Become the engineer who confidently uses data science to transform big data into informed, high-impact actions.
Industries of all sorts are in critical need of engineers who understand and can apply appropriate data science tools and methods to drive improvements to products and processes, research, design, testing, and operations. UW-Madison’s Engineering Data Analytics program uniquely combines data science learning with focused applications in engineering and skills needed to lead projects and teams.
What you will learn in this program:
- 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.
UW-Madison has been in the distance education business for over 20 years. With hundreds of…
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The Master of Engineering: Engineering Data Analytics degree will give you the skills to know how and when to use data to solve complex engineering problems.
Whether you’re new to data analytics or have some experience in the field, our program is tailored to those who want to develop skills in a wide range of areas—whether it’s computer science, engineering, information management, or physical sciences. UW–Madison has strong ties to local companies and thought leaders in the field, so you’ll know where the industry is going—sometimes before everyone else!
You Can Study, Work, and Still Have a Life
You’re busy. You’ve got work, hobbies, friends, and family. That’s why we’ve tailored our program to fit into your lifestyle. Choose from the part-time or full-time program, and log in to learn when it works for you. Both programs are 100% online—no residencies or on-campus sessions required.
Unlike face-to-face programs, you can study on the treadmill and in between Netflix or gaming binges. We know you’re already busy—so there are no “busy work” assignment fillers. Our instructors to give you exactly what you need to excel at work the next day.
At UW–Madison, we know that learning is more than just academics.
Your learning isn’t limited to what’s contained in the curriculum. You’ll develop relationships with mentors and your fellow students, learn from their successes and failures, and they’ll learn from yours. With an online learning experience you can access 24-7, you can make what you learn your own. Talk about homework with your peers in the class discussion forum, or share some of the challenges you face daily on the job. Chances are, your fellow students have been in your shoes. And so have your instructors—while they’re among the best in their fields, they’re never too busy to answer your e-mail to give some advice.
Typical Weekly Schedule
In a typical week, you will engage in online project work, readings and presentations designed to address engineering challenges, and further your problem-solving skills.
The weekly assignments afford you the flexibility to choose when to complete them, but weekend deadlines and structured support help keep you on track. Students also participate in weekly course web conferences where they interact with faculty and peer learners.
Each course will engage you in extensive, meaningful interaction with the instructor and other engineers without interruption to your work and travel plans. Depending on your background, you can expect to spend approximately 12-18 hours per course per week.
You can complete your Master of Engineering: Applied Computing and Engineering Data Analytics program in two years, depending on your schedule and course selections.
At the University of Wisconsin, we empower our students to become creative problem solvers, able to integrate statistical and data analysis with design and optimization, seek out and create new applications in computing, and adapt to new situations.
Curriculum* for this program is the result of a joint effort led by the College of Engineering, along with the Departments of Statistics and Computer Sciences, the School of Library and Information Sciences, as well as faculty across UW–Madison, working in Big Data and analytics space.
Our courses will build your ability to assemble and analyze data needed to face challenges in engineering systems. You will also learn to evaluate advanced computing tools, simulation, modeling, and engineering optimization. As a master’s student, you will develop and polish your skills in project management, team leadership, and effective communication.
You will earn a Master of Engineering in Engineering degree with an emphasis in Engineering Data Analytics upon completion of 30 graduate credits required by the University of Wisconsin.The program and course schedule are designed to be flexible for part-time students, but the degree program can be completed in two years.
No On-campus Residency Requirement
All of the courses are completely online, and students are not required to participate in an on-campus residency program. The Engineering Data Analytics program is offered in conjunction with the Wisconsin Applied Computing Center, and students are welcome to attend the annual Wisconsin Forum on Advanced Computing in Engineering in the spring of each year. Here they can share and learn from leading experts from academia and industry.
Courses in the Engineering Data Analytics curriculum will provide you with knowledge and application of the latest best practices and innovations. Course projects that you select enable you to customize your learning and gain immediate impact. Your elective courses provide complementary opportunities for focused application of data analytic methods and tools. Professional development electives provide insights to improve your leadership of engineering data analytic initiatives.
Click here to download a program outline and learn more about the concentrations.
*curriculum subject to change
Data Analytics Core Courses (15 credits)
Industrial Data Analytics
Matrix Methods in Machine Learning
Develop your ability to implement data-driven modeling techniques such as regression, classification, and principal component transformation. Understand the concept of model complexity and trade-off between model bias and variation, as well as improve your problem-solving capability using realistic industrial datasets.
