Colleges incorporate data science into curriculums

Colleges have noticed the strong interest companies have taken in data science and are incorporating the field into their computer science curriculum. Here are a few examples of the many data education efforts being made at universities.

Related: Big data worker shortage demands job candidates with diverse backgrounds

University of North Carolina, Charlotte

Two years ago, the university's College of Computing and Informatics and Charlotte businesses formed the Charlotte Informatics Partnership to determine how data science fits into business operations, says Yi Deng, the college's dean.

"Data is not an issue," he says. "It's the information and the insight embedded in the data that really are the issue. And those factors are becoming a principal drive for all industries. But if you look around the country, very few universities have the programs in these areas."

The public-private partnership aims to develop programs and practices that address corporate data science needs while turning Charlotte into an informatics hub. Understanding the connection between data and business requires a broad view, says Deng. To address that, the partnership will explore how data science fits into a firm's culture, investments and employees, in addition to what education the workforce requires. The input from executives of Charlotte-based companies, which include Bank of America, home improvement retailer Lowe's and utility provider Duke Energy, was used by the school to help craft its data science curriculum.

So far, the partnership has yielded master's and doctorate programs in bioinformatics, which was first offered two years ago. This fall the college launched a professional science masters in health informatics. In the next one to two years, the college will tackle the business and finance field by rolling out a professional science master's program in business analytics and informatics, created with the university's college of business, and an undergraduate financial services informatics program.

The college opted for this track since industries "across the board" have said that they need employees with a deep knowledge of analytics who also understand management concepts, Deng says. "It's extremely rare to find people who understand both. The goal is to train people who are technical but who can also lead, manage and work with others."

Though informatics is known in health care circles, the term remains "somewhat unfamiliar" to students who may also be unaware of the field's career opportunities, Deng says. Given enterprise IT's strong interest in big data, he expects this to change.

"Some communication needs to be done in certain areas. But with the recent publicity, those things will spread very quickly."

Northwestern University

Northwestern University in Evanston, Illinois, entered the data science education field this semester with a full-time, 15-month master of science in analytics program offered at its McCormick School of Engineering and Applied Science. Interest in the program proved strong and attracted hundreds of applicants for only 30 slots, says Diego Klabjan, the program's director and an associate professor of industrial engineering and management sciences.

"The goal is to have a highly selective, high quality program so that's why we're not going for numbers," says Klabjan. "We don't want to have 60, 70, 80 or 100 students."

Ideal candidates hold a bachelor's degree in an analytics-related field, such as statistics, computer science, economics or industrial engineering, and have between two and five years of work experience in analytics, he says. Most of the 33 students in the first class meet these parameters, says Klabjan, adding that there are exceptions in the group such as recent college graduates and IT professionals with more than a decade of work experience.

The university went with a master's program because analytics is a professional method of science and an undergraduate degree delivers the required foundation, says Klabjan. Teaching the fundamentals, like basic data modeling or statistics, would require a program longer than 15 months, he adds.

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