Alumni Spotlight: Wendy Yu, Data Scientist at ASCAP

Alumni Spotlight: Wendy Yu, Data Scientist at ASCAP

Posted by Claire Tu

Updated: Sep 14, 2016

This article is contributed by CourseReport, a third party website specialized in covering bootcamp stories.

Wendy was a biologist studying the sense of smell when she started using machine learning in her research. She liked it and wanted to learn more about algorithms and statistics, so she enrolled at NYC Data Science Academy (NYCDSA). Now Wendy is working as a Data Scientist at ASCAP, predicting trends in the music industry! Wendy tells us why she wanted to learn both R and Python, how much she enjoyed learning with her main instructor, and how NYC Data Science Academy was instrumental in helping her get her new job.


What were you doing before you started at NYC Data Science Academy?

I got my masters of biotechnology at the University of Pennsylvania; so that’s very different from data science. When I graduated, I got a job at a private research lab studying olfaction, the human sense of smell. While I was at that job, even though it was biology, I used machine learning algorithms to make predictions on whether a compound will have a smell or not. So that was my first exposure to data science.

Wow, I’ve never heard of someone studying smell! Can you tell me about it?

My study was to define the space of smells. So for example, for colors, we know there are three primary colors, red, blue and yellow, and if you mix them you can create all the colors in the world. But we don’t know how smells are created, or what are the primary smells. More importantly, we don’t know the space of the smell. I was looking for what makes a compound smell, and the reason behind it.

So why did you want to do a data science bootcamp?

At my last job, I was practicing machine learning on my own. I could write code but I didn’t really know how algorithms actually worked, and what was going on behind the screen. So the main reason for me to go to a bootcamp was to understand the statistics that go into an algorithm. In terms of field, my entire educational background is in biology, but I didn’t really want to limit myself to just biology. Machine learning is a technique that I can apply to different fields, so another reason I went to bootcamp was to open up my job options.

Did you try to learn on your own before you thought about a coding bootcamp?

My boss at my old job sent me to a few workshops, and I also learned from him along with teaching myself. So before I joined the bootcamp, I had been practicing for two to three years. Yet, I needed the hands-on experience.

Did you research other data science bootcamps in NYC?

I wanted to go to one on the East Coast because I’m from Philly so it’s closer. I looked into a bunch and I applied to three – NYC Data Science Academy, Galvanize, and Data Insight.

What attracted you to NYC Data Science Academy?

There are two reasons. First, I really liked their syllabus because it is more thorough than other bootcamps in NYC. NYC Data Science Academy teaches both R and Python. I had been using R for many years and I think R is pretty important, so I wanted a bootcamp that would teach both R and Python. (A lot of NYC bootcamps just focus on Python.) The second reason was the opportunity to do four or five projects throughout the bootcamp. Other bootcamps I researched had fewer projects. I wanted to do more so that when I applied to jobs, I’d have something to show employers.

Did you think about studying data science at a college?

I already have a masters degree, I didn’t I want to go through that again, it’s pretty expensive. Data science bootcamps are quick, relatively cheaper, and teach all the skills that you need. The amount of time you put in is equivalent to a whole semester.

How did you pay for the NYC Data Science Academy tuition?

I paid out of pocket because I could afford it. For students who are looking for a bootcamp, something to consider is that NYC Data Science Academy doesn’t provide any loans, and they don’t have scholarships. A few bootcamps I looked at, like Galvanize, do have scholarships. Overall, the other aspects of NYC Data Science Academy outweighed the need for scholarship, so I decided to go there.

What was the NYC Data Science Academy application and interview process like for you?

The first step is an online application. Then NYC Data Science Academy gives you a coding challenge where you can use whichever programming language you want to answer the questions. It wasn’t terribly hard as they want to make sure you have some basic coding experience. After the coding challenge, NYC Data Science Academy contacts you to schedule a call. The purpose of the call is for you to ask questions, and for them to assess if you’re a good fit. If you’re chosen to move forward, you’ll have an onsite interview with an instructor. For the onsite interview, they asked about my background, and my goals after bootcamp.

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

There were 20 people in my NYC Data Science Academy cohort. Out of 20 people, we had four girls, so not too bad. It was pretty diverse where half were white, about a quarter Asian, and a quarter other races. Our cohort was a really smart group of people. About a third of them had a PhD and had just finished school. Another third were probably in their 40s or 50s, and already had pretty successful careers. I was really surprised because a few of them owned their own companies, and weren’t looking for jobs, but just wanting to learn new skills. The last third of the group were people like me with Masters degrees. Plus, there were two people fresh out of college. The majority of people had a graduate degree and a few years of work experience.

Who were your instructors and what were they like?

We had three instructors at NYC Data Science Academy. Our main instructor is responsible for teaching machine learning, statistics, and the coding in R. He is very knowledgeable and had plenty of work experience before he came here. He was an actual data scientist, and his educational background is very heavy in statistics. I really like him, he’s very knowledgable and very well rounded in statistics and in machine learning. Then we had another instructor who was responsible for coding in Python; his background is a PhD in math. The last instructor taught Hadoop and Spark, the big data tools. He is a bit older and worked at Google for about 10 years.

What was the learning experience like at NYC Data Science Academy – what’s a typical day and teaching style?

So the bootcamp starts at 9am every morning. From 9am to 12pm, we have a lesson, with one break. The course is pretty intense and interactive. For the teaching style, every instructor is slightly different. We spent most of our time with Chris and he makes everything pretty fun, the way he teaches us. He’s very fluent, and has a great personality so you’re never bored. If we have questions, he encourages us to just ask when it comes to mind as opposed to waiting until the end of class. Sometimes we’d have a competition, where we’d do a small project in groups, and present it to the class.

