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Academia to Data Science: Where Does a Bootcamp Fit into the Equation?

By Emily Wilson • July 05, 2018

Photo by Nick Morrison on Unsplash

To be admitted into our data science bootcamp, you don’t need a specific level of education. However, many students come in with advanced degrees, whether Master’s or Ph.D.s, and each brings with them a certain level of expertise related to their area of study. Many of these students choose the Metis project-focused curriculum as a way to bridge the gap between the theory-heavy rigor of academia and the practicality of industry experience. Their varied interests, passions, and experiences help create and foster a rich, unique environment, which is never exactly replicated from one bootcamp to the next.

For Metis graduate Dangaia Sims, that passion is kinesiology. She earned a Ph.D. in the subject from Pennsylvania State University before applying to our bootcamp because she didn’t have the exact right blend of theory and skill necessary to move her career in the direction she came to desire.

“As part of my background, I had some statistical knowledge but very little background in coding. I had some, but not to the degree that others in my cohort did,” she recently wrote as host of a live online Ask Me Anything session about her career trajectory.

For her final bootcamp project, Sims, who’s now a Sr. Data Scientist & Strategy Consultant at IBM, combined her new skills with her educational expertise to create Yoga Vision.

“My background is in kinesiology and I am a former yoga instructor, so it was super cool to be able to combine my old and new worlds. I used computer vision and a neural network to determine if yogis were in their pose correctly and provided them feedback on their form,” she wrote. “I talked about it a lot as part of my job search. People found the juxtaposition of the two fields to be super interesting.”

For bootcamp graduate Summer Rankin, who earned a Ph.D. in Complex Systems and Brain Sciences from Florida Atlantic University, thinking about that job search was a significant factor in her decision to attend a bootcamp. She wanted to transition out of academia and into industry but wasn't sure how to go about it on her own.

“I had no idea how to structure my resume for industry, no contacts in industry, and I didn't even know that there was a whole interview process. You could say I didn't even know how little I knew about it...If I may put it bluntly, I thought [Metis] would be invested in whether or not I got a job after the bootcamp,” said Rankin, now a Data Scientist at Booz Allen Hamilton.

Throughout the bootcamp, while simultaneously enhancing those industry soft skills and building a job-ready project portfolio, Rankin came to the conclusion that an academic background isn’t necessary to bootcamp success but is certainly helpful.

“The bootcamp was like squeezing a year of grad school into 3 months. It reminded me a lot of those late nights in grad school before a project is due or before a conference poster gets printed, when you work your tail off with your friends by your side doing the same thing,” she said. “An academic background was very helpful because I was used to sitting and figuring things out for myself. That's an important skill to have as a data scientist. It also taught me to be skeptical, and to triple check things, especially code, stats, and technology in general.”

But there was something she didn’t obtain during her time in academia, aside from knowledge of the industry job search – a wider range of the hard skills necessary to make the transition.

“It's very common that we academics know a lot about one thing. I did a lot of data analysis in academia, but it was very specific to my topic of auditory signals,” she said. “I knew that there were many techniques in machine learning that I had not used in my academic life and I wanted some hands-on instruction as to how and when they should be used and the math behind them.”

A new report from the National Academies of Sciences, Engineering, and Medicine touches on this need for a wide range of skills, noting that data science “draws on skills and concepts from a wide array of disciplines that may not always overlap, making it a truly interdisciplinary field.”  The report advised that “students in many fields need to learn about data collection, storage, integration, analysis, inference, communication, and ethics...Many skills that often are not fully developed in traditional computer science, statistics, and mathematics courses are crucial in the education of future data scientists.”

In this way, a bootcamp is uniquely suited to the time and need. At Metis, the focus is on the big picture, applying theory through a project-based curriculum, representing a symbiotic relationship between bootcamps and traditional academia, which are too often seen as direct competitors instead of existing in tandem to create well-rounded opportunities for those entering the field.

Metis Sr. Data Scientist David Ziganto – who recently spoke on the topic of bootcamps and traditional educational routes at the National Academies of Sciences, Engineering, and Medicine’s Roundtable on Data Science Postsecondary Education – explained how Metis and academia work together.

“I like to think of our relationship with academia like this: Academia gives you the tools; Metis shows you how to use them. We each have our part to play,” he said. “If you ever walked away from a university math class wondering what the heck you're supposed to do with what you learned, know that you're not alone. It's actually quite common. The good news is that you actually learned something valuable. The bad news is that you haven't seen the true power behind it. For this reason, Metis specifically designed its bootcamp to strike the right balance between theory and practice.”

That latter part is of particular interest to many in academia because “practice” equals projects. Bootcamps often focus more on practical portfolio-building than traditional academic programs and work to create dynamic environments that allow students to learn holistic approaches to real-world problems.

“Academia tends to isolate topics. You may take a course in databases and complete a project. You may take another course in predictive analytics and complete another project. Rarely, though, is there an opportunity to integrate all the pieces. So students walk away from these courses with deep technical knowledge in a number of tools or concepts but lack the real power that is unlocked only when one realizes how to connect them,” said Ziganto.

For graduate Chris Murdock, the idea of working on a range of projects was a huge draw when considering a data science bootcamp after earning a Ph.D. in Chemistry from the University of Tennessee and working in the field for a few years.

“As a Ph.D. chemist, you become very specialized, which limits the types of places where you can become employed. This is what drove me to look into data science and pursue a bootcamp. After my last job as a senior chemist, I felt as though it was time to take the opportunity to use my science background and learn new skills at a data science bootcamp. I believed it would allow me to move where I wanted and work on projects that I never thought possible - and this is exactly what happened after graduating,” said Murdock, who’s now working as a Data Scientist at Solera.

Though he's sure to note that, especially when it comes to his experience studying chemistry, “academia goes beyond textbooks and involves years of one's own personal research in the field,” he said that he selected a bootcamp over a data science master's degree program because he “wanted the experience applying knowledge to personal projects rather than just in-class assignments.”

Time and time again, we hear student feedback like this, reinforcing our philosophy that projects enable quick but deep learning and increase chances of job readiness and success. We’re proud to offer such a robust and evolving curriculum, accredited by the Accrediting Council for Continuing Education & Training (ACCET) so that we’re kept accountable to each and every student.

For more, check out some examples of recent student projects - portions of portfolios that helped each get jobs in data science:

Along with the 12-week bootcamp curriculum, we offer a coinciding 12-week careers curriculum, which covers topics like resume building, networking, interviewing, and more. See both here by scrolling down the page.


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