To highlight how Dataquest has changed people's lives, we've started a new blog series called User Stories where we interview our users to learn more about their personal journeys.
In this post, we interview Helena Tan, Data Analyst at Fitbit. Helena worked in a variety of roles in private equity and investment banking after graduating from the University of Waterloo. She wanted to become more technical, and work on more challenging problems. She started learning Python and data analysis using Dataquest. Her progress has been incredible, and she landed a job at Fitbit soon after she started learning. She's been continuing to learn on Dataquest, and is continually improving her skillset. She's very passionate about sharing what she's learned, and recently presented at the Grace Hopper Conference.
You were working in investment banking before. What got you interested in data science?
I have always been passionate about using data to tell a good story. Back in my finance career, I loved coming up with investment recommendations based on industry trends and company specific financial data. I moved to Silicon Valley in 2013 where I got the opportunity to work with some of the most brilliant data scientists and machine learning experts as a Business Intelligence Analyst at a startup. At the same time, I had a chance to witness how the company applied machine learning to bring tremendous value to its clients. As a result, I became very inspired to use data to build great products and solve problems that make a difference to people's life!
How did you start learning 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. That's how the learning journey started and that's when I discovered Dataquest!
As you were looking for jobs, what would you say was the single most important thing that helped you get interviews?
If I had to summarize it to one word, it would be "persistence". I don't remember how many jobs I applied before I landed my first job in Silicon Valley, but I know it was a lot! Each data science job is unique. It varies by the team, company and industry. It takes time to find a good match among one's skill sets, career goals and the requirements from the role/team.
You've been working at Fitbit as a data analyst for a few months. How did you build your skills to the point where you could get hired?
As I was on my journey to pick up the programming skills necessary to create data products, the Fitbit opportunity came up. I still remember my first round of technical interviews with Fitbit. It was my very first Python programming test and it happened only 3 weeks after I started the Python course at Dataquest! Luckily, I was able to apply what I learned at Dataquest at the interview and eventually got the job.
I don't have a definite answer in terms of the minimum level of skills. It depends on the team, company and one's past experience. For someone who is completely new to the tech industry, I would suggest they start with learning SQL and make sure to demonstrate strong quantitative analytical skills during interviews.
What are the technical interviews for Data Science jobs like?
Technical interviews for Data Science jobs can have different formats depending on the team. Common formats include real-time coding, case studies and take-home projects. For real-time coding and case studies, candidates are often given some simple data sets and hypothetical scenarios, then they are asked to work through a problem using their preferred coding language in the interview. The take-home projects are usually more comprehensive. Candidates are given a more complex set of data and they are often asked to discover insights and build predictive models based on the data.
For people who are trying to break in the field, the first couple of technical interviews could be hard, but with a lot of practice, they get easier!
If you could start learning data science again, would you change anything in the process?
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. I find learning this way most fun and enjoyable. However, sometimes I need to switch back to a more structured learning process to make sure I understand the theoretical concepts behind the applications.
What are the biggest misconceptions about learning data science and getting a job?
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.
What are you most excited about learning in 2016?
I am excited about improving my skills to do two things: process and analyze more open data from the web, and visualize the results in an interactive fashion. I think Dataquest got me covered! :)