© 2017 by Doran Bae 

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I'm Doran Bae, Data Scientist @TVB turning data into products and stories. More about me.

My journey of becoming a data scientist

Updated: Dec 13, 2018

Becoming a programmer was never my dream. I got into this by accidents, and now I am committed to staying on this journey.

Before getting into data science, I was an analyst who glorified Microsoft Excel was the greatest invention human achieved in the 20th centuries.

On a UC Berkeley campus visit in 2017

Taking a sharp turn out of my comfort zone

In 2014, I had spent almost 5 years in the business analysis field. My career seemed almost stabilized, and I did not see much growth out of it. I decided to go for an MBA degree for the sake of a change of scene. However, I was quite apprehensive about that I would be back to where I am now even after earning the MBA degree (maybe few years faster). Then I came to learn about the new upcoming field called data science.

My first step towards getting into data science was looking for an opportunity internally. It didn't work out for me for a couple of reasons. First, there were not much data science initiatives happening within the company at that time, or at least in the offices in Korea. Secondly, as data science evolved as an extension to the engineering department in most of the companies, they could not afford to take a person like me with almost no programming background.

Beginning of a marathon race

I thought the best way to become a data scientist is to re-skill myself to suit the need of the industry. I applied to few graduate programs and got accepted to UC Berkely's MIDS (Master of Information and Data Science) program. Here, I learned how to program and was trained to question and analyze the data to solve business problems. In the summer of 2015, my life was set for a course that I did not know what to become.

Survive school

MIDS program was extremely challenging. I can call it the toughest challenge I had faced in my life up until then. However, in the end, it was the most rewarding challenge. The 1.5 year I spent at the program was a roller coaster ride for all my classmates and me. Everyone was juggling work, family, and most importantly, school. I attended weekly classes for 1.5 hours in the morning before I went into the office. I became known as the girl who brings a backpack to meetings. The quality of problems that we were tested on and the assignments and their deadlines caused us to spend sleepless nights. However, in the end, I survived and was ready to get my hands dirty.

Landing as a data scientist

As I was starting to look for an opportunity to work as a data scientist, I had two paths to choose from. One path was to work on the data analytics side. Transitioning from an analyst, this side seemed more comfortable, if not too familiar. The other path was to work on projects that build and ships data products for consumers. I decided that there would be a no better way to be trained as an engineer other than working as one of them. It turned out to be a better decision for me. Being able to use my skills to improve customer experience is something I am excited about.

What lies ahead

At work, I continue to focus on making good data models and good data products. Working closely with teams to realign the values between my team's and the stakeholder's is also part of my job. Outside of work, I am actively engaged with the data science community by writing introductory tutorials for young data scientists or talking to those who are considering a career change like I once was. I invest a good deal of my free time into keeping myself updated on the new trends in data science. Also, I am interested in developing a structured framework for machine learning model deployment.

Compared to where I started, I feel like I have come far beyond what I could have ever imagined. However, I know that this is a long haul and I am here to stay.