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Alumni Spotlight: Kelly Mejia Breton of NYC Data Science Academy

Imogen Crispe

Written By Imogen Crispe

Last updated on December 9, 2016

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Kelly Mejia Breton has a background in mathematics and statistics, and had worked as a senior energy analyst for five years when she quit her job to enroll at NYC Data Science Academy. Kelly had enjoyed learning about machine learning in grad school, and wanted to learn more about how to apply it. Now Kelly has an exciting new job as a Marketing Science Associate Director at Mindshare, where she is using both her old and new skills. Kelly tells us why she chose NYC Data Science Academy over other data science bootcamps, how much she appreciated having other women in her cohort, and why she enjoyed all of the projects she worked on there.


What is your pre-NYC Data Science Academy story? Describe your educational and career background.

I graduated with a bachelor’s of arts degree in mathematics from the University of Rochester in 2006, and started working in finance at Morgan Stanley. I was there for three years in their private wealth management department where I opened accounts, and traded. Soon after that, I decided to pursue a masters in statistics from the City University of New York at Hunter College. I graduated in May 2011 and started working at Pira Energy Group, where I was an analyst. A year and a half later I was promoted to be a senior analyst, forecasting crude prices, and product prices.

After five years in that job, I decided I wanted to go back to doing what I learned from my statistics degree. I loved Pira and I learned a lot there, but my role was based more around fundamentals and economics, and I wanted to be working in statistics. I had tried learning data science on my own using Coursera, but it was difficult to find time alongside my busy work schedule. So I decided I needed to study full-time. I did some research and found that NYC Data Science Academy was the best fit for me.

Why did you want to specialize in Data Science? How different was that from your previous analyst role?

What I was doing before was more economics and fundamentals– we weren’t using much machine learning. I had done machine learning in my graduate program, and I really wanted to learn more about how to apply it. I had done a little bit with R in my graduate program, but I wanted to do more. So after five years, I thought I needed a refresher, plus I could learn some new algorithms that I hadn’t known.

Why did you choose NYC Data Science Academy over other coding bootcamps? Was the curriculum important?

I did some research and NYC Data Science Academy was highly ranked. At the time when I was looking for a data science program, I found that most of them were on the West Coast, and the only one that compared to that level on the East Coast was the NYC Data Science Academy. I met with them, they were really nice and helpful, and that sealed the deal.

The curriculum was important too. If they hadn’t offered R and Python as a base, I would not have been interested. Also, as a perk they also taught big data topics, like Spark, Hive, and Hadoop.

What was the application and interview process like for you?

The application was an online application, and you had to submit some documents, and do some coding. Then there was an in person interview. The coding challenge was medium difficulty as I hadn’t coded before. Although I had used R as a statistician, I didn’t really code as much. I couldn’t do it on my own so I was googling a lot. I don’t think I did that well on the coding, but NYCDSA looked at my background and skills as a package deal. I don’t have coding, but I do have experience in statistics, analyzing data, and math. Those things probably outweighed the fact that I didn’t know coding. Whereas somebody else with strong coding skills, and not so much statistics, may also be a package deal and be accepted to the program. But if you don’t have a statistics, math, or coding background, then maybe it’s not a great fit. They saw how I was thinking and they saw my background, so they probably thought that I would be a good fit and could pick up whatever I was lacking.

How many people were in your cohort? Was your class diverse in terms of gender, race, life and career backgrounds?

Yes, there were people from literally everywhere, and from all age groups. We were a group of 21 students, and there were five girls. For me, that’s high, because studying math and statistics my whole life, I’ve always been the only girl in the room. They all had similar backgrounds, either physics, math, engineering or computer science.

What was the learning experience like at NYC Data Science Academy? Describe a typical day and teaching style.

