Interview: What Does it Mean to be a Data Scientist?
May 14, 2020By Sarah Dimock
Change can be intimidating, especially when shifting gears means shifting the way you thought your life might go. At Capitol we know from almost 100 years of experience that education, or the decision to go back to school and earn a second degree, is often a big change. Yet sometimes without making that leap, our lives become stagnant and we can end up feeling stuck.
Today we talked to real person and Data Analyst, Evan Snow, who shared with us his education story and what his career looks like nowadays. Mr. Snow has an undergraduate degree in Psychology from UMBC, an MBA from Loyola University, and most recently he hit the books again to earn a Masters in Data Science from UMBC. If you’re curious about what it’s really like to earn a degree and work in the field you’re passionate about, read on.
Q: Tell me about your job. What is your official title now?
My official title is Data Analyst and I work for a company that works on contracts for CMS, the Center for Medicare and Medicaid Services. Our specific product is related to price transparency tools on the Medicare website.
Q: How did you decide to get into data science and analytics? Can you share your story with us?
Well that means going back to the year 2017, when I made the decision to sign up for a master’s program. I was working at a commercial real estate data company and my title at that time was market analyst – as in real estate market analyst. So, if you imagine a stock analyst, but instead of someone who uses data and expertise to write about a stock’s performance, I was writing about how a real estate market was performing in terms of – is this somewhere you would want to build an apartment building right now? If you own apartments, should you expect to be able to increase rents in the next year? My area of specialty was South Florida – and as you know I had never lived in South Florida nor did I know the real estate market.
When I was promoted into that position after an entry level job at the same company, they told me, well you’ve gotta become our subject matter expert on what’s going on in this real estate market. Part of that was traveling there and interviewing real estate investors and brokers, but the other big thing was looking at data. We had a team of “quantitative analysts,” who functioned a lot like data scientists. I don’t think they were called that at the time. They would put reports together based on the data they were ingesting and come out quarterly with a market forecast. Then we took that data, combined it with interviews and local news and wrote a report about the state of the real estate market.
Well, a lot of people on that team were very non-technical with data. And that’s kind of where I started. Knowing that I was interested in, how can the data paint the picture that I need to paint as part of my job, but also how can I make it more interesting than just regurgitating what you see after a particular graph? About a year into that position, on the job I had the opportunity to learn SQL and began looking at various underlying datasets to see the data for myself. It was my first opportunity to look at things that weren’t spoon-fed to me the way I was otherwise typically getting information. It was like, Oh! This is really cool. And I knew it would improve the quality of reports if everyone did this. That led me to look into pushing my whole career in that direction of how can I gain more technical skills with data?
Q: When did you know that you needed to go back to school to advance your career?
After a while I was getting the hang of using SQL but noticing that a lot of other people on the team weren’t. I knew there was an opportunity to learn about this and make things better, but I didn’t have any kind of credentials at that point. I had this feeling of, I don’t know what I don’t know. I know I’m interested in the field. It sounds like the right time to look into master’s programs.
My goal became finding a way to steer my career in a certain direction towards a certain specialty that I was opportunistically going in, that I hadn’t previously thought of as what I would do. Education seemed like the best way for me to advance.
Q: Were there any surprises along the way? What was the most interesting part of your education?
When I first started, I was surprised by how much the field was really still evolving. I was in UMBC’s inaugural class for my program. It was the first time they had ever taught it so some of the professors, especially for the first few semesters, had just been hired to teach these new classes and were trying out new curriculums. Later on, I would hear other students saying, oh yeah you guys did that stuff in Data Science 101? We did this, and it would be totally different. But I think that’s indicative of a new program in a very new and rapidly moving field.
There’s a lot of debate over – are data scientists really just statisticians or is it more like a specialty of being a programmer? A lot of people ask, you’re a data scientist, what does that even mean? I think a lot of that is because it’s so new and the field moves so fast.
Q: What does a data scientist do?
Well, that’s a great question. I think, as with any rapidly evolving field, a lot people are trying to come to a consensus on that. A good place to start would be with, what is the difference between a data scientist and a data analyst? Analyst is a term that’s been around for a lot longer. One way I’ve heard it phrased is that a data scientist takes analysis a step forward into prediction. That is, an analyst can look at data and tell you, this is what happened in the past or this is what’s going on right now. A data scientist can use predictive models to say, and here’s what might happen in the future. Here’s what you should do about it.
Q: Now that you work in your field of study, can you tell us what your job is like? What sort of projects do you do from day to day?
So, I work on a government contract where we have a relatively small team working on one big project. For a while we had this interesting period of time where we were in an exploratory phase. We were able to research what might most useful to build next. My role in that involved looking to data to answer some of the questions a user might have. For example, the question might be if I’m a beneficiary when and where do I need to know prices of health care? Is it when I go to the doctor’s office? Is it when I’m going in for a procedure?
We want the data that we are surfacing for these users to be useful, to help them in making decisions about their healthcare, and understanding what their bills might look like ahead of time. I think it’s a big source of anxiety for many people, to have no idea what their upcoming medical bills will look like, and to struggle to find any information that could shed light on that. We are trying to build tools to help break down those obstacles.
Now it’s a matter of tracking down the data that’s going to drive the site, figuring out what data can we get, and how to organize it. And we just talked about data analyst versus data scientist. Even if there’s not a lot of predictive modeling at this stage, we are always brainstorming ahead. Thinking about things like fraud detection is a data science problem. Like how can I catch fraudulent claims before they happen? Or at the very least making sure it isn’t going on for an extended period before it gets noticed.
Q: Do you have any advice for students who hope to someday work in data science or analytics?
Be prepared that in the real-world, problems are going to be more open ended than they are in the classroom. You might end up in a situation where things don’t have just one easy answer. Sometimes with data science you can build a model and run it the best-in-class way but there’s just not great patterns that come out of it. There’s a lot of hype around data science being able to predict the future and solve any problem, but data science isn’t magic. Often things are a little more complicated than that. Be prepared for a lot of open-ended problem solving in the real world.
If you’re driven by data like Mr. Snow and thinking about a degree, Capitol Technology University offers programs at every level of education including a BS in Data Science, a TMBA in Business analytics and Data Science, and a PhD in Business Analytics and Data Science. The majority of our programs are and have always been completely online and we even offer combination programs like our or our PhD /MS program in the Technology of your choice.
Click the links above to learn more about our programs or talk to a member of our friendly admissions crew at admissions@captechu.edu, gradadmit@captechu.edu, or call 800-950-1992.