Student Spotlight: Samara Bliss, from NYC Data Science Academy to IBM Watson

Student Spotlight: Samara Bliss, from NYC Data Science Academy to IBM Watson

Posted by Vivian Zhang

Updated: Jan 15, 2016

alumni-nyc-data-sci-samaraThis article is contributed by CourseReport, a third party website which specialized in covering bootcamp stories. 

After one year of medical school, Samara Bliss realized that her true passion was in health technology, and that data science skills would be vital to her career goals. Now just days away from graduation at NYC Data Science Academy, Samara tells us how she landed a job at IBM Watson before graduation and shares the most important ingredient to a successful bootcamp experience.


Tell us what you were up to before attending NYC Data Science Academy.

I was pre-med during undergrad and graduated from Columbia with a Bachelor’s Degree in Neuroscience. After graduating, I did a year of research with neurosurgeons and then started medical school in fall 2014. I’ve been interested in health technology for a very long time and planned to work as both a practitioner and entrepreneur. I wanted to be one of those MDs that embraces technology. But in medical school, I realized that clinical practice was not for me. I was so passionate about technology and data that I ended up spending all of my time focusing on that. After asking a lot of people for advice, I decided to go after the type of job I wanted so I left after completing one year.

When you were in med school and doing research at Columbia, did you find that you were able to see the intersection between health and technology?

There could be two parts to this question. One is the intersection of health and data and the other is the intersection of health and technology.

I briefly audited a bioinformatics and health data course in college and I was aware of the importance of data to medicine but wasn’t able to really focus on it. I did quite a bit of clinical research before and during medical school and looking back it’s funny to think how bizarre some of those statistics were. The numbers are so small and the values are often insignificant. Sometimes it felt like we were using small data to make grand, sweeping arguments that don’t necessarily hold up all the time.

There’s another side, health and technology. There were people willing to teach me about it, but I had to work hard to foster those relationships. Medicine is notorious for being very slow to pick up new technology and I found that to be very true throughout my educational experience. In my first year I did this clinical skills class that teaches you how to be a doctor—how to use a stethoscope and related skills. I often felt like we were being trained to teach and practice medicine in the 1950s and not 2030, when we’ll be working in our own practice.

You left medical school after your first year. What did you do after that?

That summer I had a grant from the National Institute of Health to do research with the general surgery team for three months. The grant ended at the end of August and I started the data science bootcamp mid-September.

How did you find out about NYC Data Science Academy?

One of my best friends from college did a Master’s degree in statistics and is currently working as a data scientist. I asked him about it, and he suggested not to do the Master’s degree.  He is very connected in the data science world and recommended two bootcamps. One of them was Insight, which I was not qualified for because I don’t have a PhD. The other was NYC Data Science Academy. Technically, I’m not qualified for this program either because it’s supposed to be post-masters or post-doctorate, but maybe one year of med school counts.

There are people without Master’s degrees and PhDs enrolled at NYC Data Science Academy, correct?

Not that many. Almost everyone had some form of advanced degree which is a real selling point for the course. I enrolled in a General Assembly data science course while going through the application process at NYC Data Science Academy and it was great because I was able to compare the two experiences. At General Assembly, my interview was a 10-minute phone call with an interviewer who asked pretty basic questions.

Was that for the GA immersive data science course?

No, this was a part-time class that met for three hours twice a week. I think the course was 60 total hours of instruction. I got the sense that most people were accepted, which works fine for a twice a week, six hour course.

One thing I liked about NYC Data Science Academy is that they tried to get a good group of people together to meet the expectations of their industry partners.

Did you have programming experience or exposure to Python or R?

I took a statistics course in college and we used R in that course. I used it a couple of times, but not when I was doing clinical research. I didn’t have any experience with Python. A background in Python would have been extremely helpful.

Did you need that experience to get through the interview at NYC Data Science?

There’s a coding challenge during the interview process. The application includes two algorithm-type problems that involve writing code in R or Python, whichever one you prefer.

Were you able to get through the coding challenge with your background in R or did you have to teach yourself how to do it?

The challenge was something along the lines of “what is the sum of every third Fibonacci number from one to 1000?”

