In this episode we talk with current MS and PhD students about the transition from undergrad or industry to CDS.
April 2, 2020 | 13:25
HOST: Welcome to the Center for Data Science Admissions podcast. I’m your host, Tim Baker. In today’s episode, we’ll hear from current MS and PhD students about their transition into life at the Center for Data Science.
ERIC: My name is Eric. I was… I’m currently a student part time in the Center for Data Science. I was accepted just this current semester of fall 2019. I expect to graduate in about three years. As a student, full time, you definitely have to do a lot of work outside of what might be considered standard work hours and like, in my working life, like I just have to be in the office at a certain time. Certainly can’t be late to things anymore. But it’s pretty similar, I would say overall, just that during the day now, like things have flipped a bit that now I work primarily on work and then do classes and homework after. I definitely pursue a work life balance now. I think that I worked maybe a bit too hard in undergrad actually. So, I definitely make sure to get like lunch or dinner with people, sometimes during the week and then most times during the weekend as well.
AMBER: I’m Amber Teng. I’m a first year student at CDS. My current track is NLP and before coming to CDS, I used to work in risk and finance. But I majored in Economics and Archaeology. I think it is definitely a big shift, especially if you haven’t done problem sets before. And as I mentioned earlier, I come from a non traditional background in the sense that I wasn’t like STEM heavy, we didn’t do a lot of like math, math proofs undergrad, I was more applied and I also did a lot of paper as an essay writing undergrad, but we didn’t do as many problem sets, right. So it is like, it’s definitely like there’s a learning curve to it. But I think what I really like about CDS is that they provide a lot of support. So aside from like, just being able to, like work with my peers and like my classmates on thinking through questions like understanding, kind of like how to go about like solving them, I think was also been really helpful are the office hours at my professor, our professors have for each class as well as like the TAs. It’s super helpful to be able to just get a feel of, if you’re thinking in the right direction and like going in the right way about these things. Yeah, the other thing that I would say is that, for me, like time management has been key to like getting my problems also done. I feel like coming from industry, you know, you kind of have like, either like a nine to five or you work very specific hours for the most part. And being at CDS where I take like three classes per semester and the rest of the time is yours, it’s kind of like structuring that time is really key.
GUY: My name is Guy Davidson. I am a first year PhD student here at the Center for Data Science and I started this past September. The focus is almost entirely different. In undergraduate, you are there to acquire knowledge by taking courses, for the most part. I mean, people have many reasons to go to undergraduate but that’s the method of acquiring knowledge. In the PhD program, I find that the emphasis is on acquiring knowledge by doing and specifically by doing your research. There’s gonna be some small new shift the world that you’re going to learn a lot about, and hopefully to be able to generate new knowledge then. This tension sort of continues to exist because you are going to be taking courses early in your PhD, but where your courses have, you know, weekly deadlines and homeworks and they’re very clear about what you need to do and when, your research work is going to behave basically entirely the opposite. They’re not going to be any clear deadlines. Your advisor probably won’t say anything if you don’t get as much done before your meeting as they would have liked. But if you imagine the urgent important, not urgent not important matrix, it ends up being that a lot of your research work until you have a paper deadline is important but not urgent, whereas a lot of your other work is urgent but not important, and so balancing out these two things is very tricky.
IRINA: So my name is Irina, and I’m a CDS PhD student currently my second year. From Masters, I’ve seen a difference for from people who come from the Masters and people who come from an undergrad, because there is so… from undergrad school to graduate school, there is a big gap. And that’s what I noticed when I went from my undergrad to my Masters. So then, Masters to PhD. I was already used to workload in how to work in graduate school in the subjects. So, course-wise related. And then so then what I focused more was on research – was on developing my research in the first year and focusing more on that. But because I already, was already comfortable with the way graduate school works, because of my Masters but when I transitioned from undergrad to Masters, what I saw is that there are more things that you have to figure out yourself, you’re expected to search your way to solve the problem. And if you have to read another paper, you have to… you do you. And sometimes problems are hard.
JASON: I’m Jason Pang. I am a first year PhD student at the Center for Data Science. Prior to that, I was actually also in the Master’s program at the Center for Data Science also for two years and then I just started my PhD program. Towards the… let’s say the second year of a Master’s I was so like spending more time on research. So, I had expected that to be not that much of a transition or be like, “Oh, I just do more research.” I guess surprisingly, at least psychologically, I think there is a bigger difference between when you’re a Master versus when you’re a PhD student where like, now you’re sort of your life mission is now to do good research. So, your priorities shift a lot. For example, even though I’ve taken I’ve done the program here, I’m still taking like one class. But that’s really more of a side thing that I’m doing. Really my, like, how I’m planning let’s say my day or my week I say, “OK, what things do I need to do for my research projects? And then what other things can I fit in there,” and so on.
