Emerging field has huge potential for college and university curricula

By Bridget McCrea
August 31st, 2015

Data analytics is gaining traction as a new career option for college graduates. Here’s how one institution is grabbing the opportunity and helping students prepare for jobs in the field.

data-big-analyticsIn the data information age, both businesses and higher-ed institutions are scrambling for those trained in making sense of big data analytics. But what are the skillsets today’s students need to know, and are colleges and universities able to provide this training?

With huge amounts of data being spawned by modern-day technologies, social media platforms generating an equally large amount of information, and companies scrambling to turn mounds of data into useable intelligence, it was only a matter of time before the field of data analytics became a “real thing” for the world’s higher-ed institutions. Largely driven by the growth of “big data” – or, the extremely large data sets that are analyzed to reveal trends, patterns, and connections – institutions are turning out a new breed of data scientists trained on the fine points of “crunching” data and turning it into actionable information.

“Organizations recognize the business value in using tools to get actionable and useful information out of large piles of unstructured data (loosely defined as the information that’s not stored in a database format),” says Rob Reed, education evangelist at San Francisco-based Splunk, Inc., which offers a software platform for real-time operational intelligence. “But because the technology came from industry – and not from computer science or database researchers – there’s a gap between what students can learn at a university versus what’s actually useful to business and industry.”

Reed—who is charged with figuring out how to link what companies would like students to know upon graduation with what those pupils are actually learning in school—recently conducted a 6-hour-long career fair at San Jose State University. The divide was painfully obvious.

“We talked to about a hundred students,” he says, “and maybe two of them were able to have an intelligent conversation about conducting analysis on unstructured data. I see that as both a deficit and an awesome opportunity for schools.”

Stepping Up to the Plate

When the Department of Applied Mathematics at the University of Colorado Boulder started noticing more alumni—Ph.D.s, masters, and undergrads—going into data analytics-type careers, Associate Chair Anne Dougherty noticed the same opportunity as Reed.

“Some of them were working for small startups in the Boulder area, others were starting careers at larger firms, and one got a job doing data analysis for Twitter,” recalls Dougherty. “If this was going to be a big field and an up-and-coming area, we knew we could support it by training students on the technical side.”

With a solid math, statistics, and computer science curriculum already in place, the department developed a new statistics minor that includes several brand-new classes in the subject area, plus several existing courses that we revamped to better serve those employers looking for “data scientists,” or those individuals who combine computing science and applications, modeling, statistics, analytics, and math to discover insights in data.

The courses, which are available at both the bachelors and graduate level, incorporate high-level mathematics, statistics, statistical analysis, and computer science.

“I think of data analytics as an amalgamation of those areas,” says Dougherty, whose department received an Expeditions in Training, Research, and Education for Mathematics and Statistics through Quantitative Explorations of Data (EXTREEMS-QED) grant from the National Science Foundation (NSF) in 2014. The grant is being used to change the department’s curriculum, add new courses targeted toward data analytics and analysis, and to involve undergraduate students in related research opportunities.

Specific to curriculum changes, Dougherty says UC Boulder has rolled out the statistics minor, for which calculus III is a prerequisite course—illustrating just how mathematically-advanced the core coursework is. Now, Dougherty says the school is developing a data analysis class and a data analytics certificate program that will be “most relevant for students who are majoring in either computer science or applied math.” The latter will also take specific data engineering courses in computer science to learn the technical ideas behind storing, retrieving, and processing large data sets (vice-versa for those students majoring in computer science).

In assessing the various tools that UC Boulder has used to create and administer its new analytics-related coursework, Dougherty says her department uses various software platforms, including Python, MATLAB, and R, the latter of which is a free statistical programming language. “R has been used by statisticians for many years and it’s in the public domain,” says Dougherty. “Over the last 3-4 years, we’ve started incorporating a lot of it into our statistics classes.” In 2014, the school moved to using R in its first introductory probability and statistics class. “We’re now seeing the R programming language percolating throughout our offerings,” she notes.

Measuring the Internal Benefits

As UC Boulder continues to hone its data analytics offerings and develop more data scientists, it’s also benefitting internally from the effort. In fact, Dougherty sees the research aspect of the grant providing solid value for the university itself, aside from helping corporations fulfill their individual needs for data scientists.

“As our students become better trained, they’re able to bring that to bear on the research projects that they’re doing with faculty at the university,” says Dougherty. Since receiving the grant, for example, she says students and faculty have submitted at least one related journal paper and another is in the works. “We’ve also had students and faculty travel to conferences,” she adds, “where they presented together on data analysis-related topics.”

Reflecting on UC Boulder’s progress in the data analytics realm, Dougherty says that the biggest challenge by far has been getting everyone on the same page in how they think about and define terms like “data analytics.” The business and marketing school, for example, thinks of the concepts in a different light than, say, the computer science or math departments would look at them. And while the geography department is doing “tons of data analysis right now,” according to Dougherty, its definition of that term is probably very different than that of the school’s electrical engineering department.

“As a field, this is really still in the early adolescence phase,” says Dougherty. “Culturally, trying to pull all of these groups together—or, maybe they shouldn’t even be pulled together—is an issue. We don’t really have the answer to that yet.”

What Dougherty does know is that with technology evolving at the speed of light, it’s not enough to simply develop cutting-edge curriculum and hope that it stays relevant. Like any subject matter, the data analytics portion also needs to be honed and updated, perhaps even more frequently than more traditional subjects.

Going forward, she says UC Boulder plans to offer more related coursework and research project opportunities for students within the next year. “Longer term,” she adds, “we’ll be broadening the discussion of data analytics on the campus, and trying to address some of the cultural issues surrounding that.”

Bridget McCrea is an editorial freelancer with eCampus News.

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