I thought about it for a while, and I wasn’t able to figure it out how without the help of the internet and friends with a background in data science.  On my application, I wrote, “Full disclosure: I did not come up with this answer on my own, but I could explain it.” During my in-person interview they asked me to whiteboard the answer and I was able to explain it. I’d say you don’t necessarily need to know how to do it on your own, but you should be able to write the code and explain what it means.

You mentioned that NYC Data Science did a good job forming a strong cohort. Was your class strong, was it diverse, and was it a good group of people?

My favorite part about the bootcamp is the other students. I was super intimidated by the group in the beginning. It was the most impressive collection of people I’ve ever been in a room with, and everyone had varied backgrounds. There are two people with PhDs in Math, people with PhDs in Computer Science, a person who worked in a hedge fund for 15 or so years and has published numerous papers, a biodynamics PhD—it just goes on and on. It makes it easy to stay late working with these people because they’re so smart and great to be around.

I’d say our professional diversity was stronger than our demographic diversity. When I’m describing the bootcamp to my friends, I like to say the average person is a young, newly married white male pivoting their career. There’s 21 people in the class and only one other female besides myself.

Did you feel that you were on the same level in terms of programing and quantitative expertise. Did you feel that you could keep up?

I was certainly not on the same level and it was definitely hard to keep up. But one thing that’s great about having individuals with varied backgrounds is that a PhD in math may easily grasp the statistics material, but they might not grasp Python as easily and vice-versa. No one in the room knows everything or else they wouldn’t be there.

There was enough support and infrastructure to ensure that I didn’t get completely lost.

Coming from the medical field to technology, does it seem more male-dominated than medicine?

I spent most of my time and did all of my research in the general surgery department and that was mostly male aside from the Chief of Surgery.

Being one of two women in a bootcamp, do you have advice for the many other women who find themselves in that position?

It’s funny because I never thought about it. I rarely think about being the only female in the room until someone reminds me. It was a very mature, focused group of people.

I will say that I’ve never been aware of any difference in experience or problems. If anything, at networking events it felt like people were maybe even more interested in talking to me because they were consciously and subconsciously interested in hiring a female data scientist.

There are also very well-established data science groups for women, like Py Ladies or Women In Machine Learning, for example. There is a lot of support available.

Have you been to meetups for data science or Python specifically?

A few students in my cohort and I went to a three-day conference called PyData recently. There were lectures about cool, exciting things people are doing with Python.  It was very applicable to what we were learning at the time.

Have you started a new job yet?

We finish the bootcamp Friday after next. I accepted a position at IBM Watson and sent my acceptance email today.

Who’s teaching the class that you’re taking?

One of the first instructors graduated from Columbia the same year that I did, and he had a background in linguistics. My other instructor had a masters in statistics, I believe. He’s one of the best teachers I’ve ever had.

There’s something to be said for teachers that are recent bootcamp grads because they just learned the information and remember what it’s like to not know anything. They were very good at explaining difficult concepts. There were also a couple other teachers including a math PhD and a guy who was a computer science professor.

There’s a lot of controversy in the coding bootcamp world about hiring TAs who recently graduated from the school. The main argument is that a lot of times, they’re the best teachers.

We have a couple of TAs who were previous students. They all go above and beyond and are there until 11 p.m. every night.

It makes a big difference to have their support round the clock. When the students are working late on a problem set or project there’s always TAs available to help and answer questions. When I think about the cost of the bootcamp and how it went towards paying these people’s salaries so that they were available to me, it was worth it.

Was working late every night the expectation? How many hours a week did you put in, on average?

It depends on the week and the project.  It also depends on what you want to get out of the program. What is great is that so many of us in the class have the same mentality—we signed up for this crazy thing for three months, we have to give it our all and if we’re not working hard, we’re not doing something right.

We have lecture every day from 9:30a.m. to 12:30p.m. They give us an hour and a half for lunch during which time people may schedule meetings. In the afternoon we’ll either do a review of the homework, a lab or listen to a guest speaker. The scheduled time usually ends around 5:00 then people start working on homework or projects.

There were probably three to four weeks that I was there until around 11:00 almost every night. This past weekend was the first weekend that I did no work. It was really weird. I woke up on Saturday and said, “this is bizarre. Someone give me a task!” It’s a lot of work, and I think that’s because people are trying to get the most out of it.

