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Bhumesh Kumar, right, a PhD student in electrical and computer engineering, guides students through a problem during a session of ECE 532: Matrix Methods in Machine Learning.
August 1, 2023

New certificate program instills data science skills for engineers

Written By: Tom Ziemer

Hailey Mendola is a rising industrial engineering junior at the University of Wisconsin-Madison whose penchant for crunching numbers is leading her toward a career in manufacturing—ideally as a plant manager someday.

Hailey Mendola

“I love looking at data,” says Mendola, a native of Oak Creek, Wisconsin, who spent summer 2022 interning at Lavelle Industries, a rubber and plastics manufacturer in Burlington, Wisconsin. “Being able to pinpoint and use those numbers to find new ways to do things and be able to manipulate data to figure out things easier, quicker, more efficiently.”

In order to realize her career goals, though, Mendola realizes she needs to build out her data science skillset, along with credentials to entice future employers. To help prepare students like Mendola for the evolving workforce, the College of Engineering is launching a new undergraduate certificate in engineering data analytics in fall 2023.

“Data is everywhere,” says Amanda Smith, an assistant teaching professor of industrial and systems engineering who’s helped lead efforts to develop the certificate program. “Everything now is really based on data—collecting data, analyzing data, interpreting data and using data to make decisions. Regardless of industry, regardless of the specific job, having a basic understanding of how to do those four key things with data—you basically can’t be a successful engineer without those skills. And what we’re hearing, especially from a lot of the bigger-name employers that hire our students, is that they’re really not even considering candidates who don’t have a sound background in data science, data analytics.”

While the new certificate program is a joint effort between the departments of industrial and systems engineering and electrical and computer engineering, it’s open to all undergraduates across the college who are interested in augmenting their education.

To reach the required 15 credits, students will choose courses in four categories—foundations of data analytics, applications of data analytics, data science, and machine learning—before taking a required capstone course, Ethics of Data for Engineers. Kangwook Lee, an assistant professor of electrical and computer engineering whose research includes improving fairness in machine learning, is developing the new course, which will launch in spring 2024.

“The ethics component is very important,” says Eduardo Arvelo, an assistant teaching professor of electrical and computer engineering who’s helped create the certificate program. “I think these next few years will be a very clear demonstration of the impact of these tools in everybody’s lives, from the bad actors who can use this technology for harm. And I think having this understanding from an early age in people’s careers will set them up for success and for the good of society as well.”

While students will learn programming in languages such as Python and Julia, database management skills, and the mathematical fundamentals underpinning modern analytical techniques, Arvelo says the program will more holistically cultivate an engineering mindset to solve problems through data science. He and Smith are hopeful more departments across the college will add course offerings that apply data science to different engineering domains in the coming years.

“Our program will tailor to engineering situations,” Arvelo says. “There will be many paths for students to take. I think as the program develops over the years, there will be a lot of engineering-focused courses that will enrich students’ experience beyond just pure data science or pure data analytics courses.”

Top photo caption: Bhumesh Kumar, right, a PhD student in electrical and computer engineering, guides students through a problem during a session of ECE 532: Matrix Methods in Machine Learning. Photo by: Tom Ziemer.