On September 25th, faculty and staff from Computational, Mathematical and Physical Sciences (CMS) held a kick-off event to bring awareness to AI and data science programs at the new Eyring Science Center Annex.
Recently, three new majors have been developed in the CMS department, allowing students to dig deeper into AI and data science. The kick-off event allowed students to talk to professors and current students to discover which major or small routes were best for their interests.
Tyler Jarvis, chairman of the CMS Committee on AI and Data Science, explained that there are five majors in AI, data science and machine learning.
“People want to do AI, but they don't know which one to do. So when hope comes here, you can see which of these five is best for your real interests and long-term career goals,” says Jarvis.
Jarvis explained that BYU should be the place where AI is used to make the world better and promote medicine and learning.
He explained that it should be a place where students learn to use AI.
Daniela Ainz, president of BYU's Data Science Association, said she is working to build a community for data scientists and others interested in the program by hosting events like this kickoff.
They have workshops on a variety of topics within data science. This helps students build understanding of the field along with networking events.
“Our ultimate goal is to build future data scientists,” Ainz said. “AI needs data science to work, and data science is part of what goes into AI.”
David Wingate, a faculty member in the Computer Science department, teaches machine learning classes related to AI development.
“Machine learning is building predictive models that power AI. So machine learning is building something like CHATGPT, building autonomous cars, building ALEXA, building Siri. Machine learning is the science of finding patterns in data and using those patterns to solve new problems,” Wingate said.
Wingate also explained that machine learning majors will think a little more about how data can be realized and create scalable models of the algorithm.
Shannon Tess, a faculty member in the Statistics department, said the data science program is not as deep as statistics and computer science majors do, but he teaches students a bit about everything related to data science.
“Modeling, evaluation and interpretation [within data science] “It's like machine learning,” Tess said. “It's part of modeling, but there's exploration and data visualization. It's like the part that's sometimes overlooked.”
Tess explained that it is important to learn to understand data, explore it, and communicate it. This can be overlooked, but it is also part of the overall process of data science.
