus on the application of computational science to engineering. Dr. Negrut founded the Wisconsin Applied Computing Center, leads the Simulation-Based Engineering Lab and is an NVIDIA CUDA Fellow.Prior to joining UW–Madison, Dr. Negrut worked at Mechanical Dynamics, Inc. and the University of Michigan. He was also a visiting scholar at the Mathematics and Computer Science Division of Argonne National Laboratory. Dr. Negrut has a PhD in mechanical engineering from the University of Iowa.
Anthony Orzechowski, PhD
Contact: aorzechowsk2@wisc.edu
Dr. Tony Orzechowski is the director of the Abbott Diagnostics R&D Data Analytics organization and an instructor in the engineering data analytics master’s degree program. His distinguished career includes over 35 years of increasing management responsibility. He is responsible for both ongoing support of product launches and digital innovation with over 100 statisticians, data scientists, business analysts, quality engineers and data management personnel.He received an undergraduate degree in mechanical engineering and a master’s degree in manufacturing systems engineering from UW–Madison. Dr. Orzechowski is a certified Six Sigma Black Belt and an adjunct lecturer at Northwestern University, and he is launching a new non-credit course entitled Data for Technical Leaders at UW–Madison.
Krishnan Suresh, PhD
Contact: ksuresh@wisc.edu
Barry Van Veen, PhD
Contact: bvanveen@wisc.edu
Dr. Van Veen has been with the Department of Electrical & Computer Engineering at the University of Wisconsin–Madison since 1987, where he is currently the associate chair for graduate and online studies and the Lynn H. Matthias Professor of Electrical and Computer Engineering.He has co-authored the book Signals and Systems [Wiley, 1999 (first edition), 2003 (second edition)] with S. Haykin. He is also the publisher of allsignalprocessing.com. His current research interests include signal processing and its applications, including the development of algorithms for biomedical signal-processing problems.
Dr. Van Veen was a recipient of the 1989 Presidential Young Investigator Award from the National Science Foundation and the 1990 IEEE Signal Processing Society Paper Award. He received multiple teaching awards at the University of Wisconsin, including the 2014 Spangler Award for Technology Enhanced Instruction, the 2015 Chancellor’s Distinguished Teaching Award and the 2017 Benjamin Smith Reynolds Award for Teaching Engineers.
Dr. Van Veen received his Bachelor of Science in Electrical Engineering from Michigan Technological University in Houghton, Michigan, in 1983, and his PhD in electrical engineering from the University of Colorado-Denver in 1986.
Jiao (Tina) Xu, PhD
Contact: jxu25@wisc.edu
Shiyu Zhou, PhD
Contact: szhou@engr.wisc.edu
Dr. Zhou is a professor in the Department of Industrial & Systems Engineering at the University of Wisconsin–Madison. Dr. Zhou has taught courses on facilities planning and computer-integrated manufacturing and has directed graduate student research and independent study.His research interests are in the areas of modeling, diagnosis and control of complex manufacturing processes through data analytics. He has been sponsored by the National Science Foundation (NSF), the Air Force Office of Scientific Research and the Department of Energy, to name a few. He has authored and co-authored dozens of papers and received the NSF CAREER Award in 2006.
Dr. Zhou holds a doctorate in mechanical engineering from the University of Michigan, Ann Arbor.
Ari Smith
Contact: ajsmith44@wisc.edu
Kamil Can Bora
Contact: ckbora@wisc.edu
Justin Kyle Bush
Contact: justinkyle.bush@wisc.edu
Justin Kyle Bush serves as the Graduate Programs Administrator for the Engineering Data Analytics (MEDA), Polymer Engineering (MEPE), and Power Engineering (Power) programs. In this role, he offers leadership support in various aspects including student recruitment, marketing, program governance, budget management, and financial accountability. Justin is also the Student & Alumni Engagement manager. In this role, he develops and oversees programs that facilitate meaningful interactions between students and alumni. I ensure that students benefit from the wealth of experience our alumni have to offer. Additionally, in this role he works to keep our alumni engaged and involved, celebrating their achievements and encouraging their ongoing support.
Prior to joining UW-Madison, Justin was the Program Assistant and the Interim Department Manager for the Civil and Environmental Engineering Department at the Colorado School of Mines. During his time in education, he facilitated graduate student advising, provided departmental leadership and administrative support, and coordinated a variety of large events that supported student success.
Justin’s education includes a Bachelor of Arts in History from Metropolitan State University of Denver and a Master of Education in Higher Education Administration from Northeastern University.
