Though some reports have suggested that data scientists still spend an inordinate amount of their time on “janitorial” tasks, “I don’t think of it as janitorial,” Long said. “I think of it as part of digging in and understanding it.”
As for the less exciting bits, “I prefer not to have to manage projects,” Long said. Doing so means “I often have to spend time managing everyone else’s priorities while trying to get my own things done.”
As for Rosario, who was trained in statistics and data science, systems building and software engineering are the parts he prefers to de-emphasise.
Preparing for the role
It is no secret that data science requires considerable education, and these three professionals are no exception. LatentView Analytics’ George holds a bachelor’s degree in electrical and electronics engineering along with an MBA, she said.
Rosario holds a BS in statistics and math of computation as well as an MS in statistics and an MS in computer science from UCLA; he’s currently finishing his PhD in statistics there.
As for MedeAnalytics’ Long, she holds a PhD in behavioural neuroscience, with a focus on learning, memory and motivation.
“I got tired of running after the data,” Long quipped, referring to the experiments conducted in the scientific world. “Half of your job as a scientist is doing the data analysis, and I really liked that aspect. I also was interested in making a practical difference.”
Next frontier
And where will things go from here?
“I think the future has a lot more data coming,” said George, citing developments such as the internet of things (IoT). “Going forward, all senior and mid-management roles will incorporate some aspect of data management.”
The growing focus on streaming data means that “a lot more work needs to be done,” Rosario agreed. “We’ll see a lot more emphasis on developing algorithms and systems that can merge together streams of data. I see things like the IoT and streaming data being the next frontier.”
Security and privacy will be major issues to tackle along the way, he added.
Data scientists are still often expected to be “unicorns,” Long said, meaning that they’re asked to do everything single-handedly, including all the coding, data manipulation, data analysis and more.
“It’s hard to have one person responsible for everything,” she said. “Hopefully, different types of people with different skill sets will be the future.”
Words of advice
For those considering a career in data science, Rosario advocates pursuing at least a master’s degree. He also suggests trying to think in terms of data.
“We all have problems around us, whether it’s managing our finances or planning a vacation,” he said. “Try to think about how you could solve those problems using data. Ask if the data exists, and try to find it.”
For early portfolio-building experience, common advice suggests finding a data set from a site such as Kaggle and then figuring out a problem that can be solved using it.
“I suggest the inverse,” Rosario said. “Pick a problem and then find the data you’d need to solve it.”
“I feel like the best preparation is some sense of the scientific method, or how you approach a problem,” said MedeAnalytics’ Long. “It will determine how you deal with the data and decide to use it.”
Tools can be mastered, but “the sensibility of how to solve the problem is what you need to get good at,” she added.
Of course, ultimately, the last mile for data scientists is presenting their results, George pointed out.
“It’s a lot of detail,” she said. “If you’re a good storyteller, and if you can weave a story out of it, then there’s nothing like it.”
IDG News Service
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