Back in October, our Chicago Career Advisor wrote a blog post on why The Windy City is a viable, vibrant place to consider launching a data science career.
In it, she writes:
“If I asked you to imagine a bustling tech and data science scene, your mind might wander to the robust startup culture of San Francisco, or Amazon’s home base in Seattle. You may even jet set over to the East Coast and think of New York City. And you wouldn’t be wrong! All three have significant tech and data science communities offering a ton of job opportunities. But one city you may not picture is Chicago – and you really should!”
Bootcamp grad Chris Gillespie is a Chicagoan through and through. He got a B.A. in Economics from Northwestern, attended our bootcamp in the city, and now works there as a Sr. Analyst for United Airlines. He agrees that Chicago is often overlooked when it comes to what’s considered a data science city. To a certain degree, he understands why.
Based on his own experience, plus those of his fellow graduates, it seems that many companies in Chicago are awake and alert to the value of data science, but they may be in earlier stages of implementation by comparison to other companies in other cities. Depending on your perspective, that can be a good thing.
“That's certainly a challenge, but it is also one that I was happy to walk into, because it gives me exposure to a lot of different elements from data engineering through data science,” he said. “I can learn and be a part of something that's developing. There's definitely a market here in Chicago – and a strong one at that.”
In his current role, he’s exposed to a wide range of data sets large and small, as he works with multiple departments on analytics projects and more.
“The data runs the gamut from e-commerce on the customer side of things, to our actual operations like aircraft maintenance and the installation of parts, keeping everything running on a day-to-day basis,” he explained.
In his first 10 months on the job, he’s worked on models looking at utilization metrics, and has worked significantly in PySpark to search for data, perform data munging with large data sets, and to clean and organize it all so the team can perform historical analysis and predictive modeling.
Before attending the bootcamp, Gillespie worked in healthcare consulting focused on specialty pharmaceuticals, trying to implement programs for insurers. In the process, he was introduced to a lot of medical data, including projects around financial forecasting.
“I realized that I really enjoyed that modeling component, working with data, and trying to support a new initiative with it,” he said. “Yet, my career was heading toward more of an account management role and away from the technical side of things.”
In order to take control of his future and steer it in a more technical direction, he started looking into data science bootcamps. He happened to know someone who graduated from our very first cohort in Chicago, and after speaking with her and attending some onsite events, he decided it was time to apply.
While a student, he focused on using his developing skills to build a portfolio filled with projects of topical interest to him personally. The idea being that the more you care about a given topic, the more likely you are to care about the project results and do whatever it takes to reach them.
Among the five projects he completed, one predicted the salaries of starting pitchers in Major League Baseball with a focus on his beloved Chicago Cubs. For his final capstone project, he wanted to try his hand at unsupervised learning and a recommendation system seemed like a good way to do so. Being a podcast lover, he created a podcast recommendation engine.
“It was a perfect marriage of something I thought could be applicable in a career and something that was really interesting and close to me,” he said. “I wound up with this finished product that was beyond anything that I thought I could have accomplished prior to the bootcamp. It was really a great experience.”
Gillespie was hired at United Airlines within a month of completing that final project. In large part, he credits the Careers Team at Metis for its thorough approach to providing tips and guidance throughout the bootcamp. But he also notes that as a bootcamp student, it’s about what you personally put into the career development process, which can be a hard thing to prioritize when concurrently learning many difficult data science concepts.
“I think it’s something that should be paid attention to during the course of the bootcamp, because a lot of it comes down to what you put into it,” he said. “I think I benefited from not waiting until the bootcamp was in the last week, or over, to really start working on implementing their advice. I started incorporating the advice early on and I think it paid dividends.”
Interested in reading more about the student/alumni experience? Check out more stories here.