Messy Data and Polished Resumes: How GW Data Analytics Boot Camp Helped Stephen Powell Defy Convention and Land His Dream Data Job
Stephen Powell remembers his first daily stand-up meeting at a new startup. A large whiteboard stood at the front of the room, gradually being filled with lines of code and software engineering terminology. As black dry erase marker consumed the board’s remaining blank spaces, he felt lost.
Stephen had gotten his professional start at age 20, working in the retail sector at Verizon. His career with the company spanned 11 years as he matriculated his way up to government telesales, consulting with the Department of Homeland Security and Department of Justice, training B2B representatives, and serving as a client partner of national enterprise accounts.
After hitting the ceiling at Verizon, Stephen left for a startup. Just like that, he’d gone from the only job he’d ever known at a large, corporate conglomerate to a new role at a small company of maybe thirty people, most of them software engineers.
“I was 32, I didn’t have a degree, I didn’t want to circumvent the process, but I needed some kind of technical training,” he said. “And I didn’t have four years to get it.”
Stephen started taking online classes and acquired a few agile certifications, but he knew that wasn’t enough. After doing some research, he discovered GW Data Analytics Boot Camp — then applied, took the aptitude test, and got in.
A different route
Stephen didn’t follow a conventional educational path. He dropped out of high school, got his job at Verizon some years later, then received his GED at age 21. Today, Stephen finds that his lack of formal education has proved to be something positive. He knows who his competition is and what he needs to do to set himself apart, which has allowed him to work harder.
“I’m not formally educated and I’m Black. Those are two things that work against me,” he said. “That’s not a complaint I have — that’s just a statement of facts.”
The boot camp has turned Stephen into a lifelong learner intent on self-improvement. He even took a summer class at MIT recently because he felt he needed to do more.
“It’s not so much to get every certification,” he said. “It’s targeted. I know what I have to learn to stay on this path and get to the place I want to be. I don’t think I have an option other than to keep moving forward.”
Developing more than just data
During his time at Verizon, Stephen was able to rise in the ranks due to his experience, knowledge, and work ethic. Because of this, he never had to polish a resume and had little experience with professional interviews. “Through the boot camp, I probably learned just as much, if not more, about career development as I did about analytics,” he said.
As soon as Stephen got into the boot camp, he started filling his resume with relevant data science information. Soon after, he began receiving recruiting calls from companies. As a newcomer to data with no formal resume writing or interviewing experience, he interviewed for his first job — and bombed it.
“Naturally, I felt bad,” he said. “But I kept applying, because I knew at some point I was going to get another interview and I was going to be polished enough to handle the things that were coming my way.”
When he found GW Data Analytics Boot Camp, Stephen was 32, recently married, raising a 10-year-old, and working full-time. He had to learn how to budget his time in order to get the most out of the boot camp while balancing several other life factors.
From the beginning, Stephen identified his three top areas for improvement. First, he wanted to gain proficiency in the coding language Python. Next, he wanted to learn more about data visualization — when to use it, and how to best consider his audience while doing so. Finally, he wanted to build on his machine learning knowledge.
Today, he can happily say he achieved all three goals.
Short-term program, long-term payoffs
As a data analyst at MorphWorks, Stephen is responsible for modernizing IT infrastructure. He loves his new job, and says that his upbringing and role models helped shape his professional approach. His father was a blue collar worker, his mother was a veteran, and his grandfather was a Korean War veteran.
“I’ve had three pretty good examples of hard work,” Stephen said. “So when I call myself a data miner, that’s exactly how I approach my job — with a shovel. I enjoy getting as dirty as I can with messy data, trying to find insights and intelligence behind things, and challenging them.”
Stephen hopes to grow with the company —not just as a data analyst, but as a data steward. “I’m someone who cares about the structure of data and about what it means to the company,” he said. “If it only matters to me, it doesn’t matter.”
Although he never attended college, Stephen feels that the experience and knowledge he gained in the boot camp was just as valuable as if he had gone to one.
“I understand I didn’t go through a four-year program,” he said. “But I think you can learn a lot from someone who has gone through this boot camp, willing to learn and work at the same time.”