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Demystifying Data Science: An 'Aha' Moment That Shaped A Career
By Emily Wilson • October 12, 2017
During a conversation with a former colleague, Lillian Tong had an 'aha' moment. As they discussed machine learning and how it could be applied to the work being done at their company, she felt a surge of interest and engagement that she hadn’t experienced in regards to any of her previous work. She took it as a sign.
“That was the moment when I felt that maybe I should be doing something different with my career,” she said.
She’d previously earned a bachelor’s degree in bioengineering from the California Institute of Technology and was working in the field of biotech and neuroscience. But based on her newfound interest in machine learning, she felt compelled to dig deeper into the topic, and that excavation led her to data science.
“I actually didn't realize what the data scientist job title meant until I was looking for ways to get more involved with machine learning and stumbled upon data scientist job descriptions,” said Tong. “It sounded like something that would be a really great fit for me, which is why I decided to pursue it all the way.”
She went the route of the data science bootcamp, attending Metis in San Francisco. Her final project focused on creating a model to identify the type of food causing a person’s food allergy based on their experienced symptoms. A few months after graduation, she accepted a position as an Associate Data Scientist at Envestnet | Yodlee, a leading data aggregation and data analytics platform powering innovation for digital financial services. Though her final project has no direct correlation to the financial services sector, the process of seeing a project through from start to finish helped her land and prepare for her current role.
“[The project provided] excellent talking points that could be used in the interview process and provided me with real examples of what I am capable of in data science,” she said.
Now she has been with Envestnet | Yodlee for four months, where she’s getting access to high impact projects that she describes as fast-paced and fulfilling. But being new to a job, and new to an entire industry, can come with challenges. Tong’s main challenge so far is closely tied to an important topic we cover in the bootcamp – imposter syndrome.
“One of the biggest challenges for me is speaking up in meetings and voicing my opinions,” she said. “Given my unrelated background to the financial sector, I sometimes I feel like I’m not understanding something to the extent I need to in order to have an opinion about topics we’re discussing.”
But then she starts talking about the projects she’s working on and how she’s becoming the spark expert on the team, and you get the sense that she’s an exact right fit for the tasks at hand.
“One of the projects I've been working on is scoping out how we could migrate some of our work to a Hadoop/Spark infrastructure and setting it up. It's been fairly challenging, but I've learned so much in the process,” she said. “It's been the first, big independent project I've been assigned to do and I'm looking forward to being the ‘spark expert’ and the person of contact on the matter.” _____
Interested in learning more about the Metis Data Science Bootcamp? Check it out!
In a Q&A session with Course Report, Metis Live Online Bootcamp graduate Anupama Garla shares her experience with the online classroom and learning style of the bootcamp, her advice for other career-changers, and her plans to innovate the world of architecture now as a data scientist.
In our latest Made at Metis post, read about two projects created during the bootcamp, including a tool for homeowners to determine if building a backyard home makes financial sense and a live local music recommender.
This post features two projects from recent graduates of our data science bootcamp. Take a look at what's possible to create in just 12 weeks, including a project to leverage a user’s existing street art preferences to recommend visually-similar fine art and a project to develop a collaborative filtering recommendation system using sales transaction data.