Helena Tan

How Investment Banking Data Inspired Helena Tan to Improve Lives

Senior Product Manager @ Niantic
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How Investment Banking Data Inspired Helena Tan to Improve Lives

Helena Tan

How Investment Banking Data Inspired Helena Tan to Improve Lives

Senior Product Manager @ Niantic

Helena Tan, Data Scientist

Here at Dataquest, we know a thing or two about trying to predict the stock market — it’s how our CEO and founder first got into learning data science. The stock market had the same allure for Helena Tan, Data Analyst at Fitbit, who became interested in data while working in finance and making investment recommendations.
In 2013, Tan was working in Silicon Valley with people she credits as the most brilliant data scientists and machine learning experts. This is where she learned that applied machine learning could bring tremendous value to their clients, so she decided to start using data to build products and solve problems to improve other people’s lives.
So, how did Tan go from investment banking to applied data science?

Based on my understanding, there are three major aspects of Data Science: programming skills, statistics knowledge and product sense/domain knowledge. Fortunately, I have a degree in Statistics, so I didn’t have to start from scratch on the statistics part. However, after I was inspired to use data to build products, it didn’t take long to realize I couldn’t go very far in pursuing my passion without picking up a programming language like Python.

Learning with Dataquest

And that’s when she discovered Dataquest. Only three weeks later, she took her first Python test as part of a technical interview for a job at Fitbit. Using what she had learned on the Dataquest platform, Tan passed the test and got the job. But she’s far from considering herself finished learning.

I am still in the learning process. There are so many interesting things to explore in this domain. For my own process, I enjoy learning by doing. It keeps me motivated. I have a list of product ideas that I feel excited about, so I go find the learning materials and pick up the techniques required to build them.

Tan goes on to say that while she finds experimental learning the most fun, she also sometimes likes to change it up and switch to a more structured learning process to make sure that she’s really nailing the theory behind applied data science. We agree. We’ve structured our modular courses at Dataquest so you can progress through them sequentially, or jump right in to learn something specific. The best way to learn is by doing — what you do and in which order is every learner’s decision.

Advice for Learners

And for those data science learners just getting into the field, or getting ready to start applying for jobs, Tan has some parting advice . . .

Data Science has become a very generic term. A data science role can vary from data engineer, machine learning engineer to business analyst. As a result, candidates nowadays need to spend more time to understand what type of problems they want to tackle instead of focusing on a job title.

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