When it comes to the educational foundations surrounding data science, the traditional focus has remained on the hard skills like computing and engineering. However, onstage Thursday at Gigaom Structure Data in New York City, AnnaLee Saxenian, professor and Dean of University of California, Berkeley’s School of Information Science, said that the educational aspects of data science are not only broadly incorporating social sciences into the curriculum, but they’ll also be valuable for more than just engineers.
“I am pretty confident that data literacy will become an increasing part of all curricula,” Saxenian said.
In addition, she explained that as more companies look to solve problems with big data, the data scientists assigned to the task will need so-called “soft skills” — communication and leadership proficiencies — to help translate the numbers into usable data for all.
“One of the things we keep hearing here and elsewhere is that by bringing the data into the organization and centralizing it, you’re going to change how that organization is organized,” she said. “You’re going to need the people skills.”
In addition to broadening skill sets, data science education is also known for its increasing accessibility through Massively Open Online Courses. Saxenian said that some MOOC participants may gain a value from pursuing a certificate program, but the nature of data science education (and the intricacies of programming) means that it could be hard to learn everything alone.
“I think there are some very self-motivated people who will learn skills that way,” she added. “But I think the record on those courses is that it’s hard to keep people engaged.”
To hear more of Saxenian’s outlook on data science education, including how Berkeley teaches its students, see the video below:
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Photo by Jakub Mosur