There’s a moment in data science, says Business Analytics student Kaedric Cobbs ’24MSBA, when everything starts to click. “You start to see the patterns, and how to get the information you need from them,” he states.
He chose to study business analytics because if you can capture those moments, you can be a decision maker, he says. “Every single decision companies make is decided by information,” Cobbs explains. “You can’t just go by gut feeling anymore, you have to be able to back it up.”
Right now, in Professor Suhong Li, Ph.D.’s Large Scale Data Management and Data Ethics course, he’s learning how build a world that makes sense — aided by Bryant’s brand-new Data Science Lab.
Through the course, which provides a comprehensive introduction to data preparation and management with a focus on applying it to large-scale analytics projects, students learn about state-of-the-art research, industry trends, and the social and ethical dimensions of data analytics and data science.
They also engage in hands-on exercises with a variety of industry tools including PostgreSQL, Neo4j, Gephi, Python, and AWS — and come to realize that coding isn’t just about knowing what works but also about understanding why it works and how to apply it to a nearly infinite range of projects.
Making sense of it all
Today, Li has presented her students with a new challenge based on an old idea. She’s asking them to develop business questions and write SQL code to query a video rental store — to uncover trends/insights on customer behavior, movie popularity, inventory usage and sales performance.
As the students attempt to create their own business questions and write code, they brainstorm, work together, and help each other out. The 36-seat lab is currently arranged in small pods but is highly reconfigurable, allowing for a variety of seating options that can accommodate different teaching and learning styles “We wanted to create a space for collaboration,” Li says.
As she visits each group, Li offers praise (“This looks very insightful” is a commendation of the highest order), but there’s also always something to tweak: a bug to squash or a line of code to streamline. With each stop she challenges the students to do more ¬— a never-ending search for perfect clockwork.
“Even the smallest change can be very useful,” she reminds them.
The lab helps the students keep up with even the smallest of tweaks in the most complicated of data analytics. It is equipped with the technology to project screens from the giant monitors at the front of the classroom to one or all of the “pod” monitors and vice versa — all the better to address problems and offer potential solutions wherever they might occur in the room.
“The technology makes it very easy to follow along, especially when the professor is working on something complicated,” Cobbs notes. “You can see what she’s doing up front right in front of you. Or, if she’s working with someone else, she can share it with everyone, which is good because you might have the same questions.”
Slowly, furrowed brows and looks of conclusion give way to confidence as the students make the information jump and dance at their command. They’re beginning to see the patterns Cobbs mentioned, and they’re even creating new ones.
Growing and Evolving
As the Information Systems and Analytics (ISA) Department Chair, Li has presided over a period of great expansion for Bryant’s ISA department, including the addition of Master of Science in Data Science (MADS) and Business Analytics (MSBA) programs.
That additional programming has been accompanied by an influx of new educators with a wide range of expertise, from applying machine learning techniques to examining biometric data to using data science to better understand and improve processes that involve vulnerable populations such as those experiencing homelessness, human trafficking, and forced migration.
And the department isn’t done yet. “Bryant has built our new advanced data science labs because we wanted to take our programs to a different level,” notes Li.
The lab is open to students outside the Information Systems and Analytics programs as well, says Li — true expansion means welcoming in partners from other areas as well. “We see students from finance, marketing, accounting, so many different areas of study, taking advantage of this lab,” she states. “We’re looking for interdisciplinary collaboration.”
Dirty hands, clear eyes
Li, who was named Computer Educator of the Year by the International Association of Computer Information Systems, doesn’t just want her students to learn, she wants them to get their hands dirty — metaphorically speaking, of course. They frequently work with real datasets provided by corporate partners such as Amica Insurance and AAA Northeast and attempt to solve real-world problems.
In order to tackle bigger projects, the lab allows students to connect their laptops to a powerful virtual machine equipped with impressive software and to a high-performing cluster — a group of computers that can work together to solve problems that are too large or time-consuming for a single machine.
“One of the most challenging parts of working on more advanced data science projects is the lack of computing resources,” says Li. “This lab provides students with access to some very powerful tools.”
With great power comes great possibility, states Li; she wants Bryant’s new data science-related labs to become centers of innovation and discovery, where students and faculty can explore the limits of the future — and transcend them
That’s exactly what the Bryant students in her class want as well. “I think what I’m most excited about is the opportunity to just dive right in,” says Margaret Sikand ’24, ’25MSDS, who studied actuarial science as an undergraduate at Bryant and is now looking to augment that math skillset with coding prowess.
Her plucky spirit is shared by her classmate Katie Farley ’25MSDS. “I think a big part of learning is just trying — and the more you mess up, the more you’ll learn to not make those same mistakes,” she says with a laugh.
The work can at times be difficult, and exacting, but there’s an unexpected bonus to the new Data Science lab that makes things a little easier, notes Sikand — one that goes beyond raw computing power. The large windows in the back of the room let in bright, warm sunlight and offer incredible views of the surrounding woods.
It can be a nice break to get a taste of the natural world when you’re concentrating on building a virtual one, she says.
This post was originally published on here
