What is it like to be a data scientist? Glassdoor recently named the best jobs in America
, and data scientist
came in the number one spot. With high salaries and many job openings, these analytical minds have earned a spot in the career hall of fame.
Before switching careers to work in data analytics, big data
, or machine learning
, it might be helpful to know what exactly a data scientist does all day.
General Assembly Data Science Immersive
instructor, Lucy Williams
, is excited to welcome students to class. Mostly, she says, because people from all professional backgrounds can benefit from analytics. Williams herself took a nontraditional path to her current role. A former Naval Officer, she has pursued three Masters degrees, and earned fellowships for her work with data. Here, Williams explains why data science
is important, what the future of data is, and how her career allows her to be both creative and analytical.
Tell us about yourself. What’s your academic background and what did you do before joining GA?
My formal education is a BS in Biochemistry, an MS in Physiology, and I actually didn’t complete my two other Master’s programs, one in Systems Engineering and another in Liberal Studies. Before joining GA, I worked at a think tank, a FFRDC (Federally Funded Research and Development Center), a consulting firm, for a futurist, and I’m also a former Naval Engineering Officer.
When did you first become interested in data and engineering? How did you break into the field?
I got interested in big data around 2009. I was working for Johns Hopkins Applied Physics Lab at the time, and I proposed an Individual Research and Development Project (IRAD), “Social Media in a National Security Context.” Nowadays, if you said that social media could be used for national security purposes, everyone would say, of course! But, back in 2009, I think it was such early days, that my project wasn’t picked up. That said, my big data interest only kept increasing.
“Data science isn’t just for astrophysicists and data analysts, it’s for everyone.”
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After your experience with the Navy, you worked in research and consulting. What inspired your transition from research to data science and startups? How did you make this change?
After Johns Hopkins Applied Physics Lab didn’t pick up my IRAD project, I decided that despite the fact that the majority of my career had been spent in the national security community, it wasn’t a place where I could be very creative or innovative. The Johns Hopkins project was only one of a number of attempts I had made over the years, so I decided to try something new.
How has data, analytics, and engineering changed since you’ve been working in the field? Where do you see the field going in the future?
A friend of mine who is an astrophysicist, always says that astrophysicists have been
data scientists all along. To which I say, yes, that’s true. The difference is that now, because of the amount of data being created, the amount of data that’s available, the
cheap cost for processing and storing that data, all organizations need to use their data wisely.
“Data science and data engineering skills allow someone to work in almost any industry.”
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I think in the near future, if organizations aren’t using their data wisely already, a failure to do so will at best make them non-competitive; at worst it will make them obsolete.
I recently did a prospective student meet and greet in DC. There were a bunch of analysts from a number of different industries who were looking to make the progression from data analysis to data science, which I didn’t think was unusual. The prospective student who I found most intriguing was an architect. She is the future. Data science isn’t just for astrophysicists and data analysts, it’s for everyone.
What inspired you to become an instructor at GA? Why are you excited to teach data science?
I think the most rewarding extracurricular that I’ve done over the years is tutoring STEM. There was one young woman I tutored in algebra who had gotten an F one semester. Within one semester of tutoring, she went from an F to an A and even said that she thought algebra was fun!
Of course, it was fantastic that her grade improved that much, but it was so much more than that – it improved her confidence in herself, in her decision-making, in her critical thinking and analysis skills, and other areas seemingly unrelated to algebra. I think that kind of teaching/learning experience is agnostic to subject and I look forward to similar things happening with the DSI students.
What is your favorite part about data science and data engineering?
The agnostic application of them. I think that data science and data engineering will permeate every industry in a meta sense. What makes that neat is that I think data science and data engineering skills allow someone to work in almost any industry.
What advice do you have for aspiring data scientists?
Make a plan and stick to it.
Who was your favorite teacher and why?
When I’m not reading about or doing STEM, I tend towards a lot of sci-fi reading and when I was younger, writing.
My favorite teacher was my 6th-grade teacher Mrs. Fasching. We had to write an essay for a contest to eat dinner with our state governor. I wrote a sci-fi tale, but what I remember most about it, is that I made some words up and created a glossary at the beginning. Instead of telling me that doing this was totally ridiculous, which she certainly could have, Mrs. Fasching let my creativity rule. I came in third in the state out of about 10,000 6th graders. So, no dinner with the governor, but creativity and Mrs. Fasching rule!
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