Data Science Bootcamp Open House: Alumni Panel Discussion

Data Science Bootcamp Open House: Alumni Panel Discussion

Posted by Queeny Lin

Updated: Feb 5, 2016

Data Science Bootcamp Open House Alumni Panel Discussion

(Image: Panel L to R: Christopher Peter Makris, Instructor at NYC Data Science Academy, John Montroy, Data Scientist at theLadders, Fangzhou Cheng, Data Scientist at the Earnest Research Company, and Janet Kamin, member of Admissions team.)

Panelist Backgrounds

On the night of January 28th, we hosted an Open House of Data Science Bootcamp. It was a great pleasure to have bootcamp graduates John Montroy and Fangzhou Cheng, who have been working at the Ladders and the Earnest Research Company, join the panel discussion to share their experience at NYC Data Science Academy and respond to questions from attendees. Christopher Peter Makris, Instructor at NYC Data Science Academy, and Janet Kamin, Admissions Director, also joined the discussion.

The panel discussion offers great perspective and insight for those who are serious about entering the data science field. See below for a full transcription of the discussion. The video is also available online.

Introducing Our Panelist

Janet: Christopher is one of our teachers here. John M just graduated in December. Fangzhou graduated from the second bootcamp. How about telling us a little bit about yourselves?

John: My name is John, I graduated from college in 2012. I joined the 3rd cohort and started searching for jobs right after that. Two weeks ago, I started a new position at the Ladders here in NYC.

Fangzhou: I’m Fangzhou. I was here during the summer in 2015. Before that, I was a master’s student at NYU and I was majoring in information systems. After that, I started my first round of job hunting. The bootcamp just made it easier. I’m currently working as a data scientist at a financial research company. We do research for hedge funds. We maintain their database in the AWS system and we use Hadoop and Spark for pre-processing.

CPM: Hi, my name is Christopher Peter Makris. I’m one of the main instructors here for NYC Data Science Academy bootcamp. I specialize in R and Machine Learning. My background is in logic, discrete mathematics, and statistics. I got my master's and undergraduate degree from Carnegie Mellon University at Pittsburgh.I have a passion for teaching, specifically data science.

Q: John, could you tell us about your current position?

John: I worked at the Ladders now which is a resume and career startup. I do things involving both data science and data engineering there. I went to Middlebury College and majored in physics.

Q: What is the price tag for the bootcamp? Is there an application fee?

Janet: The bootcamp is $16,000. There is no application fee.

Q: How soon do you usually respond to an application?

Janet: We typically respond to an application within 24 hours via email. The objective here is to set up a phone call, largely to answer any questions you may have about the bootcamp. We also use this call to schedule an interview. The admissions committee meets on Fridays. That is when we make all our decisions. If you have an interview on Tuesday, for example, you’ll hear me back by that Friday. It’s a fairly short process.

Q: Is there a deadline for accepting an offer?

Janet: Yes, once we accept you, we give you a week to finalize your decision. We work with everybody individually to design a pre-study program in advance coming to the bootcamp. The idea is that the more advanced you are when you get here, the more advanced project you’re able to do and the better job you’re able to get. So the sooner you get your application in, the sooner we can get you on board with a plan actually makes a difference in your life in the long run.

Q: What are your job placement rates?

Janet: About 85% of our students get a job in less than 3 months of graduation, 96% of our students within 6 months. The 3rd bootcamp that just graduated in December, more than half of them have already been hired.

Q: How many people have gotten jobs, specifically, as data scientists?

Janet: Almost everyone I know of has gotten a job as a data scientist. For the most part, people get jobs as data scientists.

CPM: It also depends on our students because some of our students are going to be consultants, where their title is technically not going to be “data scientists”, they’re consultants in data science.

Q: What is the minimum programming knowledge required to be accepted into the bootcamp? For students who don’t have the required knowledge, how do you think we can figure that out?

Janet: It depends on a couple of things. There is no straight answer. For example, Fangzhou, you weren’t a programmer before right?

Fangzhou: Yes, I was not a programmer before.

Janet: And you were one of the first people in class to get a job?

Fangzhou: Yes, I got the job offer before the end of the bootcamp.

