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The Student Perspective on the Metis Experience (Course Report Interview)

By Emily Wilson • October 30, 2015

Course Report hosted a webinar with two recent Metis Data Science Bootcamp graduates, asking them to describe their experience throughout the program, to talk about their projects and gained skills, and to discuss the job market since graduating a few months ago.

During the 40-minute chat, Course Report Co-Founder Liz Eggleston asked former students Emily Schuch and Itelina Ma to open up about their time before, during, and after Metis.

It's a particularly interesting discussion because the two women come from very different backgrounds, in terms of experience and career. Emily comes from a fine arts/data fusion past and is interested in the visual presentation of data, while Itelina has worked mostly with data in the healthcare sector.

Emily is now employed as a consultant at the United Nations, working on a dashboard designed to present data about global warming. In the interview, Itelina goes into detail about the many interviews she's landed in recent months and the job offers she's received, also detailing her resolve to take her time and explore interviews until she finds a career fit with a company that will make an impact on society.

Read on for a truncated version of the interview, and/or watch the video by clicking on the image below.

INTRODUCTION

Liz: I'm really excited because today we are joined by two graduates of the Metis Data Science Bootcamp. Emily [Schuch] and Itelina [Ma] are going to tell us about what they were up to before they started at Metis, their experience at the Bootcamp, and their lives after graduation. We will get started with these questions. We'll see where they take us.

What were you up to before you started at Metis? Tell us about your education background, your last job.. Emily, do you want to start?

BEFORE TAKING THE METIS DATA SCIENCE BOOTCAMP

Emily: Before Metis I was working for a small branding firm, where I was doing qualitative research and visual strategy...brand strategy. I really like doing research. Prior to that job, I had a BFA [from NYU], which isn't necessarily something you think of when you think of data science. I got really interested in data visualization, and I just really wanted a lot more quantitative skills.

I started taking courses in the evening, in statistics, and that just kind of snowballed until I found out about Metis and this data science course, and I thought, "Okay. I want to know all of those things that they are teaching."

Liz: I love that trajectory. I feel like sometimes when we think of data science, we think quantitative, statistics, math...that side of the brain. But data visualization, D3, those JavaScript libraries, can actually be super creative. That's an awesome background.

Itelina, what were you up to before you went to Metis?

Itelina: I have a background in mathematical economics from Princeton University. I graduated from there about four years ago. In the last four years, I've been working as a management consultant at PricewaterhouseCoopers. I work mostly with healthcare clients, so I work with hospitals, insurance companies, and pharmaceutical companies. I did a lot of projects that involved solving problems with data, so I was often the lead in my projects on the analytics work stream. I would work on analyzing data from hospitals or from insurance companies, noticing different patterns in cost of care and quality of care. That got me really interested in solving problems in data, and I also did some of that in my undergraduate degree. I really wanted to find a program where I could learn more on some of the latest techniques and technologies...and Metis was the perfect program.

Liz: Itelina, you were doing some data analytics projects. What clicked and made you think: "I want to be a data scientist, and go to a bootcamp, and get that data science job after I graduate..."?

Itelina: When I was an undergraduate doing research in economics, we would use some statistical software, such as Stata, and we would analyze data. While consulting, analyzing data...isn't always the primary focus, because I was a part of the group where we do projects in strategy. But we started working with clients who increasingly had larger and larger data, and very early on I realized some of the traditional tools for data analysis, such as Excel -- which is what's commonly used at companies -- obviously isn't powerful enough to do the kind of things that we need to analyze large quantities of data.

I started taking courses online from Coursera. I took the data science specialization track and learned a lot of R programming and all of that. That made me realize there are a lot of technologies and tools out there. I wouldn't be able to focus on learning as much while I'm doing a daytime job. That was what made me realize that I wanted to attend a bootcamp program.

Liz: You mentioned doing Coursera. Emily, you mentioned doing a night statistics class, right?

Emily: Yeah, and I did some online courses as well, not through Coursera, but I did one that was through edX.

