Andrew K. headshot

Andrew K. M.A. ’21 earned his degree from the Data Analysis and Applied Statistics program based in Northern Virginia.

What is your academic/professional background?
I went to Virginia Tech originally for computer science from 2011 to 2016. Then I worked as a software engineer in a government space for about five years, up until recently where I became a data science/machine learning engineer after finishing the DAAS degree.

What prompted you to pursue the DAAS degree?
I’ve done a little bit of everything, but really what I wanted to delve into more was in the machine learning/data science realm. I figured that I liked programming, but it wasn’t the programming that I wanted to do. Looking into data science and teaching myself a little bit on my own, I thought, “Oh, this is really cool, this is really fascinating — I am enjoying this a lot more.” That’s why I wanted to pursue that. I knew that if I wanted to go further into a field I found more interesting, then I’d have to get my advanced degree.

I was working in an office doing data science work — although I was originally hired there for software engineering work — and they wanted me to start learning data science simply because I could help both aspects of what they needed: somebody who understood data science and someone who could build tools to analyze data. For a while, I was debating if I even wanted to go to grad school or if I could just teach myself what I need to know. I was having a hard time doing that — finding something that I could just do online, on my own — just doing YouTube videos or Udemy, etc. But then I thought, ‘OK, I’ll just let grad school come to me. I’ll find the right program.”

Sure enough, I saw an email in my inbox about a new program that was specifically targeting exactly what I wanted to do — a data science master’s degree. I saw this message: Hey, if you come down to this bar in Clarendon, there’s going to be a meet and greet, and you can actually talk to the director about the program. I thought, “This is perfect.” As soon as I was looking for it, it just happened to appear. I was like, “I can’t pass up this opportunity.” I went to the meet and greet, and thought, “Wow, this is exactly what I want to do. This is everything I want to do and learn.”

What is your current position?
I work as a data scientist/machine learning engineer for one of the Big Four consulting firms. Suffice to say, I’m using data science to look at patterns in people and help improve their lives a lot more just by looking at their environment or how they work out or how they manage health in various aspects, psychological and physical. We're trying to find patterns that can better forecast people’s future behavior and see if we can model that — and then using that model target new programs to help improve people’s living situations.

It feels really good to be able to do what felt like science fiction back when I was a kid to actually helping people today in real ways. It basically has been fulfilling my childhood dream of being a superhero, where I get to be that crazy science guy who figures it out and puts the puzzle together and is able to say, “Hey, if we do this thing, it should improve people’s lives.”

How would you say that this program has benefited you?
One thing that has been fulfilling for me is in the fact that I don’t get confused by jargon anymore — it’s really easy for me to pinpoint out jargon. This program demystified a lot of big, technical terms that are often thrown out in the space of journals or people trying to impress other people with technical talk.

One of my favorite things that happened to me recently is when I was on a call with a fellow data scientist. He was like, “Oh, I have this data — I’m just going to throw a neural network at it and see how it performs over the weekend.” And immediately in my head, I thought, “You can do that, but if that’s your first reaction to anything, that’s such a bad way to do it.” You only approach neural networks in very niche scenarios, and you can't just throw a network at any random set of data. I remember being on the call thinking, “This is so funny to me, because I understand this subject matter, this tool that’s being used. I know how it’s used properly, and that’s not how you use it properly.”

I think the biggest hurdle for a lot of people is a lot of vocabulary that can be very intimidating a lot of times. What this program did for me is make those vocabulary terms a lot more tangible and a lot more knowable — it taught me how to understand and use those terms, as well as how to find patterns in data, and better understand the world through statistics.

The DAAS degree also helped me find a way through programming to automate. With my background in computer science, I knew how to automate tasks and use computational resources usefully — but what I wanted to do was automate the analysis as well, without glossing over important nuances of data. There’s only so much automation you can do with so many random datasets coming in. What I like to do is use my background for programming, as well as my new background in statistics, and combine them to really dive into the machine learning space headfirst. I think this program let me do that.

What would you say to others that are considering starting this program?
What's cool about this program is that you could be working full time and still do this. It’s not easy, but it’s manageable. On top of that, you don’t have to be an expert already in math or computer science at all to pick it up and go. That was what was so good about it. It was very inclusive. People who were in different fields still got a lot out of this program. I worked with someone before who had a middling range of programming knowledge, and he winded up being an incredibly stellar student I got to know in the program. There was someone who had a health background, and she was amazing too. She was actually part of my team that won a hackathon studying COVID data back in April 2020. As far as I knew, she didn’t have a background in statistics or math as a strong suit before, and she helped us win the competition.

There’s very much a sense that you can be anyone and still get a lot out of this program if you have any interest at all in data science or statistics or just understanding patterns that affect our world every single day.

What else would you like people to know about the DAAS program?
It was super fulfilling and I actually miss it a lot. The staff is incredible — Dr. Woteki was super cool about trying to bring in stuff that people are doing in the field nowadays. He was really good about bringing in people who were actually doing the techniques we were learning, and then having us learn how they’re doing it, and then seeing it applied to real scenarios. The professors were incredible too — all of them were just kind people. It felt like they were kind of your friends in this. Granted this was during COVID, and any human contact I had was precious to me. Maybe it affected my feelings

Shout out to Cherie [Nelson, program manager] as well. She saved my butt so many times when it came to bureaucracy stuff that I just didn’t understand. There’s one point where she actually did save me. There was a mix-up that happened, I think in the Graduate School at Virginia Tech, where my Capstone class wasn’t registered properly. I went through the entire class over the summer, did it, and completed it. The only proof I had that actually showed I graduated in summer 2021 was an email from Dr. Woteki. About a month later, I saw this email from the bursar’s office at Virginia Tech saying, “Just so you know, we can’t find your last class. We can’t award you the graduate degree until we see that.” I was like, “I totally did this class, I totally paid for it too. I put a lot of effort into it.” And then Cherie just comes in, not even having me respond yet, like ‘I’ll handle this.” I got the degree I think two weeks after that in the mail.

I also learned everything I ever wanted to know about data science. I’m hoping to even dive down into the fields of statistics and computer science for a Ph.D. sometime down the road.

I’m glad I did this because I now have so many doors open to me, just for the sake of computer science, statistics, data science, machine learning — and all these things I can do now and can pursue with the knowledge I have now, and the tools to use specific techniques in certain scenarios. I just feel like if I don’t know something, then I can be like, “Oh, I don’t know how this works specifically, but I know tools that can do something similar,” so I know how to pick up new things later on.