Denis was a biomedical engineer on the pre-med track when he realized he wasn’t on the right career path. He had always loved tech and keeping up with the latest tech trends, so he researched coding bootcamps. Denis came across data science and realized it was the perfect combination of his tech and STEM interests, so he enrolled at NYC Data Science Academy. Now Denis is a data scientist at Ameritas Life Insurance Corp. and it’s exactly the job he was looking for. He tells us why he chose NYC Data Science Academy over other data science bootcamps, how dedicated his instructors were, and about his Donald Trump web scraper project!
What is your career and education background before you started at NYC Data Science Academy?
I graduated with a bachelor's in biomedical engineering. I then started medical school, but I soon felt it wasn’t for me and I lost interest. So I turned to technology because I knew I liked it. I have always been a tech person, trying to find out what’s new, what's hot, and all the trends.
I was also interested in math and worked for a while as a tutor for high school math and standardized tests like the ACTs and SATs. Then I realized, what better way to combine technology and math, than with data science! I felt it was a great move into data science because, like in tutoring, you’re telling stories and figuring out how to break down this information to make it more deliverable and easier to understand.
How did you become aware of Data Science as a career?
I was interested in technology and I’d heard of web development courses and bootcamps, so I looked into doing one of those. But in one of my searches, I came across a Data Science bootcamp and that’s when I first started learning about it.
Did you have any exposure to data science in your work or previous studies?
I didn’t necessarily have exposure to Data Science, but more so the storytelling and the research aspect. I did research for two years in a biomedical engineering lab.
Did you research other data science bootcamps in NYC?
I looked at Galvanize and a few others in the northeast. I wanted to stay local. I’m from Westchester County, NY, so I wanted something close. When looking at other schools, it looked like NYC Data Science Academy was a little more personalized, and I liked how they scheduled their curriculum. It would be standardized, you’d have planned coursework and classes, and pre-prepared notes as well. I thought that would be really useful because, let’s say you learn something in the morning, but you don’t really remember the exact code for it, the notes are always great to refer to.
Did you want to learn a specific data science language?
No. Prior to the bootcamp, with my interest in technology, I was dabbling in web development, HTML, CSS, a bit of Ruby, and Python, so I was actually open to learning more languages. I thought that would be beneficial, because if one language wasn’t great then you could always turn to another.
Did you think about studying data science at college?
Possibly, but bootcamps are a lot faster, and they get you work experience which is what I feel is very important. Going back to school would take longer, and I already had a degree. I was actually looking at masters degrees if anything, but I thought a bootcamp was the best way to get experience and get into a new career.
How did you pay for the NYC Data Science Academy tuition?
I don’t think NYCDSA offers scholarships, so I financed it on my own. During bootcamp, I was also tutoring 15 hours every weekend so that helped pay for it.
How many people were in your cohort? Was your class diverse in terms of gender, race, life and career backgrounds?
There were about 22 people. I think it was diverse. Even though we had more guys, about a quarter were females. People came from different backgrounds, there were some people coming out of academia with PhDs, and some people who had work experience. Most people had a masters and work experience, and were looking for a change in career.
NYCDSA usually only accepts people with masters or PhDs. Was it unusual for you to have a bachelor's and not a master's or PhD?
There were four or five of us with bachelor's degrees. Maybe I got in because I got into a doctorate program, even though I didn’t finish.
What was the applications and admissions process like?
There was an online application with one or two coding challenges at the end. We could use any coding language, I think I stuck with Python because I had experience with that at the time. And then we had a phone screening interview, where I could ask questions, they could ask me questions, and get to know me. Then I got called in for an onsite interview, and that’s where I had interviewed with an instructor to talk about my past experiences, what I hope to get from the program, and my future direction, to see if I’m a fit. I was nervous throughout the whole process because the bootcamp was something I really wanted.
How difficult was the coding challenge?
Not exactly difficult. I had done previous problems like that, using online resources. When I was learning how to program in Python, they would give me basic math problems, and say “how would you put this into code,” and I thought that was pretty helpful in figuring out the application challenge.
What was the learning experience like at your bootcamp — typical day and teaching style?
A typical day started at 9:30am with lecture for three hours, that ended at 12:30pm, then lunch until 2pm. Usually people would go to grab food and come back to do work and ask questions while they ate. The afternoon was for homework review, help with projects, and sometimes there were extra learning sessions, like workshops, with topics that would be useful. Some of us stayed until 11pm or 12am. I’ve done that quite a few times, it’s not abnormal. The instructors are also there up until 10pm or 11pm, so they are really helpful. That was something I really liked and was one of the reasons I think I made the right choice going there.
How did it compare to learning at college?
In college, professors have office hours but I never really utilized them because I didn’t feel I needed them. However with this, because you’re learning at a fast rate, and most of the learning is done through practice, having the TAs and instructors around for questions is very helpful, so that’s the main difference. The learning pace at NYC Data Science Academy was comparable to some of my past experiences with education.
What is your favorite project that you worked on at NYC Data Science Academy?