Liz: Did you only do online, or did you ever go to Meetups, or workshops in person, before you thought about plunging into a full-time bootcamp?

Emily: I hadn't been to any Meetups before. I did go to the open house that Metis had, just to check them out to see, was this really something that I was interested in? I was sold on the idea after I went to the Metis open house. After that, I researched the other bootcamps in New York, and I felt like Metis just really stood out to me.

YOUR DECISION TO ATTEND METIS

Liz: For both of you, what factors were you considering? Itelina, did you also look at other bootcamps in New York?

Itelina: Yeah, I did. Honestly I considered graduate school programs versus bootcamps. I figured that doing a bootcamp would be good for me for what I want right now, because it will be a shorter program, more practical. I looked at some of the other bootcamps in New York City. I think there's a bootcamp called Data Incubator. They're geared more towards students with a PhD. There was a couple of other programs. I just thought Metis was great for what I was looking for because it's very involved...instructors, full-day program, the curriculum looked great...that was why I chose Metis.

Liz: The world of data science bootcamps is a little bit different than the traditional coding bootcamps because some require a PhD, some require some coding skills, some just require a bachelor's degree. It's an interesting world. I'm glad you mentioned Data Incubator.

Emily, what were the other bootcamps that you looked at?

Emily: I can't remember the other ones I looked at. I looked to see which ones were being offered in New York. The main reasons I chose Metis were that they had their curriculum published, and I think there was another bootcamp in New York that was teaching mainly with R, and I was interested more in Python, because I feel like it's a language that's very versatile. It can be used for so many different things. Also, [Metis] touches on so many other different languages. They do touch on D3, which I wanted to get better at, as well as a lot of other languages. We did a little bit of SQL, and we did some MongoDB, and even a little bit of Hadoop. I wanted the full breadth of all of those.

THE METIS APPLICATION PROCESS

Liz: Since you mentioned Python and the languages you learned, tell us what the application was like. Did you have to do a coding challenge, and did you have to do it in a certain language? Can you take us through that experience?

Itelina: For the application process at Metis, they ask you to do a take-home challenge. You're required to do it in Python, but you could do it at your own pace at home. It's a set of problems for you to solve, and come up with some descriptive results and answers. It was just a take-home, and there was a video interview with one of the instructors. Overall, they did want to do a basic test on your background, but it wasn't super intense or super difficult.

Emily: I don't know if I had the same questions as Itelina. For my take-home, I don't think they said you were required to use any particular language. I didn't use Python for mine because I didn't know Python at the time. I actually figured out how to solve it [with] JavaScript, because that was the language I knew best at the time.

Itelina: That's awesome, because for me, I knew how to do the data analysis in R, but when I got the challenge from Metis I was like, "I need to learn how to use Python in a day." So I looked through a book and did the challenge. That's awesome that you did yours in JavaScript.

Emily: Yeah, and I just used the JavaScript console, and I think they were impressed with that, because they were like, "Oh, she'll be able to use the terminal, no problem."

Liz: I love that you mention that you have different technical backgrounds, because obviously, probably everybody else watching has different technical backgrounds and knows one language better than others. Good to know.

THE METIS EXPERIENCE

Liz: How many people were in your cohort?

Emily: I think it was 21.

Liz: Did you find it to be a diverse class in terms of age, gender, and race? It's something we think about a lot in tech, and in these bootcamps in particular.

Itelina: Yeah. I would definitely say so. In terms of gender, definitely. I think almost half of our class were girls, and half were guys. Age, definitely. We had people who recently graduated from college to people who were trying to make a mid-career change. I think [there was] a lot of diversity. We had people who were born outside of the U.S. -- definitely a lot of diversity in backgrounds.

Liz: And is [the field of data science] fairly male-heavy? Have you noticed that after graduation? I know it's only been a couple months.

Emily: I'm not sure, because I feel like it's such an emerging field. That's one of the things I noticed when I was looking for jobs after graduation...just how many people are trying to hire data scientists right now.