High-Performance Computing for Applications in Engineering
An introduction to machine learning that focuses on matrix methods and features real-world applications ranging from classification and clustering to denoising and data analysis. Mathematical topics covered include: linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include: the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. In addition to the formal course requisites, students are expected to have had exposure to numerical computing (e.g. Matlab, Python, Julia, R).
Engineering Applications of Statistics
Study and apply hardware and software solutions that enable the use of advanced computing in tackling computationally intensive engineering problems. Hands-on learning is promoted through programming assignments that leverage emerging hardware architectures and use parallel computing programming languages.
Introduction to Design Optimization
Make better, data-driven decisions using statistical analysis. Students say this is the course that finally helps them understand statistics. In your course project, you’ll design and execute a physical experiment, and present the results.
- Tracking Down Variation, Descriptive Statistics, and a Start with Statistical Software
- Probability Distributions, Sample Size Effects, and Confidence Intervals
- Design of Experiments
- Measurement Capability, Variance Components, and Gage R&R
- Regression Analysis
- Other Types of Data: Skewness, Proportions, and Counts
- Process Capability Metrics, Data Transformation, and Response Surface Methodology
Introduces basic concepts and techniques used in the optimization of engineering design components and systems. Pose and solve typical optimization problems such as truss and finite-element-based optimization. Enroll Info: Background in programming (preferably MATLAB) and basic knowledge of finite element analysis (FEA) are desirable but not required.
Data Analytics Elective Courses
This course introduces the design, theory, and creation of data visualizations. The course design includes weekly readings, assignments, and lectures. Emphasis is placed on commonly used graphical techniques for solving data-driven questions in industry. Students will develop data visualization skills using Tableau, Python, R, and Excel. Students will also be exposed to other software such as MINITAB, JMP, and SAS. Topics include visual perception, descriptive data analysis, time series analysis, correlation analysis, part-to-whole ranking analysis, deviation analysis, and multivariate analysis. Students should have some familiarity with a programming language.
Experience a Rich Learning Environment
UW’s online Engineering Data Analytics master’s program is designed for working professionals. The learning environment is flexible—allowing you to make the best use of your time, without interruption to work, family or other commitments.
We pair flexibility with a fixed curriculum and semester schedule that will help you maintain focus and consistent progress. This versatile but structured approach produces graduates with valuable new skills.
Our program is structured to capitalize on a variety of learning experiences. Learning in our program does not all happen in front of a computer. You will get the chance to provide insight in discussions, interact with experts from industry and academia, and use a variety of computer applications.
As a student, you will also have access to the UW-Madison academic libraries, which offer you 10 percent of the world’s library resources. Our students often highlight how regular check-in times, access to the Wisconsin Applied Computing Center’s advanced computing systems, and networking opportunities with fellow students, faculty and staff, enrich their experience in our program.
A supportive, collaborative environment
You will progress through the program as part of a vibrant learning community, and will constantly interact with peer learners via online tools like web conferencing, online discussion forums, email, and conference calls. Students and alumni often note the interactive approach is essential for staying engaged and on track for completing the program.
In addition, faculty and staff understand the challenges you face as a working professional and distance learner, and proactively monitor your progress. A full-time program advisor stands ready as your advocate in helping you with any issues that may influence your degree progress.
UW-Madison’s academic reputation, research focus, and industry partnerships partners guarantee high-caliber staff for each of our online modules. Professors are dedicated to keep students engaged, progressing, and confident that their learning goals are being met. Our academic advising will ensure you get the responsiveness and support you need, while keeping you on track in your studies.
Kristin Eschenfelder is a Professor and Director at the School of Library and Information Studies at the University of Wisconsin-Madison. Dr. Eschenfelder is also an affiliate of the Holtz Center for Science and Technology Studies in the School of Journalism and Mass Communications, and a founding board member of the Wisconsin Digital Studies program. Her research interests focus on information access, sharing and rights management. She writes about use regimes – or the complex, multi-level networks of laws, customs, technologies and expectations that shape what information we can access in our daily lives.
Linderoth is professor in the Industrial and Systems Engineering Department at UW–Madison. He has worked for the Argonne National Laboratory’s Mathematics and Computer Science Division, the optimization-based financial products firm of Axioma, and as an Assistant Professor at Lehigh University. Linderoth received the SIAM Activity Group on Optimization (SIAG/OPT) prize for the most outstanding paper on a topic in optimization in 2002. In 2014, he was also awarded the INFORMS Computing Society (ICS) prize. He received his PhD from the Georgia Institute of Technology.
