What makes a data scientist?

It's a whole new ballgame for traditional data analysts, as training focuses on deep knowledge of statistics and computer science.

Universities Step Up

The skills required to perform these tasks cut across traditional academic disciplines, including statistics, mathematics and computer science. This is why several US institutions, including New York University and NC State, offer specialised data scientist certification and degree programs.

"Data used to be something you collected. It had neat rows and columns," explains Rappa. "You ran experiments that were time-consuming, laborious and costly, and you didn't have a lot of data so you dealt with sample sizes."

Now, in contrast, " data comes streaming off of every touch point you have with employees, partners and customers," he says. "Big data is about taking all of that data together and using it to optimise business or inventory levels or to better target customers. That's the trick of the whole thing. You need people who are good at handling large volumes of data and have knowledge of math and statistics to analyse the data."

Recognising this as early as 2005, NC State created the Institute for Advanced Analytics, which pulls together faculty members from various disciplines and teaches data science "in a very integrated way," Rappa says. Students take technical courses in statistics, finance and business, and they learn communications and teamwork skills, which Rappa says "almost always trump the technical skills," as far as employers are concerned.

Teamwork skills are critical, he says, because "you can't wrap up all of the [data scientist] skills you need in a single person." (See " Stalking the Elusive Data Scientist.") Instead, data scientists typically work in teams. IBM, for example, mixes statisticians with MBAs in its Data Analytics Center of Excellence, which helps businesspeople determine what questions they need data to answer. The centre's goal is to generate revenue through a marriage of business savvy and analytics, says CIO Jeanette Horan. One project optimised sales coverage in the 170 countries in which IBM operates, yielding a 10 per cent performance improvement in territories where the models were applied.

Rather than completing a final thesis, students work in teams to complete practicum projects with live data from major companies, including GE and GlaxoSmithKline. Seventy per cent of the program's students come from the workforce, many of them sponsored by their employers. Most students have at least two years of on-the-job experience, and their average age is 29.

At NYU, the newly launched, two-year master of data science degree is also multidisciplinary, intersecting mathematics, computer science and statistics. This is because to do data science well, "you need to have expertise in all three," says Roy Lowrance, managing director of the university's Center for Data Science.

Lowrance emphasises that data scientists also require what he calls "application knowledge." Without it, "you have no intuition about what to work on and test, especially in business," he explains.

What Lowrance refers to as application knowledge, some other experts describe as domain expertise. But whatever you call it, all agree that it's absolutely essential for data scientists in the business world.

Because data scientists are charged ultimately with showing business value, knowing a particular business is critical "because there's a lot of nuance in each domain", says Josh Williams, a data scientist at Kontagent, a company that finds and identifies customer behavioural insights from social, mobile and Web data in real time.

"A data scientist is someone who is familiar with statistics and classical mathematical analysis, and they need a strong background in programming and computer science or at least the ability to get things done in a programming language," Williams says. "But they also need domain expertise around how to apply different automated analysis algorithms to a given domain."

However, he adds, "data science skills are not necessarily industry-transferrable" because the volume and complexity of data varies from industry to industry. "We're dealing with orders-of-magnitude greater volumes, but the really important part is that the data is much more rich and complex," Williams says.

Follow CMO on Twitter: @CMOAustralia or take part in the CMO Australia conversation on LinkedIn: CMO Australia.

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