I've met people since graduating who say, "What do you do?" And I say, "I'm a data scientist," and they say, "My company's been trying to hire data scientists for months and months and months and they can't find anyone." That could partly be because there are more positions to fill right now than there are data scientists, but it also could be that different companies are looking for someone who's very specific to really fulfill their needs, so they're looking for a very specific type of candidate.

Do I feel like it's a male-dominated field? Possibly.

Liz: What was the teaching style like [at Metis]? Was it a lot of lecture? Was it a lot of lab or project work? Did it work with the way that you typically learn?

Itelina: Yeah. The general format for the bootcamp is, we would have lectures for a couple of hours in the morning. We would also have some pair programming problems to improve our coding style. In the afternoon, it was mostly individual work on your projects. The instructors would be available. You could ask them any questions you have -- or could ask any of your classmates. It's half structured, half individual work.

Liz: What did you think about the teaching styles compared to a traditional university classroom?

Itelina: I thought the format was very innovative. It's sort of project-based. Every two to three weeks, we do a project and we make presentations. It's very practical. It sort of simulates being in real work environments, where you are delivering results, often under short time periods. I thought it was a very innovative, good learning model, because you're learning as you're doing, but at the same time we have good coverage on the theory behind the different math models -- and also the technology tools. I thought it was a very good learning model.

Emily: I thought so, too. I thought that having the unstructured time in the afternoon...at first, it was sometimes hard to stay focused. But it did replicate a real work environment, in that you don't always have someone looking over your shoulder and telling you, "Oh, you need to do X, Y, and Z." You sometimes have to figure that out for yourself and manage your own time.

Liz: That's totally fair.

There were two main instructors. Were there also teaching assistants or former students that were around to help?

Itelina: Yeah. We had one teaching assistant. She was around to help everybody with the problem sets, as well as with questions. We had very good support.

Liz: Were there things you didn't expect, or that you would have changed? And was there a good feedback loop with Metis? Were you able to give feedback pretty regularly?

Emily: Yeah, they encouraged us. We had one-on-one meetings with one of the co-founders and other staff members. They like to do that a few weeks in, just to see how it's going for you, to see what's working for you, and what might not be, so they can make real-time adjustments to the course, to improve it. They encouraged us [to provide feedback] regularly. They occasionally asked us to fill out anonymous surveys, as well.

Itelina: I definitely agree. I think they were very active in getting our feedback and making adjustments on the spot. There were even a couple of times when, if there was a particular topic that we wanted to get more coverage of, they would schedule it ad hoc -- like a review session in the afternoon during our free working time. It was a very good process.

Liz: Did everybody that you started with finish with you? Did anybody drop out? Is there such thing as attrition at Metis?

Emily: I think we had one person who started, who left after the first week, which was disappointing. The other 21 people made it all the way through to the end.

Liz: I know this could be a really long answer, but what technologies did you learn while you were at Metis? Could you give us a quick overview? I know you mentioned Python, some Hadoop, some JavaScript libraries...

Emily: Python was the main language we used. For different projects, we were encouraged to use different technologies as well. SQL was one. MongoDB was for another project. Hadoop for another project. D3, as well...Hive, Spark...we covered those a little bit. You could use those, or not, depending on if you felt that they were necessary for your project. There were other people who may have had more advanced knowledge coming into the course, who got really into neural nets as well, and used neural nets on their projects. But that wasn't explicitly taught.

Liz: Emily, you mentioned your background before -- and [the fact that you] really wanted to get into more data visualization. Were you able to do that at Metis? Was there a lot of emphasis on D3 and data visualization?

Emily: Yeah, you could go as far into D3 as you wanted to, but there was a lot of emphasis on communication. The three main things that they talk about teaching at Metis are programming, statistics, and communication. Those are the three elements, because at the end of a project, where you've done all these complicated statistical models, you need to explain it to your client or the key stakeholders in the project. A lot of that communication comes from data visualization.

Liz: Itelina, because we have been talking a lot about the projects...you said you did them every two to three weeks. Can you tell us about your favorite projects that you worked on? What did you do?