Janet: We look at a lot of things when looking at applicants. If you have very strong aptitude in something, even if you don’t know programming and there’s enough time before the bootcamp to get you up to speed, then your program is not that important. We can teach you programming. If you have some extraordinary expertise in something, some extraordinary domain knowledge in some area, and that’s the area you want to go into, then we’ll work really hard with you to make sure you learn everything you need about data science because you’ve got this leg up already. So every applicant is looked at differently.

Q: I’d like to know what failures we might encounter. How many people actually finish the bootcamp?

Janet: We’re in our fourth cohort now and there was one person who did not finish. We’re pretty good at selection but even more than that, we’re pretty good at making a commitment. If we believe in you enough to bring you in here, we’re going to put in as much support as you need. We have two teams working full time.

CPM: There’s also this level of passion that we look for in our applicants. It’s not only someone who, on paper, has programming skills or has a background in statistics. You could be perfect on paper but not have that passion to get the job done. You won’t be accepted into the program if you don’t have that fire. Our students here are working as hard as they can. If you’re going to learn so many things in a 12-week span of time, you have to have the passion and drive to change your life. You have to have that drive to make this career happen quickly. Fangzhou had no programming background whatsoever but she was one of the first people to get a fantastic job review.

Q: You have homework, you have projects, you have to worry about jobs. You have the three hour lectures and guest speakers. How did you manage your time, your mood, and your health?

Fangzhou: I was here for the summer and I was determined to get into the data science field. I had no data science projects on my resume and they would love to give me roles in system analysis or project management but not data science. I came here every day and I left sometimes 10PM, sometimes 7 or 8, but I stayed really late to try to finish everything. That was my motivation. Also, once you get some projects on and you want to make it perfect, that was my push too, to get things done. When the guest speakers are here and they’re really interesting, we would communicate. If they’re not, I would focus on my projects. I was really into this field and I want to absorb as much as I can because I paid a lot to get here.

John: I can only echo what has been said already. I would get on the train, open my laptop and look at some stuff. I would stay 6:30, 7:30, 8:30, take the train back. Keep going through some stuff and go to bed. Next day, do the same sort of thing. But you’re spending these long hours with people who are doing the exact same thing and people who want to be doing the exact same thing so you’re spending long hours with all these great resources, starting with the same problems, and it’s really motivating. There were frustrating points, sure, but compared to doing it all on your own or something like that, just having everyone around you was really motivating. They were all friends too at the end.

Fangzhou: There was also peer pressure. I didn’t want my project to look worse than theirs at the end.

CPM: That’s also another good point. The cohort becomes a family and because you have so many people at different cross sections in this venn diagram, no one is going to be left behind. Someone who has a vast knowledge of our programming will help those who don’t. When you get to the Python section and that person doesn’t know Python and the other person knew Python, then you start building that relationship and also get a network here as well, just within the twenty or so students that you’re with, day in and day out. You become a family.

Q: What is the age range?

Janet: The youngest person we ever had was 21 and the oldest person was in his early fifties.

Q: I’d like to hear, what is your favorite project?

John: Actually, my favorite project is what I’m still working on with another guy from the bootcamp right now. It was very infrastructure heavy. It was a lot of AWS but basically, the core of it was using NLP oriented stuff with Python to scrape a whole bunch of different websites and do a lot of algorithm mixed stuff and start comparing between Venture Capital firms and start-ups to do all sorts of fun stuff. We’re still working on that now and we’re excited about it. It was really intense data science and algorithms. You have the skill set from the bootcamp to start building up the infrastructure. I’m still enjoying that one a lot.

Fangzhou: One of my favorite projects is from the bootcamp too. Actually, thanks to the network here, because you have a lot of guest speakers coming, that was one project with a real world company. It’s called Fusion and it’s a media company similar to BuzzFeed. They came to our bootcamp hiring people to work on projects. They wanted to solve problems regarding their Twitter and Facebook followers because 90%, over 90%, of their web traffic on their main website was directed from the social media account. So they wanted to figure out the influencers because they don’t have that kind of analysis done in companies so we wanted to reach out to them to see if they can tweet more just to attract more web traffic. So our project is to use Twitter API and NLP, natural language processing. We used alchemyAPI tools which the company was already exploring a little. It is also a NLP tool. Then, using a shiny R app, you can see which point, which tweet, your traffic gets boosted the most. They were pretty pleased with the project. I talked to almost all my employers and they really liked it.