CHRIS: Hi, everyone. This is Chris. I’m a second year Masters student at the Center for Data Science and working for ten years. It was a transition because once you go into that sphere of working like sphere of industry for a year, maybe some industry people would be angry at me, but there is not as much learning and as much activity as a student life compared to when your industry because when you’re a student, things are so unclear and unfussy compared to like a working environment, which I felt really like, it just hit me on the face after I became a student like, because there are a lot more things to do, and you have a lot more degrees of freedom in a very mathematical way. But yeah, things that helped me were to prepare to lay a good foundation before I came here. So, I realized that I’m going to pursue a Masters and before that I took a couple of online courses. I did not put a lot of effort because my job was stressful and I was fully concentrating on the job, which was what most people do. But here and there on the weekends, I managed to put in the effort necessary to get back up to speed with prop stats. And that actually really helped me once I was here because it helped smoothen the transition. Picking up on background knowledge have been prepared for what you’re about to face will definitely ease the transition. To be honest, the thing that actually makes the transition easy is after you come here, you meet a lot of people who help you out. And a lot of great admin faculty and a lot of great professors who help you out with courses and teaching assistants and office hours. And so once you’re here, there are a lot of things that you can use to make the transition easy.
KATRINA: I’m Katrina, I’m a second year PhD student at the Center for Data Science and also an alumnus from the Master’s program. Started the PhD in 2018 and graduated from the Masters in 2017. Yeah, industry and academia are a bit different. Like when I was working in industry, I had a still like a research engineer position, and I still had it in the back of my mind that eventually what I wanted to do was entry PhD program and start working towards my Doctoral degree. But, I mean, I think the experience I got during, like working in the industry was valuable to give me a perspective on how like data science is applied in the wild. And as I came back to the PhD program, I think one thing I’ve tried to keep from like working in industry is like to keep, like schedule from like nine to five, let’s say, be in the office early in the morning.
TIM BAKER: So you’ve been able to, like strike a work life balance, basically by having that sort of a schedule?
KATRINA: I’m trying to.
TIM BAKER: Well, yeah, I guess it’s difficult, right? It’s a lot of work.
KATRINA: Yeah. PhD is a commitment.
CHRIS: My name is Chris Ick. I am a first year PhD student but I also did my first years of the Master’s program before switching to a Master’s program into the PhD program. While I was in the MS program, I was taking it part-time. So, I was only taking two classes a semester while I was working as a research assistant. So, because of that I went from that to taking three classes per semester but one of those classes is a research rotation. So, it’s pretty similar. The biggest difference for me is obviously having an office here, it’s nice having my own space I can work in. Academically, the coursework is very, very similar for the first few years or so. So the coursework hasn’t changed that much. But I do find that the support I get from, like, staff and events and stuff like that, it’s definitely more oriented towards research and fellowships and scholarships, and you know, typically more academically minded environment. So, one of the reasons why I really liked the MS program, but I decided it wasn’t a good fit for me was there’s tons and tons, almost too many resources for job placement and hiring and stuff like that and I thought it was really interesting, but once I determined I want to do research, I thought I could steer my sort of energy towards getting research roles and fellowships and stuff like that so swapping over sort of changed the mindset of the people I work with this sort of resources that are targeted towards workforce oriented, more research oriented.
YOSH: I’m Yosh. I’m a second year Master student, I started in 2018 and I am doing the Masters in Data Science. I was still new to the industry, so I was only working for two years. So I feel like the transition wasn’t rough. I liked it, because I didn’t really like the work culture at the company that I was at. So having the flexibility and having the structure of academia is something that I really liked. That transition was smooth. In terms of… so at my job, I also dealt with large amounts of data, but it was more of an analytics role so more front end. In terms of the transition from that to data science, that was something that took a little getting used to because data science tends to be more exploratory than analytics.
WILLIAM: My name is William Falcon, a PhD student at the Center for Data Science, And I’m a second year. In terms of going from school back to school, because I kind of maintained that relationship with school, I was still in the mindset of, “OK, classes, how does that work?” I’m gonna research, I had research experience, and I already knew what to do and I hit the ground running in terms of what to expect. If it may, if it was my first research project, it might be very hard. And but I assume that admissions kind of filters through that, because most people have publications before they come in. You should really know that you want to do a PhD because you don’t want to find that out a year or two into it.
TIM BAKER: Yeah, definitely.
WILLIAM: And then on the professional side, I mean, getting adjusted from like, industry lifestyle to student lifestyle hasn’t been super difficult because I had the foresight when I was in industry to like, know that this is something I was gonna do. And so you can prepare yourself financially for that.
HOST: Thank you for listening to the Center for Data Science Admissions podcast. Today’s episode was hosted by Tim Baker, mixed by Katerina Mora and music was provided by Cryptic. You can find his music at crypticone.bandcamp.com.