Alumni Spotlight: Isaiah Yoo – Healthcare Analytics By Way of String Theory Physics

I did my PhD in the area of string theory, which is a more mathematical area in physics. I don’t think the specific focus of my PhD directly contributes to my being a great data scientist, but I think the general nature of having gone through a quantitative PhD does.

For one, doing my PhD taught me how to ask good questions. Questions that are useful and interesting, but at the same time answerable with a reasonable amount of effort. This is an important skill to have as a data scientist, because given a dataset, there are plenty of uninteresting questions you can easily answer, but answers to more impactful questions are often harder to get at.

In doing my PhD I also learned that getting to a nice result is often a matter of being persistent and following through with a lot of tedious work. This is also true in the context of data science. Real-world data is often messy, and getting to a nice actionable insight is usually not simply a matter of waking up one morning with a brilliant idea. I think my PhD background set me up to be a great data scientist in this regard.

 

I got a job out of The Data Incubator! But in addition to that, I got at least three important things out of The Data Incubator. One, I received comprehensive training in key data science tools. Two, I was able to become a part of a supportive network of fellow PhD data scientists. And three, I came away with a good understanding of what is needed in industry and how I, as a PhD, can be of use.

For someone who is in the same situation I was in, fresh out of a theoretical physics PhD, or for that matter any non-computer/data-related PhD program, I recommend two things. First, I recommend learning the prerequisites listed in the websites of the various data science fellowships and boot-camps out there. These prerequisites include, e.g. Python, SQL, and machine learning. [Editor’s Note: for more information on preparing to be a Data Scientist, check out this previous blog post.]

Second, I recommend working on a data-related project. Start with a seed of an idea and follow through with it until you have an interesting result. Getting your hands dirty and struggling through a project is the best way to learn. [Editor’s Note: for suggestions on data sources, check out our posts here and here.]

 

[Working on weekly mini-projects] provided me with a comprehensive toolkit that I will undoubtedly use in the course of my data science career.

We were required to complete six mini-projects over an intense five weeks. These mini-projects covered the following topics: SQL, web scraping, graphs, machine learning, natural language processing, time series, and mapreduce. Completing the mini-projects was the ‘bootcamp’ part of The Data Incubator.

They were quite challenging, but also a lot of fun. The mini-projects were perfect for someone like me with a strong quantitative but not data-related background. They provided me with a comprehensive toolkit that I will undoubtedly use in the course of my data science career.

 

The online fellowship was basically the same as the DC fellowship, except it was done remotely from the comfort of my own home. The online Fellows, like the DC Fellows, were required to do the mini-projects rather than personal projects, which the NYC Fellows were required to do. Online Fellows connected remotely via video conferencing to participate in the daily lectures and the Partner Panels [Editor’s note: Partner Panels are events in which our Partner Companies come in to speak to the Fellows about Industry and their companies. We have around 30 companies visit throughout a fellowship]. The main method of communication for online Fellows was Slack, which I think was pretty effective for its purpose.

For me the main benefit of being an online Fellow was being able to participate as a Data Incubator Fellow, without having to deal with all the overhead of moving to a new place. Being in LA, for example, I didn’t have to worry about purchasing a plane ticket, and I didn’t have to worry about relocating a second time if I ended up getting a job in a location different from my fellowship location. And of course, I didn’t have to deal with the awful East Coast weather!

 

A week before the fellowship started I was able to meet up with another online Fellow, Daniel Huang, who happened to be visiting LA from the Bay Area. We ended up working together throughout the fellowship. Being able to work with Daniel on the mini-projects, in particular, was especially valuable. I learned a lot more from them and enjoyed them more than I would have if I had been on my own.