I like to wake up early because that’s when I learn better. Whereas some students would stay late, I would get in around 7am and start my day early. I’d go over my homework, and my notes before class started at 9:30am. At 12:30pm we had a lunch break, then class started again at 2pm. In the afternoon we would either be doing some kind of topic that’s not part of the main curriculum, continuing the lectures, reviewing homework, presenting a project, or working with the TAs on our projects. Classes normally ended at 4pm or 5pm, then afterwards you stay for a few hours going over homework. I would stay there until 7pm on a daily basis and some people stayed there until midnight every day.

What was your favorite project that you worked on at NYC Data Science Academy?

I honestly liked them all. What’s cool about the Academy is they let you pick the data set you that you want to work with. There are five projects, and every 2 weeks you have a new project due that you have to present. In my cohort there was one project the instructors selected for you, which was a kaggle project. We had to work in a team and it was pretty fun. Even though the kaggle project was all about physics, I still liked it because at the end of the day, it helped me realize data is data, and I was able to still find a story, still analyze, and still forecast, whether I had any background in physics or not, so that was pretty cool.

How did your learning experience at NYC Data Science Academy compare to learning at college?

What I liked about the teaching style was it was more one-on-one, you really felt like you could reach out to instructors at any point. It wasn’t only in TA or office hours, you were able to reach out to them throughout the whole day. A lot of instructors stayed there until late– there were always at least two TAs there until 10pm at the earliest.

How did the bootcamp prepare you for job hunting?

They had professionals come and help with our resumes and interview skills. They also have a career fair, which is like a networking event, where employers in the industry who are looking for employees would come in and network with us. A lot of interviews came out of that, and a lot of people got positions from those events.

What are you doing now? Tell us about your new job!

The position is Marketing Science Associate Director at Mindshare, and I got it through the Academy. Mindshare was going to attend the networking event, but didn’t end up coming. I was looking for them and couldn’t find them, so the following day I reached out and sent them my resume. About a week later, I started interviewing with them. Then later on, they contacted the Academy again and we actually have another NYC Data Science grad who is working at Mindshare, so that’s pretty cool.

What does your role involve?

My position is working with data – sometimes large datasets – using Python, R, Hive, and some Spark to find data insights for clients. We analyze client advertisements, and marketing campaigns to see how they performed, and use machine learning to forecast how they can improve their advertising.

Are you using the technologies you learned at NYC Data Science Academy or have you had to learn new skills?

The Academy gave me a great foundation so that the new skills I am learning just build on what I already learned. Without the Academy, I think I would’ve had a more difficult time understanding and using these new skills that I have learned. We pretty much use everything I learned at the academy including SQL.

How has your previous background been useful in your new job?

Because I worked a lot with data before, data is data, so my previous experience is always useful. Everywhere I go, I feel like I’m building on what I’ve learned.

How do you stay involved with NYC Data Science Academy? Have you kept in touch with staff and other alumni?

We have a Slack chat group, so we stay in touch through that, and a lot of us stay in contact through Linkedin. Every now and then the Academy has a meetup or some event. I went back to speak at a meetup once, before I started my position at Mindshare. I was really nervous to start NYC Data Science Academy because I didn’t know anyone who had attended, and I wasn’t sure what to expect. So I like to give other people that comfort, that it actually was a great experience, and for me it was a dream come true. So if they reach out to me I always make the time to go.

What advice do you have for people thinking about changing careers by going through a data science bootcamp?

I would say, if you really do love data, and you like coding, machine learning, and finding the inspect of the data, and the story and all that, then I would say go for it. I was very nervous and had a really stable job that I was really good at it, so it was a hard decision to leave. It was a tough decision, but I do not regret it. So if you really love it, do it.

It’s hard work, it’s not an easy thing, so if you’re not committed 100%, then don’t waste your time. But if you do like it, then it’s worth a lot, and I would say do it. It’s hard work, but it can be done.

Find out more and read NYC Data Science Academy reviews on Course Report. Check out the NYC Data Science Academy website.

About The Author

Imogen Crispe

Imogen Crispe

Imogen is a writer and content producer who loves exploring technology and education in her work. Her strong background in journalism, writing for newspapers and news websites, makes her a contributor with professionalism and integrity.

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