John: Like with my project as well. They’re really good with talking to employers.

Fangzhou: That’s true. You can explain a lot of details which makes you feel like you know a lot.

Q: How much statistics and programming is required for the program?

Janet: This is very similar to the prerequisites are and how many programs you need to have.

We work with students in advance to get their skills up to a level where they can start the bootcamp. We do expect them to have some background in statistics but there are some online courses we recommend; some books we recommend so those are part of the required pre-work if you have limited statistical background. Certainly with programming, there are some programming websites that we recommend. Also people who are in the New York City area, we give courses and we let our bootcamp students take those courses for free so they can get to speed with the bootcamp in advance. But we work with every student individually.

Q: With your current position at financial services, how important is your previous education in relative to the education at NYC Data Science Academy? Did they look at all at your previous education? Did they basically just hired you from what you gained here?

Fangzhou: For my role, it was more like a data scientist or data engineering role. For smaller companies, they were kind of both. When I was in interviews, I talked to the employers. The main questions for me was more like the skill set. We have tons of data analysts who have a financial services background who work for big banks but they just were talking to me, more like behavioral, to see if I had faith in the team but it’s more skill-set based. I didn’t have any financial services background and I don’t have any now either. I play with the data and I make sure everything in the database is right. I do a lot of QC, I make sure that everything I give to the data analysts are up to date and no duplicates but I don’t really know what these numbers actually mean. But I don’t need to, like I know the basics, I’m not an idiot. I’m just saying that it’s not required for you to have intensive experience.

Fangzhou: They were really interested in my NLP processes so I analyzed Twitter and they have transaction data so they have description so all the text messages were everywhere. So in order for them to understand their data set, my projects and what I learned at NLP camp would be applied to that. Those are the things that they care about.

Q: Do you teach deep learning?

CPM: Yes. So we start delving into deep learning with neural networks. That’s one of the most advanced topics we get into in terms of machine learning. We kind of skim the surface of deep learning because we have some students who have no statistical background whatsoever. That’s kind of our delve into that area.

Q: Imagine that you’re sitting in a seat in the audience right now, not where you are, what advice would you give somebody either who’s considering coming to the bootcamp or someone who’s in the bootcamp, what advice would you wish someone gave you at the beginning of the bootcamp?

John: For the record, someone did give me this advice, multiple people did. As I was listening to it, that’s the first time I’ve ever seen that presentation. There’s a lot of stuff listed there, connections and jobs stuff. I even thought to myself, “Wow, there’s a lot of stuff listed” and then I would check and yeah, that all did actually happen the way it’s going down and the resources are and were really available. My advice, which I got, is if you do a program, take advantage of all that. Your free time is going to take a pretty serious hit but it’s all great stuff and you’re around so many resources and smart people who want to help you do all this stuff. So just from day one, I think the first thing I did in the first week was get in the meetup group and start seeing what meetups were going on and start talking to other classmates about their background and what sort of problems they’re interested in working on and technologies. There was never a single door I knocked on that was either locked or like once it opened, there was so much available to look into. Once again, you’re going to be working very hard but it’s all very worthwhile.

Fangzhou: I agree with all of this and on top of that, I have two more pieces of advice that I would want to hear from my beginning. The first is whatever your weakness is-- for me, it’s stats in this bootcamp. So when you are looking through the curriculum, check which part you’re not sure you can handle. If you have that part, start working on it before you join the bootcamp so that you feel less pressure and along the process, you know that these are the points that you need to get and you should probably get it earlier before the course because everything in data science comes from one central place. You don’t want anything to become your weakness or else you won’t have the intuition and you won’t understand the data in the right way. That’s the first advice. Second is more idea based. Before you join the bootcamp, I would suggest you think of your interests for your data science project because I know there are a lot of wonderful instructors and TAs here. If you don’t have an idea of what your project is going to be, you’re useless. You’re not useless but if you have projects-- I did traffic data, there’s the website data, NYC traffic data, there’s a lot of data sets you can download. Just look through it and think of financial data, traffic data, healthcare data, whatever industry interests you the most. Then you’re probably going to find similar data over at that industry and start thinking upon it so that you know what exactly you want to get out of the bootcamp at the end. Just make sure, tell the TAs, tell the instructors and ask the right questions.


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Queeny Lin

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