Mark Millard is Director of Learning Design and Technologies for the Department of Engineering Professional Development at UW–Madison. Mark has published extensively on online education and educational innovation, and leads courses at UW-Madison to teach faculty effective practices for online instruction. Previously, Millard was Assistant Director of the Office of Instructional Consulting in the School of Education at Indiana University. Millard holds an MS in information science from Indiana University. firstname.lastname@example.org
Dr. Negrut is the Mead Witter Foundation Professor in the Mechanical Engineering Department at UW-Madison. His research and teaching are focused on the application of computational science to engineering. Dr. Negrut founded the Wisconsin Applied Computing Center, he leads the Simulation-Based Engineering Lab, and is a 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. Negrut has a Ph.D. in mechanical engineering from the University of Iowa.
Christine Nicometo is nationally known and respected for her ability to teach effective communication and presentation skills to technical professionals. She has taught at Michigan Technological University, University of Minnesota (Iron Range Engineering), and Finlandia University. Nicometo’s book on technical presentations was published in 2014 by IEEE-Wiley. An active member of ASEE and IEEE, she also worked on a multi-year National Science Foundation study about how people learn engineering. Nicometo received her MS in Rhetoric and Technical Communication from Michigan Technological University. email@example.com
Nowak is the McFarland-Bascom Professor of Electrical and Computer Engineering at UW–Madison. His research focuses on signal processing, machine learning, optimization, and statistics. In addition to his research and teaching, Nowak is a Fellow of the IEEE and the Wisconsin Institute for Discovery, a member of the Wisconsin Optimization group, and organizer of the SILO seminar series. Nowak has received multiple awards for his papers, including IEEE Signal Processing Society’s best paper award in 2009. He received his PhD from UW-Madison.
Qian is associate professor in the Department of Mechanical Engineering at UW-Madison. His research aims to efficiently acquire scan data from 3D objects to analyze and model that data using computer-aided geometric design and geometry processing methods. In a recent collaborative study, Qian investigated how the shape of the bones in the human hip joint could affect the risk of developing osteoarthritis. Before coming to UW–Madison, he worked as a research engineer at GE Global Research Center in New York. He has a PhD in mechanical engineering from the University of Michigan.
Salo is a faculty associate in the School of Library and Information Studies at UW-Madison. She specializes in research-data management, digital preservation, and big data ethics. She received the Early Career Award from the UW-Madison Department of Letters and Science in 2013 and was named one of the 2014 WISE Instructors of the Year. She has master’s degrees in Library and Information Studies and Spanish from UW-Madison.
Sifakis is assistant professor of Computer Science at UW–Madison. Alongside other UW computer scientists and UW medical researchers, Sifakis is working to invent a virtual surgery simulator. He has worked as a consultant with Intel Corporation, SimQuest LLC, and Walt Disney Animation Studios. He focuses his research on computer graphics, physics-based modeling/simulation, and scientific computing and is interested in algorithms that can simulate parts of the human body for biomechanics and virtual surgery. He earned his PhD in Computer Science from Stanford University and conducted postdoctoral research at the University of California–Los Angeles.
Krishnan Suresh, PhD is associate professor at UW-Madison. He is the director of the Engineering Representations and Simulation Laboratory (ERSL) at UW–Madison, which focuses on the design and analysis of multi-scale and multi-disciplinary engineering systems. In collaboration with his colleagues in the UW–Madison Department of Mechanical Engineering, Suresh is working to design a prototype heat exchanger using a 3D printing technique called fused deposition modeling (FDM). He has a PhD from Cornell University.
Zhou is a professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. 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 area 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. Zhou holds a doctorate in Mechanical Engineering from the University of Michigan, Ann Arbor.
Admission requirements for the Master of Engineering: Applied Computing and Engineering Data Analytics program are listed below.
Exceptions to standard admission requirements are considered by the admissions committee on an individual basis.
- A BS degree from a program accredited by the ABET or the equivalent.*
- A minimum undergraduate grade-point average (GPA) of 3.00 on the equivalent of the last 60 semester hours (approximately two years of work) or a master’s degree with a minimum cumulative GPA of 3.00. Applicants from an international institution must have a strong academic performance comparable to a 3.00 for an undergraduate or master’s degree. All GPAs are based on a 4.00 scale. We use your institution’s grading scale; do not convert your grades to a 4.00 scale.
- 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.
