KSU: Strengthen Your Company’s Data Science Program
Companies need data science to turn their data into insights that drive better business decisions. But how do they hire the right people to create a world-class data science program?
I interviewed Jennifer Priestley, professor of statistics and data science at Kennesaw State University, for episode 9 of our Data Dialogues podcast. She is a pioneer at bringing data science programs to universities, and she shared her wisdom on how companies can work with universities to strengthen their own data science program. Priestley offered these three key takeaways for our listeners.
3 Things Companies Can Do To Build Their Data Science Program
1. Collaborate with Universities to Solve Business Problems
Priestley said universities are trying to pivot their curriculum to meet private sector needs, but schools need to partner with companies to solve real-world problems.
“I know everybody has openings for data scientists, and they’re having trouble hiring. Universities are working hard to make sure that what we’re doing in the classroom is relevant. But importantly, we can’t do what we do unless we’re partnering with the private sector,” she said.
She encourages companies to reach out to their local university to sponsor a project or capstone class. They can also provide de-classified data that represents a day in the life for their analysts.
Listen to the full interview now.
2. Mentor Students to Help Improve Communication Skills
Communication skills are a critical component of data science. Aside from the hard skills universities teach, students need help articulating their research for a non-technical audience. Furthermore, they need to be prepared to deliver a speech or write about their work for various levels of an organization. Priestley said companies can help students develop this soft skill through mentoring.
“From the CEO all the way down, you need to be able to communicate and throttle the depth of your messages in terms of computational complexity. You need to be able to throttle that based upon your audience,” she said. “It’s through conversations outside of just their faculty, actually working with people and talking with people who are practitioners of data science who can help them develop their communication skills, which is so critical.”
3. Understand the Differences Among Data Science Programs
If data science had a mascot, Priestly said it would be the platypus. A platypus defies traditional boundaries and classifications, just like a data scientist. A platypus is a mammal because it has fur and feeds its young milk, even though it has a duck-like bill, webbed feet and spends most of its life in the water. A data scientist crosses boundaries as well.
“To reiterate that point about interdisciplinarity, a data scientist is not a computer scientist, but they have to understand computer science. A data scientist is not a statistician, but they have to understand statistics. A data scientist is not a mathematician, but obviously they have to understand mathematics,” she said.
And when it comes to university programs, Priestly describes them as either a hub program or a spoke program.
“Just about every major university across the country has some type of initiative in data science. Some are going deeper into data science, really helping students become scientists of data and going deep into hubs. And then some are more aligned as spokes where the students aren’t necessarily going into the deep nuances of programming, but they’re learning how to work with black boxes. And they’re taking those results and then tying it back to the original business problem,” she explained.
It’s important that businesses investigate what kind of program a university has so they can set expectations for their collaboration.
Learn more on this topic by listening to episode 9, KSU: How to Strengthen Your Company’s Data Science Program or check out one of our other episodes.
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