This certificate is designed to enhance the skills of engineering students in the field of Data Analytics, which is in high demand across all engineering fields. Students may choose from a wide variety of courses from each of the four main areas: Foundations of Data Analytics, Applications of Data Analytics, Data Science, and Machine Learning. The culminating course in the program focuses on ethical issues in Data Analytics and provides students with principled solutions to address these modern societal challenges.
The program is open to any degree-seeking undergraduate engineering student with a plan of study that fulfills the certificate requirements. Applications can be submitted at any time, but students are encouraged to apply early to ensure a smooth and successful completion of the program.
How to Get in
All current undergraduate students in the College of Engineering are eligible to complete the Certificate in Engineering Data Analytics. Students should meet with the Certificate Advisor to discuss their intention to pursue the certificate and submit an online declaration form: https://engineering.wisc.edu/programs/certificates/engineering-data-analytics/declaration.
Students declared in the Certificate in Data Science are not eligible to declare the Certificate in Engineering Data Analytics.
Requirements
Select one course from each area. The ethics course must be taken after the other four courses are completed.
| Code | Title | Credits |
|---|---|---|
| Foundations of Data Analytics | 3 | |
| Applications of Data Analytics | 3-4 | |
| Data Science | 3 | |
| Machine Learning | 3 | |
| Ethics (Complete last) | 3 | |
| Total Credits | 15 | |
Foundations of Data Analytics
Choose one of the following courses:
| Code | Title | Credits |
|---|---|---|
| E C E 203 | Signals, Information, and Computation | 3 |
| E C E 204 | Data Science & Engineering | 3 |
| E C E 331 | Introduction to Random Signal Analysis and Statistics | 3 |
| I SY E 210 | Introduction to Industrial Statistics | 3 |
| I SY E 312 | Data Management and Analysis for Industrial Engineers | 3 |
| I SY E 412 | Fundamentals of Industrial Data Analytics | 3 |
Applications of Data Analytics
Choose one of the following courses:
| Code | Title | Credits |
|---|---|---|
| E C E 334 | State Space Systems Analysis | 3 |
| E C E 431 | Digital Signal Processing | 3 |
| E C E 432 | Digital Signal Processing Laboratory | 3 |
| E C E 454 | Mobile Computing Laboratory | 4 |
| E C E/COMP SCI 533 | Image Processing | 3 |
| I SY E/M E 512 | Inspection, Quality Control and Reliability | 3 |
| I SY E 517 | Decision Making in Health Care | 3 |
| I SY E 575 | Introduction to Quality Engineering | 3 |
| M S & E 561 | Machine Learning in Materials | 3 |
Data Science
Choose one of the following courses:
| Code | Title | Credits |
|---|---|---|
| E C E/COMP SCI/I SY E 524 | Introduction to Optimization | 3 |
| E C E/COMP SCI 561 | Probability and Information Theory in Machine Learning | 3 |
| I SY E 516 | Introduction to Decision Analysis | 3 |
| I SY E 620 | Simulation Modeling and Analysis | 3 |
| I SY E 624 | Stochastic Modeling Techniques | 3 |
| I SY E/MATH/OTM/STAT 632 | Introduction to Stochastic Processes | 3 |
Machine Learning
Choose one of the following courses:
| Code | Title | Credits |
|---|---|---|
| E C E/COMP SCI/M E 532 | Matrix Methods in Machine Learning | 3 |
| E C E/COMP SCI/M E 539 | Introduction to Artificial Neural Networks | 3 |
| I SY E 521 | Machine Learning in Action for Industrial Engineers | 3 |
Ethics
Choose one of the following courses:
| Code | Title | Credits |
|---|---|---|
| I SY E 562 | Human Factors of Data Science and Machine Learning | 3 |
| I SY E/E C E 570 | Ethics of Data for Engineers | 3 |
Certificate Completion Requirement
This undergraduate certificate must be completed concurrently with the student’s undergraduate degree. Students cannot delay degree completion to complete the certificate.
Learning Outcomes
- Apply tools and methods to understand, analyze, and interpret data from a variety of sources
- Apply tools and methods to draw conclusions from and make decisions based on analysis of data
- Articulate the potential impact of a data-driven decision in the context of ethics, fairness, and equity
- Identify how engineers apply data analytics in practice using machine learning, data science, and other fundamental tools of data analytics