Itelina: Yeah, sure. For one of my projects, I was working on some data that was published in a Kaggle competition. Kaggle is a company that hosts a lot of predictive modeling competitions. Sometimes a company will say, "We have this data set and we're looking for someone to develop a predictive model on this," and then they will host it as a competition, and there will be money involved.

I was using that resource; I was using Kaggle to get a set of data. That set of data contained information on patients and their medical records, and the topic was predicting a person's risk for developing Type 2 Diabetes based on their past medical records, including diseases, which doctors they've seen, which lab tests they've done. For that project, I worked on developing a predictive model for a person's risk for diabetes based on some of these factors. That was definitely an interesting project.

Liz: I love that because it's clearly [in line with] your former career, working in health care. You're able to use data science skills to solve problems that you couldn't have solved before.

Did you work on it alone or with a group?

Itelina: For that project, we had a group. I was working with a couple of classmates. We all picked health care-related topics. Another classmate of ours was focusing on wellness, so she was analyzing people's diet and what they were eating, and their blood pressure, and how that correlated to their risk for certain diseases. Somebody else was doing something similar, but it was with heart disease. So yeah, I was working with a group, collaborating on similar topics but from different angles.

THE CAREER SEARCH PROCESS

Liz: Emily, what was your favorite project?

Emily: It was probably my final project. I did analysis of some UN data. It was for a data visualization challenge that the UN was hosting with Millennial Development Goals data.

One of the goals [of the contest] had to do with curbing the spread of different diseases. I focused on HIV data and specifically found some interesting things going on with the spread of new cases of HIV in Sub-Saharan Africa. I looked at which countries were able to stop the spread of HIV. I was looking for causes of that -- and it was kind of hard to pull that out of the data -- but I think I still managed to come up with some interesting visualizations. I ended up as a finalist in the competition, and the UN took notice of me and offered me a job.

Liz: I want to talk about that. What are you up to now?

Emily: I am a consultant. I'm on a short-term consulting contract with the UN right now, and I'm working with them on a dashboard for exploring some data related to climate change.

Liz: What was that [hiring] process like?

Emily: I sent them [my project] because I had to make the entry into the challenge. Also, my school had a contact at the UN because they were looking to hire. I had sent in an application for that, too, and then I got an interview because they saw the work I did for my final project and thought it was good. They ended up interviewing me.

Liz: That's such a cool story. So, you're building a dashboard right now. I'm assuming that you're using data science skills for this project, right?

Emily: A lot of the analysis, yeah. At first when they were telling me about the project, I was a little bit scared because I knew it was only a three-month contract and I was like: "You want me to do all that analysis in three months? You want me to solve climate change in three months?" But they actually have a lot of analysts who have been working on this, and working with this data, for a couple of years. I am working with a team of people who are figuring out how to present it and build the dashboard.

Liz: What is it like graduating from a bootcamp and transitioning into a real data science position, where you're working on a team?

Emily: At first it was difficult because I feel like I've worked on so many projects individually. So [I had to] figure out how to set up a workflow in terms of sharing code and things like that.

But then on the other hand, I also feel like I need to shake my "imposter syndrome." I feel like I have so many skills. I'm working with people who are very skilled as well, but I don't feel like my skills are subpar.

Liz: Itelina, what are you up to since graduating from Metis? Are you going on interviews? Tell us what your life looks like now.

Itelina: Since graduating from Metis, I've been doing a lot of job searching. The process has been going a lot better than I would expect if I was just searching for jobs on my own, because Metis provides a great network. They have great career services team. In fact, even before I graduated from Metis, I actually had a job offer. I think Metis does a great job making the right introductions and connecting you to the right companies that are a good fit for you.

For me, I'm looking for the kind of job that [will] really grow my career, so I want to take a little bit more time to talk to different companies and see what's out there.

Liz: What's your dream position? What kinds of companies are you looking at? Mostly in health care?

