Founder Spotlight: Vivian Zhang, NYC Data Science Academy

A former statistician at Brown University, Vivian Zhang started a Meetup group in New York City in 2013 teaching topics in computer science and data science. As the meetup size and demand grew, NYC Data Science Academy was born, and what started as a set of weekend classes has grown into a full-fledged Data Science academy, which features a 12-week data science bootcamp. We sit down with Vivian to talk about the importance of the R language in data science, keeping a tight schedule at a bootcamp, and the types of applicants who excel in the course.

Remember, the Course Report community is eligible for a $500 scholarship to NYC Data Science Academy!

 

Tell us about your background and your experience with education and data science!

I got a Masters degree in Computer Science in 2008 and a Masters in Statistics in 2009.

I was working as a statistician with Brown University. I worked with professors, writing the code for their papers; I designed my career path so that I could learn as much as I could.

 

What motivated you to start the first NYC Data Science Academy classes?

I do a lot of volunteer work; in May 2013 I started my meetup and started teaching people computer science classes. After a while, the meetup group asked me to offer a class, which is how I got started. We started to offer weekend classes in November 2013.

 

Why did you decide to expand into the 12-week bootcamp?

We saw more and more students were getting jobs as data scientists after just taking the weekend class; we thought we could help more people change their lives by offering the full-time bootcamp.

 

What types of students do you see at NYC Data Science Academy?

We train three types of people. More than half of them were already in data analytics roles, and most of them were engineers. We also see ~20% who are senior managers; they manage 50 data scientists and need to know what they’re doing right or wrong. So we train senior managers who were already in positions.

We also see around 30% who want to switch careers. They are software engineers or business analysts. I saw a lot of people from academic backgrounds who are stuck in their post-doc, struggling to find a professor position. It’s not easy to make the jump but it’s good that they have a good analytic background so I’m very confident that I can train the programming side and get them ready for the job.

 

Do most of your students have undergraduate degrees or are they PhD students?

In the current cohort we have 14 students on campus. We have two students who are engineers taking the class remotely from Atlanta. Those two have Masters degrees. Of the 14 people we have in New York, we have 6 PhDs, 6 Masters and 2 bachelors.

The two bachelors degrees we admitted already had a good statistics background. Also, they were already doing data analytics work. They quit their jobs right before the bootcamp. Most people quit their jobs the last day of January to join the bootcamp.

 

Why did you start the Data Science Academy in New York instead of another city?

I used to live in Silicon Valley. I think New York is becoming the second Silicon Valley so it’s the best place to start training data scientists.

 

NYC Data Science Academy teaches R as well as Python. Can you tell us a little about R as a language and why it’s important for data science?

I own a data science consulting firm and do data science for a living, and I always find R has an advantage over Python because it has more than 6,000 packages and there are 4 million R users globally.

Since day one, when R was born, it was designed for data analytics and statistical learning. Python is more for software engineering. I would classify Python, Java, and C++ in the same group. People in the Python community are migrating their components into the data science field, which is why they now have Non-Py and Panda. You can use the same syntax to cause a similar function in Python. So it’s getting there but not as far as R gets.

 

How did you develop the curriculum for the bootcamp?

We spent 15 months testing that out before we started the bootcamp. All the lessons we teach in the bootcamp, we offered over the weekend class; so the lesson on R for beginners, we’ve taught more than 10 times now and the machine learning lesson we’ve offered two times. We validated our material by teaching it at meetups and the weekend class - so when we started the bootcamp, we were ready; we spent months to get here.

 

How many women do you have in the class?

We have two. In the June cohort we’re going to have 5 women.

We announced the bootcamp on December 15, admissions finished on January 15, and the class started on February 1st. Given such a short amount of time, women were more hesitant to make such an investment.

This time promotion started early and we already have 5 women. I feel like if we give longer time for consideration, we can get more female candidates.

 

What is the application process? Do applicants need to have technical skills or do they need more logical skills?

We have a programming question in the application form. We want to know what technical level you are, how you work with a team and the most difficult work you have done regarding data analytics.

We need to know if you can be a good candidate for a data scientist position

 

Did you get a lot of applications for this first cohort?

This batch is amazing. We’ve got a director from Deloitte who quit his job to do the Data Science Academy. He worked at Deloitte for 25 years, got so excited about data science, and decided to become a Director of Data Science instead of Director of Finance.

 

What kind of job preparation or guarantee are you able to give students? Do you have formal hiring partners?

First, I’m very well connected in New York. I have meetup groups that have more than 3,400 members, with a lot of well-known members. We are also planning a job fair so hiring partners can come in to see students’ work.

Even for my weekend class, students will do a demo day. Last time we did Python class demo day. We have people come in to see the students’ work as they finish the 20-hour class.

I remember one time on demo day, a student brought their grandparents and a parent and they made it like a graduation ceremony.

 

Are your students in the bootcamp working on a project throughout the whole course or small projects?

The day the get admitted we will start to work with them. Within one day they are making their first project. The day we accept you, we start to work with you.

 

What kind of pre-work do you expect students to complete?

One of our requirements is we want you to finish 9 Coursera classes before you start class. You need to finish them before you come, unless you run out of time. We also ask you to write 5 project proposals before the boot camp so we can work on examples that attract their attention.

 

Who are the instructors?

I’m teaching and two of my past students are helping to teach. We have Janet, who had a PhD and MBA. She’s covering the statistics side. We have Brian who graduated from CMU, he’s a CS major. Both of them took my class a year ago and now they are working with me.

I do a three-hour lecture every day. We run the boot camp like West Point. We start at 9:30am, finish at 12:00pm then we have another session from 2:30 to 3:30. I teach R, Python and Hadoop, Brian teaches 3DS and Github.

I think a lot of bootcamps have a really loose structure. I think it’s more efficient if students can get their body and mind prepared. Every day students need to do preview for the next class, you need to do homework, you need to do projects. In the first half hour we do code review, we do presentations, we record all the student presentations. So in week one we really do micro-orientation.

 

Is there anything else that you wanted to add about Data Science Academy or bootcamps in general?

We encourage hardworking smart people and really dedicated people to apply. This is a gift for yourself. We don’t often get the chance to learn every day for three months so we hope people take that gift for themselves and gain the benefits. And student should keep learning.

 

Want to learn more about NYC Data Science Academy? Check out their School Page on Course Report or the NYC Data Science Academy website!