Do you take tests that you have to pass?

They have an internal grading system but they don’t show us the grades. It partly has to do with accreditation—they need a way to assess students and attendance. They keep track of attendance, homework, in-class “labs”, and projects. You have to pass each section of the course (there are six or seven). There would be many steps and interventions though before they would finally say you “failed” a section.

Is everyone in your class graduating?

One person left for personal health reasons and he’s actually going to the next bootcamp cohort. I believe one other person left very early on but I don’t know what happened.

Can you tell us about your favorite project that you worked on? 

There’s a really large range in terms of the scope of these projects. The coolest project that I worked on was a web scraping project with another classmate. We created a tool that ingests the 20 most recent articles written by any New York Times author and does analysis including word frequency, sentiment analysis, and provides a personality profile of that author. We used the New York Times API and then created a scraper to pull the contents of the articles. Then we did the analysis using some Natural Language Processing Python packages. Lastly, we used an API from IBM Watson called Personality Insights. We fed the twenty most recent articles into the Watson API, and it outputs a numerical, in-depth personality description.

What was the job search process like?

The bootcamp provides a lot of job search resources including making introductions, hosting networking events, and bringing in a resume specialist. I found a couple of companies that were health driven and seemed like a good fit. We were excited to work together, but in the end their data science team was so small that no one would be able to mentor me while I got used to the workflow. They needed someone who could jump in and take on an entire project on their own.

I think that’s typical of any bootcamp grad. They need mentorship in their first months.

Because data science is such a new field a lot of companies have very small data science teams and very little room to take on people who are in the beginning stages. There are places that have much bigger data science teams and have positions like junior data scientist or data analyst. In talking to my classmates, that’s something that they’ve come across as well.

Were you able to get through the technical interviews for those junior data scientist roles?

I just had lunch with one company and both of us acknowledged that they needed someone with a PhD in Molecular Genetics.  For the other one, there were four steps to the technical interview and they said, “You were fine on three of them and on one of them you were a little nervous.” I was pretty happy with how I did though. I had just learned Python five weeks ago and I coded out an answer to an algorithm problem in Python that the interviewer seemed happy with. Even though I may not necessarily be able to do a project completely on my own immediately, I was very happy with my increase in knowledge and I think they were as well.

In the midst of that interview with IBM, did you convince them to change you to the technical team?

Not exactly. I think we both realized that my strengths lie in between and that if I was put in a purely technical role, I would be very behind. The role I’m starting in is more along the lines of product management.

What was IBM’s reaction to NYC Data Science? Did you tell them that you were doing it?

They thought it was really really great. In the end, what AI comes down to is machine learning and data science. They were very excited about it.

Did you think that it was worth the tuition? Are there types of people that you would not recommend this bootcamp for?

In order to get out of it what you deserve, you have to put a lot into it. Vivian (founder of NYC Data Science Academy) said that throughout the interview process and when we first started. She was very clear. She said, “We can’t give you results unless you put in the work” which is 100% true. This bootcamp is for anyone who is extremely motivated and sees data science as their future. But at least some previous knowledge of R and Python would be extremely helpful. For someone who wants to casually develop a working knowledge of data science, a General Assembly or Coursera course is ideal.

Is there anything we didn’t cover that you want to make sure we include?

I’ll mention this again—the quality of the students really makes a program. The teachers are great, but the students are the ones you actively work out problems with. I’m hoping that I’ll have this alumni network that I can call upon for a very long time.

Interested in learning more about NYC Data Science Academy? Check outreviews on Course Report or their website here!

Vivian Zhang

Vivian is the CTO and School Director of NYC Data Science Academy and CTO of SupStat. With her extensive experience working in the data science field, she developed expertise in multiple programming languages, including R, Python, Hadoop, and Spark. In August 2016, Forbes ranked her amongst one of the nine women leading the pack in data analytics. In 2013, she created the NYC Open Data Meetup group, which stands as one of the largest data science communities offering meetups, conferences, and a weekly newsletter. In her spare time, Vivian enjoys meeting people and sharing her motivational stories with our students and other professionals

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