Itelina: Yeah, I definitely want to work for a company that has a really good vision -- a company that is doing something cool that will make an impact on our society. I'm mostly looking at opportunities at mid-stage startup companies, although not exclusively at those. I'm looking primarily at opportunities in health care, but I've looked at opportunities in other industries as well, and definitely at data scientist/data analyst types of positions.

Liz: What is Metis's approach to job preparation? Did you do mock interviews, and/or interview practices like resume building?

Emily: I think we did all of the above. [Metis] did a really great job. We had workshops on writing resumes and workshops on writing cover letters. We also did mock interviews where we were actually whiteboarding. We had the opportunity to do mock interviews for soft skills and one-on-one meetings with career staff. Sometimes it felt like a lot to take in at the same time you're learning so many things, but it was really great, really supportive.

I've still been communicating with the career staff. They still get back to me if I just want them to look over a cover letter or to look at some changes I've made to my resume or anything like that.

Liz: Itelina, since you graduated, you've gone through a number of data science interviews, right? Have those been set up through Metis, or are they through your own networking?

Itelina: I would say, for me, partly through Metis and partly through personal network or connections -- but generally Metis. [Metis] helps us and makes an introduction, or tells us about a certain opportunity. They make that first connection and then we take it from there and see if there's a fit.

Liz: From talking to a ton of web development coding bootcamp graduates, I know what a coding interview is like for a developer job. What is a data science interview like?

Itelina: I would say for data science job interviews, it's very different from company to company. For some, all that's required is doing a take-home challenge, where you solve a couple of problems and show them your code, as a skills test, and then go to the company and visit or meet different people and see [if there's] a cultural fit.

[At other companies], I was asked to make a presentation to an entire group on a past project and [had to] defend my work. I've had interviews where people ask me questions related to SQL. I think it's really different depending on what each [company] is looking for and what particular set of skills they're looking for.

Liz: What has been the reception to your being a bootcamp graduate? Are companies interested in the fact that you went to Metis -- that you went to a data science bootcamp? Do they not know what bootcamps are? Is it different at each company?

Itelina: I think people are generally interested. At almost every interview, people ask me to describe what the program was like and... [and to ask] how they teach the programs. People are definitely interested in that kind of background. They are interested in how well it prepares you for the kinds of problems that you have to solve in a real job.

CLOSING REMARKS - WOULD YOU RECOMMEND DATA SCIENCE BOOTCAMPS?

Liz: The last question I ask everyone who I talk to about bootcamps is this: Was it worth the money? Would you recommend it? Do you recommend Metis in particular?

Emily: I would say yes, absolutely. I think it was worth it. It was hard. It was a challenge. It was really intense, but I don't think that I would have gotten all of this knowledge, especially in this amount of time. I feel like I'm continuing to reinforce that knowledge and add to it with projects that I am continuing to work on. I really don't feel like I could have just downloaded all of that knowledge into myself in any other way.

Itelina: I absolutely agree with everything Emily said. I would do it again in a heartbeat. It was definitely worth the money.

I think the kind of learning environment, and having the classmates you have, that you can learn from, and the instructors...I definitely could not have gotten it just by learning on [my] own. Again, the Metis career hiring network is also really great. I would definitely recommend it to anyone. I wish the program was longer, so that I could have stayed there and learned more.

Liz: Anything we totally missed that you want to make sure people who are watching know about Metis, or about doing a bootcamp, or data science in general, as a career? It's a big question.

Emily: I think there is a lot of talk about data science -- and a bit of hype around it because it is, as I said, one of these fields where there are more open positions than there are people to fill them.

To do one of these courses and to succeed -- or even to do a master's -- you really have to love working with data and love a challenge. [You have] to love problem solving and love getting in there and working hard. I don't think it's something you can do just because, "Oh, I'll have a guaranteed position and a decent salary," or something like that. You have to love this kind of work.

Itelina: I would say for anybody out there who's looking to get their foot in the door for a career in data science...or looking for a change...I definitely encourage everyone to check out bootcamps. I benefited greatly from the program. I think it's a great thing for somebody who is trying to start a new career. Definitely check out programs like Metis.


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