Alumni Spotlight: Karen Masterson of Springboard
Learning Her 10th Language: An Interview with Springboard Alumna Karen Masterson
Karen came to Springboard with a doctorate in linguistics and a desire to combine her passions in engineering and language. Unlike other people who become data scientists to change careers, Karen's goal was to update her skill set to further delve into her original career and learning goals.
While researching bootcamps, Karen was drawn to Springboard's online school in part because it focused on robust coursework and one-on-one mentorship. Karen discussed her experience with SwitchUp—learn what she thinks about the world of data science, and see why she feels coding is unlike any other language in the world.
Your educational background is in linguistics and philosophy. What made you want to learn Data Science? What was your journey?
I started off my college career as a civil engineering major. I've always been interested in the intersection of technology with language. For my PhD research, I applied information theory algorithms to natural languages in order to quantify metrics such as information and uncertainty in language. That was in the 1990s, and I consider that to be the beginning of my career as a data scientist.
After spending time raising my children, I decided to update my technical skills by enrolling in the Springboard Data Science Career Track. I've been excited to find that what had interested me so deeply as a research topic is now at the cutting edge of practical application in machine learning, natural language processing, and artificial intelligence.
Do you feel data science and linguistics are related? If so, how?
Yes, data science and linguistics are closely related. Much of the heart of data science, including natural language processing, machine translation, recommendation systems, and speech recognition, to name a few, all lie at the intersection of linguistics and data science.
After dedicating so much time to other fields including earning your doctorate, what made you decide to make such a big career shift into the world of technology?
I started my college career as an engineering major. I also spent over 10 years working at UCLA in technology, starting as a computing lab technician, becoming a programmer/analyst, network engineer, network manager, and director of computing. So, I don't see it as so much of a career shift as an updating of my original educational and career objectives. I'm excited to be able to brush up my technical skills and combine them with my love for language and research as a data scientist.
How did you decide to attend Springboard? What made it the right program for you?
I was searching for a program that I could do online that was both rigorous and intensive, and I found all of that with Springboard's Data Science Career Track. The program also assigns a mentor that you meet with on a weekly basis, which has been invaluable for the accountability and advice.
You currently work as a solutions technician for Verizon in their Digital Media Services Department. What does this title mean and what does a normal day at work look like for you?
I work in the Enterprise Data Warehouse Department at Verizon Digital Media Services. It's essentially a data analyst position where I'm contributing to their data integration process. The job is giving me the opportunity to gain hands on experience with "big data", data modeling, ETL, Hadoop, dashboard creation, data warehousing, scrum and agile project management. Right now, I'm working on automating an executive report that gives a monthly and weekly "state of the business" overview for executives of the company, as well as creating and modifying Looker Dashboards and ETL jobs.
We've heard a lot of programmers say that coding is a combination of learning logic and learning a language. Because you speak 9 languages, we're curious what you feel the similarities and differences are between learning to code and learning a new language?
I think the reason I love coding so much is because it is a language, yet so precise that the computer does exactly what you tell it to do, not what you meant to tell it to do. Language, because it is a natural process, is filled with ambiguity and uncertainty, (something known as entropy, which I did my PhD research on by the way). Coding by its nature must be unambiguous and logical which is what makes it different from natural language.
Have you faced any challenges trying to become a data scientist?
My biggest challenge is the gap in my technical career which I took in order to raise my children.
Has Springboard helped you to get a job in data science field? If so, how?
Springboard has given me the opportunity to update my technical skills and my interviewing skills which gave me the edge in being hired for this position with VDMS.
Where do you see your career heading in the next 5-10 years?
I'm very interested in the research aspects of data science, as well as natural language processing and applying and improving algorithms on large datasets. I see myself learning, growing, and contributing to this exciting field in practically applying my analytical skills and natural curiosity. I hope to mentor and teach and further the cutting edge of research in the field.
What makes you most passionate about the world of data science?
I'm passionate about data science because it affords me an opportunity to make contributions into the practical application of useful products at the intersection of technology and linguistics. I basically believe that data science is as much of a leap forward as the printing press was.
If you could go back and give yourself one piece of advice before pursuing data science and coding, what would it be?
I would advise myself to start earlier in the pursuit of a data science education.
Recently, Springboard gathered feedback from their students and mentors, combined it with deep industry research, and updated their Data Science Career Track—the first course of its kind with a job guarantee, or your money back. Want to be like Karen? Learn more about Springboard here.