3 Successful Data Science Students Tell You What It Takes To Become a Data Scientist

Published: Apr 07, 2017

3 Successful Data Science Students Tell You What It Takes To Become a Data Scientist April 6, 2017
By Mark Terry, BioSpace.com Breaking News Staff

The already white hot field of data science got a big boost this week when Tainer Halicioglu, Facebook’s first employee, and now a co-founder and partner in Seed San Diego, gifted the University of California San Diego with $75 million. As part of a UCSD $2 billion project, his $75 million gift is earmarked to train students and support faculty at the newly created Halicioglu Institute of Data Science.

Data Science is a no doubt a hot new area, a multi-disciplinary field that tries to take massive amounts of data and sort, analyze and understand it. Harvard Business Review, in 2012, dubbed it “the sexiest job of the 21st century.”

Data science is big now in many areas, but growing every day in the life sciences and biopharma—making sense of vast amounts of clinical, molecular and genomic data. But not all universities have specific Data Science programs, although, as the UCSD initiative demonstrates, that is changing.

BioSpace talked to three data scientists about their education and the field of Data Science.

Samara Bliss is currently working as an Offering Manager at Watson Health, which she says is similar to a product manager. Bliss initially planned to go to medical school. She received a Bachelor’s Degree in Neuroscience from Columbia University, spent a year conducting research with neurosurgeons, and then began medical school in the fall of 2014. But she realized that clinical practice wasn’t where her heart was; she was more passionate about technology and data.

After discussing various options, she chose to attend the 12-week bootcamp at the NYC Data Science Academy. “It’s expensive,” she told BioSpace, “but if you consider a Master’s degree is about $50,000 per year, it’s clearly worth it.” Tuition for the NYC Data Science Academy is $16,000.

The bootcamp runs 12 weeks, 12-hours days, five days a week, and covers topics such as Data Analytics, Machine Learning, Python, Big Data, Linux, Git, Bash, SQL and others. It also includes four to five independent projects. Bliss provides an example: “I utilized APIs from the New York Times. It allowed us to scrape through the New York Times by author and analyze the content for things like attitude, for example—some writers take a really dark approach, etc.”

Nick Vogt’s path is a little different. A graduating senior at Michigan State University, he is also the vice president and co-founder of the MSU Data Science Association. Michigan State University does not have a specific data science program yet. Vogt started in economics as his major. “I wanted to do behavioral economics. I thought it would be really cool to be able to predict human behavior, or to at least understand it,” Vogt said.

But as he got deeper into the field, he found he really enjoyed the programming and data monitoring more than the theory. From there, he sort of fell into data science, although he noted, “It doesn’t hurt, either, that data science is very well marketed. I think a lot of data scientists out there market it really well as the sexiest job of the 21st century.”

Without a specific data science program, Vogt is wrapping up two Bachelor’s of Science degrees in Economics and Computational Mathematics. He’s already secured a job with the Ford Motor Company as a data scientist.

Vogt formed the MSU Data Science Association with Adhi Rajaprabhakaran. He notes that the group has attracted students from a number of different departments.

“Since people started to figure out who we were, we get a lot of PhD economics students and business analytics,” Vogt said. “Take that with a grain of salt, though. When we started we wanted to get the word out in the departments that supported us, which was math and business and economic—we had really close ties with them. The math, science and engineering departments are just starting to get their own data science major going, so a lot of them are looking at related coursework.”

His position at Ford will “be principally working in marketing challenges.”

Brian Saindon, like Samara Bliss, is a graduate of the NYC Data Science Academy. He earned a Bachelor of Science in Health Science and a Master of Public Health, both from Boston University. He told BioSpace, “When I went into grad school, those classes were more targeted towards public health or population health, where I picked up study design, biostatistics. That’s where I got more interested in the analytical science side of health informatics or health analytics.”

He worked for about 18 months as a Health Analyst for Predilytics, then took the 12-week bootcamp with NYC Data Science Academy. “I saw a broader need for some of the harder code skills in a variety of languages,” Saindon said. “I didn’t want to be married to one language. I wanted a more general knowledge of the different types of machine learning techniques that are currently being used. Basically, I saw the data science bootcamp as a way I can gain a bit more hard skills or programming skills as well as a broader knowledge set of different types of machine learning, models, algorithms that I could use with those additional languages that I gathered from the bootcamp.”

As opposed to a Master’s program, he noted the bootcamp was a way to gain some of the hard skills and programming languages quickly — in a very hands-on, practical way. “Rather than have to enroll in a year or two-year Master’s program or a six-year PhD program, I knew specifically what I wanted. It was clear from the NYC Data Science Academy that I could get those skills,” Saindon said.

He also monitored various job boards, looking to see what different data science jobs were looking for. “I’d seen that the Data Science Academy had the answers—or it packaged the skills training well—and I was confident that I would get those additional skills that were needed for different roles,” Saindon said.

Saindon is now a Data Scientist with Prognos. “We work with laboratory data,” he said. “We’re working in different verticals to penetrate different industries—the pharma industry, the health insurance industry — but essentially our main focus is laboratory data. I will typically work on some of the R&D-type project to identify a particular business problem that would require some type of advanced analytics or machine learning technology.”

At the moment, it’s clear that there is no single path toward becoming a Data Scientist. Some in the field have graduate degrees in mathematics, statistics or computer science. Others are coming from other fields, including economics, engineering, epidemiology or others. In biopharma, it’s been observed that many people have backgrounds in molecular biology, biochemistry, or genetics and genomics, and then take additional training in data science in order to apply it to their specialty.

And these are good jobs that are worth the tuition for a 12-week course. According to Glassdoor, the national average for a Data Scientist is $113,436, with $76,000 at the low end and $146,000 at the high end.

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