In the afternoon, it’s a bit more flexible. If we didn’t finish a lesson in the morning, sometimes it runs into the afternoon, or we do homework review. For every lesson we have homework, and then we have four TAs to help us review the code. Sometimes, we have guest speakers from the industry to help us prepare for our career.

What hours did you and the other students usually put in?

Every student is different. I’d get there 9am, some people would get there 7am or 8am. In the afternoon, our homework reviews generally went until 4pm or 5pm. A lot of students would stay and work on homework or projects as the TAs stay until 9pm or 10pm. So you could spend an entire day (or 16 hours) there, and some students do. I usually went home after homework reviews because I would get tired and I need a break.

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

So we had a few different projects. The first project was data visualization, which was really fun. All the data I plotted in my last job was static, but during the data visualization project, we learned how to use Shiny, which is an interactive app you can use to build interactive graphs.

The other project I really liked was the web scraping project, which was in Python. It was great for boosting my Python skills! With web scraping, you can pretty much scrape any website you want. One of the challenges I found in doing all the NYC Data Science Academy projects was finding a suitable data set. It can be hard to find good data sets, and hard to validate the source. But with web scraping you create the data set, so you know the data is good and the structure is what you want. So I did a web scraping project to see if I were to buy a house in NYC, where should I buy it, if I want to rent it out as an investment. It was down to a few different boroughs, but I think the top area was somewhere near Williamsburg in Brooklyn.

How did NYC Data Science Academy prepare you for job hunting?

The entire bootcamp is 12 weeks, and we started the career help in week 8. We had a resume coach look at our resumes and help fix them, then we also learned how to fix our Linkedin profiles. We had a workshop on interview techniques- how to dress and how to speak. In the last two weeks, potential employers came in to interview us. Two or three companies came and each student got at least two or three interviews – which NYC Data Science Academy guarantees.

Lastly, we had the hiring party where we spent three hours meeting potential employers. We met around 25 employers. The hiring party is very effective because all the employers who came out are obviously looking for data scientists. When I followed up with them, my return rate was almost 100%, and a lot of those turned into interviews.

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

I’m a data scientist at ASCAP, the American Society for Composers and Publishers. We are a music company; we handle the performance rights of songs, and we represent the songwriters and publishers. So for example, you write a song, and then when your song gets played you need to get paid, so we are the middle man between you and how you get paid. So think you and the radio station, or you and the TV. We will represent your rights and if someone wants to play your song, we charge a fee, then we give you your money.

How did you find your job and when did you start?

I found my new position thanks to the NYC Data Science Academy hiring event. ASCAP’s HR contacted me before they came out to the event. The bootcamp sent our resumes out to potential employers to contact us before the hiring event. So that’s how I got connected.

The bootcamp ended April 1st, and I started interviewing about a week before that. The interview process was quick because I received the offer a week after the bootcamp, then started May 1st.

What’s your specific day-to-day role?

We have a few different projects going on. The main one I’m working on is to predict music trends. So for example, say you want to predict the trends for the song Hello by Adele. We know the song is a really big hit right now, so we know it probably will get played 1000 times a day on radio stations, and we want to predict what the song is going to look like three months from now, if it’s going to play 2000 times, or if it’s already in its declining phase.

What is the company like and how big is your team?

The company is not new, we just celebrated 100 years, so it’s definitely a company with some history. Our data strategy team is brand new, it’s been around less than a year. My team is still growing but we are pretty well rounded. We’re part of a bigger team, but I work with five or six people every day. I’m the first data scientist here, and we’re looking for more. That’s a very good thing about my job because I have a lot of flexibility to do the job the way I want to. And I can report the insights I find directly to my manager and to all the senior management, so my voice is heard in the company.

Are you using the technologies you learned at NYC Data Science Academy?

Yes. My day to day job is pretty much data analysis and machine learning all day long, so I use both R and Python as well as Spark.

Is this the job you wanted? Do you feel like you reached your goal?

Yes! I’m pretty happy here. I like the job I’m doing every day, because it really is a data science job.

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

Yes. Since you spend eight hours a day with all the students, you become friends. I’m still pretty close with two girls from my cohort. And since our instructors were really nice to us, I still go back and visit every now and then. Everyone becomes this family within your cohort so people still stay in touch and occasionally get dinner together.

What were the best and the most challenging things about studying at NYC Data Science Academy?

I definitely learned a lot there, but the best thing was the job connection.

The biggest challenge for me was time management because we had a lot of homework and projects were due every two and a half weeks. You spend a lot of time doing both, and sometimes if don’t have enough time, it’s hard to pick and choose how to fit everything in within that finite time frame.

What advice do you have for people who are thinking about doing a data science bootcamp?

Two things. First, I encourage everybody to apply early, because after you get accepted, you can spend that time before the bootcamp to start improving your skills. They do teach from the very beginning in terms of coding, but if you don’t know any languages it will be pretty hard for you to follow. So you’ll want to spend some time studying both R and Python.

The second thing is, trust the system. NYC Data Science Academy had us do four or five different projects, then we fixed our resumes and LinkedIn, and started applying to Jobs. One thing I did, that I wouldn’t do again, was that I started to apply for jobs fairly early, about a month after the bootcamp started. Back then, I only had two projects to show, plus my resume and LinkedIn weren’t fixed. I know I missed a few good opportunities because I wasn’t fully prepared. My advice would be to wait until towards the end of bootcamp, until you’re at least 75 percent prepared, before you start sending out your resume.

Claire Tu

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