- International applicants must have a degree comparable to an approved U.S. bachelor’s degree.
GRE is not required. Applicants who have taken the test are encouraged to submit their scores.
*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. Registration as a professional engineer by examination, if achieved, should be documented to support your application.
All applicants are advised to determine whether this program meets requirements for licensure in the state where they live. See the National Society of Professional Engineers website for contact information for state licensing boards
The admissions process has been designed to conduct a holistic review of your likelihood of success in the program. Decisions are based on your academic and professional background.
To start the process, please read the admission requirements to determine your eligibility. If you have questions about your eligibility, please request an eligibility review by e-mailing Shainah Greene. This e-mail should include a copy of your current resume and informal transcripts.
Applications are accepted for admission during the Fall term. Applications are reviewed in the order received, on a rolling basis until the July 15 deadline. Admission is competitive and selective. Therefore, applicants are encouraged to submit application materials prior to the deadline.
Steps to Apply Now
Step 1: Submit the Online Application
In the application be sure to:
- Upload a pdf version of your current resume/CV
- Upload a pdf version of your “Reasons for Graduate Study” essay
- Upload a pdf version of your transcripts
- Enter contact information for at least three professional recommendations, including at least one from a direct supervisor
- Important: Complete the application by submitting the application fee. Applications submitted without paying the fee cannot be reviewed and will not be acted on.
Step 2: Complete a Phone Interview
After all of your application materials have been received, the admissions committee chair will schedule a phone interview with you. Once completed, your application will be presented to the Admissions Committee for evaluation at the next scheduled meeting.
Step 3: Application Evaluation
Admission decisions are made on applications in the order received. The Admissions committee will make one of the following decisions:
- Recommend admission to the UW–Madison Graduate School
- Request additional information before evaluating further
- Decline further consideration of your application
After a decision has been made on your application, the admissions committee chair will contact you by email to inform you of the decision and to schedule a time to discuss the decision and your next steps.
The admissions committee provides admission recommendations to the Graduate School. The Graduate School is the formal admitting office for graduate students and retains ultimate authority on all admissions decisions.
Step 4: Request Transcripts
If accepted into the program, arrange to have one copy of your undergraduate final official transcripts sent directly from your previous educational institution to the University of Wisconsin-Madison. International applicant’s academic records must include an official English translation done by the bachelor’s degree granting institution OR an official translator. See graduate school requirements for country specific information.
If your transcript does not have a degree and conferral date posted, the Graduate School will need a final official transcript before you begin your studies.
Directions to send official transcripts to the University of Wisconsin–Madison Graduate School:
- Order your transcript through your institution.
- We prefer electronic transcripts if your institution offers that option.
- If not, please send paper copies to the address below. Please do not send both a paper and electronic copy of the same transcript.
The Graduate School Admissions Office
University of Wisconsin–Madison
232 Bascom Hall
500 Lincoln Drive
Madison, WI 53706
(University of Wisconsin–Madison students do not need to send official transcripts, since we have access to them.)
Tuition and Financial Aid
Tuition Costs |
$1,300 per credit, payable at the beginning of each semester.
Tuition Includes |
- Technology costs for Internet course delivery
- Toll-free telephone line for the audio portion of conference calls
- Use of the webconferencing facilities for group project work for program courses
- Access to campus computing resources
Total Tuition |
Total tuition for this program is $39,000*.
* This total does not include textbooks or course software. Software required for courses is typically available in educational versions at substantial discounts.
Students who are U.S. citizens or permanent residents are eligible to receive some level of funding through the federal direct loan program. These loans are available to qualified graduate students who are taking at least four credits during the Fall and Spring semesters, and two credits during Summer. Private loans are also available. Learn more about financial aid.
Many students receive some financial support from their employers. Often, students find it beneficial to sit down with their employer and discuss how this program applies to their current and future responsibilities. Other key points to discuss include how participation will not interrupt your work schedule.
High Return on Investment
Your investment immediately begins paying back as your employee becomes a more effective contributor of engineering projects.
No Interruption to Employee’s Availability
All students are full-time, working engineers, and most travel extensively for their jobs. The online format enables your employee to pursue world-class graduate engineering studies without interruption to his/her work schedule and availability to travel. This internet-based program allows students to continue their studies from anywhere in the world.
Proven Program from a Top-Ranked University
The UW–Madison degree your employee will earn via distance learning has the same high-quality standards and academic status as a degree earned on-campus. The only difference is that UW’s program is conveniently delivered online for working professionals.