Hezekiah Branch, an undergraduate Cognitive and Brain Sciences major at Tufts University in Boston, had taken enough courses outside his major to know he liked both programming and statistics. But he was unsure of what sort of career those skills might speak to until a simple Google search pointed him towards the data science field.
“I was really just trying to find something that fit those skills specifically,” he said. And unsurprisingly, when he searched for jobs that make use of programming and statistics, data science came up frequently. “I started looking at the differences between data science, data analytics, and data engineering,” said Hezekiah. “Just a lot of Google searching and reading different articles, documents and white papers. That process is how I ended up finding out about Dataquest.”
Learning Data Science Online
As as current undergraduate, Hezekiah has access to programming classes at his university, and he said that he has enjoyed them. But while they’ve been excellent, Hezekiah was looking for something more specialized, and he wasn’t getting the data science know-how he needed from Tufts’ more broad CS classes. So he started studying data science online via MOOCs and other web resources. Eventually, he saw Dataquest mentioned in a Quora answer, and decided to give it a try.
Once he’d signed up, Hezekiah quickly worked his way through the free Python Fundamentals course and found that he enjoyed the approach. “I felt like the rigor and the concept was very, very similar to what I was already learning at Tufts, so I took that as a good sign,” he said.
“From there, I just started taking as many of the classes as I could. I started off doing the data analyst in Python track, and then when the newer courses in R were released, I started going through the R courses as well.”
Putting New Skills to Work
Armed with a solid grasp of data science fundamentals, Hezekiah then began searching for a technical internship. He didn’t find one immediately. His first internship, a 2018 stint at the Harvard Graduate School of Education, was non-technical, but he was still able to pick up some data-relevant skills on the job while working on something that truly matters: civic engagement.
His next role, however, allowed him to start making use of his newfound data science skills. At the South End Technology Center (SETC) in Boston, Hezekiah had the opportunity to work with renowned Civil Rights activist and MIT professor Mel King towards the SETC’s mission of improving the lives of Boston residents through free and low-cost technical training and other technology-related services.
“It was cool because I was able to use specific skills from Dataquest, such as working on data munging and the command line and creating visualizations and all of those skills,” he said. “I was using data that I was getting from the [South End Technology Center] site, which specifically worked with residents of the South End neighborhood of Boston and created social movements that create change in the city of Boston.”
As he was exploring summer positions after his time at SETC, Branch came across and applied for a variety of data-science-related positions, including data analytics and business systems job with Prudential. Knowing the interview process would be rigorous, he dove back into his studies.
“I just ended up going back through all of the notes that I had taken from the classes, and went through all the repositories that I had made from the courses, he said. “I was like, ‘I'll just see how it goes.’”
“It ended up working out really well,” he said. He got technical internship offers from Liberty Mutual and Prudential, and ended up choosing Prudential’s analytics role. “When I got the feedback, I was told that my application had stood out — especially when it came to the technical interview for data analytics.”
Branch was placed into one of the most difficult teams at Prudential. He has since focused on honing his data science skills and learning tools like Hadoop and Spark.
Advice for Data Science Students and Job-Seekers
Hezekiah has held three data-science related positions in a relatively short period of time, and he’s still an undergraduate. Clearly, he’s doing a whole lot right. What’s his secret to success? One thing, he said, is dedication and taking his time.
“I would definitely recommend not trying to just finish everything as fast as possible,” he said. “Even if I finished a mission on Dataquest, I would always go back through. And if I couldn't do it without looking at the hints, or without having to re-reference something multiple times, then I knew that I didn't really know it.”
Another tip: if you want to learn, teach. “It's helpful for you not only for you to learn it, but to show other people how to learn it, too,” Hezekiah said. Whether you can explain things “without using the statistical, mathematical jargon” is a great way to test how well you really understand what’s going on. “To be able to break it down into plain English and be straightforward with the conclusions and the results is a huge skill.”
His advice for the job search? Don’t expect it to be easy, and don’t give up. Hezekiah estimated that for each internship offer he’s gotten, he’s applied to 50 places that said no or simply didn’t respond to his application. If he didn’t get an offer, he’d ask for feedback and apply it to the next application or round of interviews. He also suggests being open to “stepping out of your comfort zone to go somewhere that you may not have pictured yourself going.”
“Just being flexible in that process and giving yourself a chance to grow is just as important as what you're learning,” he said.
If you have the drive, we have the courses that will teach you the skills required for a data science career. Check out our free Python Fundamentals course and free Introduction to Programming in R course to get started.
Photo credit: Kyle Lui (IG: @lei.visuals)