My favorite would have to be the data visualization projects and the web scraping. Web scraping was interesting because it made you think, how could you write a script to pull data off a web page? And sometimes in real life you’re not going to have data in a table for you, neatly laid out. So that was a helpful project to learn how to tackle those problems later on.
I scraped a Twitter page, it was Donald Trump’s page. I was looking for word counts, so seeing which words he used a lot, and the conclusion was, he used the phrase “make America great again”, very often. I also analyzed and looked at who he tweeted at the most, which I found were social media personalities and media outlets. Looking at the number of retweets and likes, you could tell that before he started to run he around 50 for each tweet, but a year later he is getting a couple of thousand likes and retweets for every single tweet.
What was your Data visualization project?
This was the first project and the topic was regarding water quality in NYC. It was interesting to see the number of water complaints and the type of complaints in each borough. I drink water straight from the tap, so I like to know where I should be careful about drinking unfiltered water. I remember Staten Island had the least number of complaints, however, they also have a smaller population. I didn’t have a chance to compare the number of complaints with population size.
How did NYCDSA prepare you for job hunting?
Besides the knowledge and education, they have a hiring party in the second to last week of bootcamp. That’s where we learned to network and talk to people who were hiring, and that’s actually where I met my current manager. Then after the bootcamp, the school opened up an extra room where grads could come and continue to learn and ask questions. The job placement manager also held mock interviews to make sure we were prepared and I found that really useful. They also helped refer us to potential employers.
What are you doing now? Tell us about your new job!
I am at a life insurance company called Ameritas, and I’m a data scientist in the marketing department.
What is the company like and what do you do there?
The company is headed in a new direction and I’m the first data scientist here. So far, I’m in the process of learning about the products – I didn’t know there were so many insurance products. I’m trying to learn more about the industry, and figure out how we go about learning about our customers, which is basically the aim of every marketing organization. Once we do that, we can cater our products to the customers a lot better, and also improve the customer experience.
How big is your team? How many people do you work with?
I interact more with the IT department for now, but I do report directly to my manager in marketing, and she reports directly to the CMO (chief marketing officer). So I feel like my voice and my suggestions are being heard, and that’s good because I wasn’t looking for an organization where I would join and do things that would be disregarded.
How have the first three weeks of your job been so far?
They’ve been really fast and busy. For the first two weeks, I was meeting everyone in the marketing department. But there’s another office in another state, and I work with people in both offices. I think I’ve met with 20+ people, ranging from graphic designers to videographers, to people in charge of each product that the company has.
So far I’ve been exploring the data, gathering what we have, and talking to other people in marketing to figure out what would be useful to know. We are just starting this, so it feels like a startup environment in an established company. I like that.
How did you find your job and when did you start?
I talked to my manager at the end of hiring party. A couple of weeks later, I followed up, and my hiring manager set up a phone interview with me – she was in Ohio. So we set up a phone call, and she gave me a problem, and asked how I’d tackle it. I got called back for more video interviews with a director in IT, two marketing managers, and the CMO. After that, I got good news, and I moved to Cincinnati, Ohio.
Are you using the technologies and skills you learned at NYCDSA?
I actually just started using them because I finally got my hands on some of the data. Right now, I’m starting off with R since there are a lot more more packages available, so that’s what I prefer to use. If Python is needed later on, I’ll use that, but for now I’m just going to stick to R. I think the company is also going to use some other applications which I’ll learn as well.
Is this the job you wanted? Do you feel like you reached your goal?
This is actually the job I was looking for. I wanted to do marketing, because it was interesting. I’m coming from a background in health, I like to understand people. Being able to analyze people’s buying behavior or catering services to make them feel more involved with the company, and keeping them around, is something that really interests me. I also like that I can explore any avenue I want, instead of going into a company and being told, “you’re the data scientist, you’re only going to be looking into email clicks or just websites.”
I know you’ve moved states, but how do you stay involved with NYCDSA? Have you kept in touch with other alumni?
Yes, when I was still in NY, I was coming back and using the extra room at NYCSDA so I often saw my classmates and instructors. And since I’ve moved, if I know of an opportunity on LinkedIn, I’ll send it over to them. We also still stay in contact in our cohort Slack group.
What was the best thing about NYCDSA?
The helpfulness of the instructors, their knowledge and willingness to help us- they dedicated a lot of time during their lunchtime, and after class. Some of them stayed to help until 9pm or 10 pm. Having flexible access to the building was great because we could always come and study if we wanted and I think that was really useful.
What was the most challenging thing about studying data science?
The most challenging thing is the amount of information given in that short period of time, because you can’t slack off and hope to catch up the next day. You have to actually stay on top of it every single day. I think that was the most challenging thing because sometimes you’re just a little tired, so you’ve got to make sure you get enough sleep as well.
What advice do you have for people who are thinking about doing a data science bootcamp?
The projects are very very important. Anyone can say “I know how to do this” on their resume, but a portfolio of projects actually proves it. The fact that we do five projects gives you a lot of opportunities to showcase different skills.