My UPenn MCIT Experience | Tina Huang

0:00My English has clearly degenerated as my computer science skills increase. That's a good sign. The problem that I want to is called MCI-T, at University of Pennsylvania. So this is a program for people who don't have a computer science, or engineering or a computer science-see technical degree, and want to get a master's in it and then go do all these computer science-see jobs in the future. So it's a two-year degree. With the first year being very core, so that you learn all of core computer science and the mathematical stuff, the undergrad will learn the first year, so it kind of catches you up to speed. And by the end, you should know your basic computer science information. And then in the second year, this is what you take electives, like data science after learning processing clearly I went into data science after it.

1:03But yes, so you have a bunch of electives you can do, like systems, engineering, databases, all sorts of things that normal people in computer science master's degree would be doing for their two years. I'll just talk a little bit about my experience throughout that entire journey. So, you know, as I was saying, coming in, I knew a little bit of programming, I knew a little bit of Python, I took a couple classes, my undergrad in Python, I also worked on mathematics, so I knew R, data science stuff. I never really learned all the algorithms, like data structures, and all those things. I didn't know how to do it in that. Also, my math background was actually very like, was very weak, so I never did like linear algebra, what else do math that we do, discrete math. So, coming in, you know, it's pretty nervous. I hope I pass, and I'm able to get a job in them.

2:05I was right to be nervous because my first year was really quite difficult. I don't want to scare anyone, but I really was probably the most challenging year I've ever had academically. So, let's see the three classes that I took in my first semester, were the course course were CIT 591, 592, 593. So, 591 was a introduction to Java class, and that has pretty much just taught you how to do it for loops, I just basic Java programming, and 592 was the class of how you discrete math, probability, and proofs, which I've never done before. I'm told out, and 593, which is a basic computer, I think is a computer systems class, it's very low level stuff. We pretty much went from my bytes and bits all the way up until C level-ish, so you learned about how computers are actually working under the hood.

3:08You also have the program in assembly, which is a very low level, and in the second semester, there was a continuation, which were 594, 595, and 596. In 591, 594, there was a continuation of programming in Java, and in this class, we went about data structures, mostly. I think this is the class of most useful you want to eventually become a software in here, because it's mostly your early code things and whatnot, and for, yeah. It definitely wasn't easy class, because this whole program is very accelerated, so you're coming in not really knowing anything, because you don't really have that background. There's no background that you need to have. If you're like me, you don't have very strong math background, we're putting background. It was just like, yeah. So, yeah, 594, and then 595 was a continuation of the systems class,

4:14so we did some, a little bit of web stuff, kind of get a taste for that, and also, maybe it's, yeah, we did more seeded seed plus plus, yes. And it was more like a sampling of different programming languages, and finally, it was 596, was the algorithms class, and this is kind of a continuation of 592 in default. So, this was more, you don't really do coding this class, except for a little bit of Python, but you learn about like, recursion, learn about different sorting algorithms, calculating like, big O, that's time complexity and things like that. And that essentially brought you up speed more or less, so that you can go take a lot of things with only a year. So, let's talk about internships. Right. So, it was definitely, it was very difficult,

5:18not only because you had to learn all the things that you probably didn't know before, you just kind of like, throw it into that. On top of that, you also had to do internship preparation. So, in doing internship preparation, if one of these software engineers, which most people want it to be, you had to do like lead code prepping, white boarding, things like that, not with so much behavior of stuff, but you have to do a lot of prepping for the interviews, the technical interviews. So, personally, I didn't really do lead code, so I'm not saying that you have to do lead code, because I didn't do it, and I still got an internship, but that was also my personal experience. Most people like you did lead code, and the reason why I didn't do it was because I thought I was so good, and I was able to do everything in the amazing. I was just like very overwhelmed. So, I just ended up maybe doing like one or two lead codes, and try to depend on my personality through the interviews. If I didn't know how to answer the technical questions.

6:20So, anyways, I ended up turning GoPasats doing software. Okay, one is a software engineer, but I ended up doing more like data science-related stuff, because that was the part that was more interesting to me. So, yeah, a lot of people I know they end up doing internships with big tech companies, like Amazon, I don't know about Facebook, I think there was like one that went to Google, but a lot of other people also do smaller startups, pre-ding software engineering, with mid-sized companies. So, I don't know if I have this statistic completely right, but I do believe that almost everybody, I do believe that everybody that wanted to get an internship, did get an internship, there's no guarantee that you get like an amazing internship about like Google, you know, or like Amazon, I think that. But everybody that wanted to internship the software engineering did get one, and this program is really designed for you to be able to get a job in the end.

7:23So, they're very good at internship process. So, of course, you also have to put in a lot of work for it, but that pen branding, as well as just like working hard, that pen branding and entire program really helped you prepare for that. So, the second year of MCIT was very, was like when you were released into a while all the other computer science people, and you can take whatever elected that you want. But bear in mind, even though we did do this sort of like one year ramp up a celebration bootcamp kind of thing, you're really not at the level of people who do like a four-year undergrad degree. So, my advice is they actually still be pretty cautious about well-elected that you're choosing. Like you don't want to go and jump in and do like this super high level C++ programming when you've done it for like maybe two weeks in 595. Of course, if you want to do that, you can also teach yourself, but I did not do that.

8:23And most people I know also they not do that. So, yeah, like the electives, you have choices in like natural language processing in data analytics, data science, data bases, web development, web design. I think there's some other classes, like more systems related as well, like your photography. And you can also do more advanced algorithm classes, more like theoretical algorithm classes. So, yeah, you take your electives and the thing with because it's a two-year degree and master you are all like busy preparing for internships and stuff, then this year rolls around and now you're really busy preparing for job interviews. Assuming that you were on a lucky people who landed in internship and then became full-time and then don't actually have to do worry about that anymore. But for myself and most other people, we then had to prepare for job interviews. For software engineering, people usually tend to do better in their full-time job interview

9:32than their internships naturally because they have more experience at this point. And you do get a lot of people landing those big names like people going to Google, Facebook, this like thing in general, as well as other startups as well. And if you're not doing software engineering, there's people who go to data science rules, whether in tech or not in tech, I am in a data science rule right now in tech. And you have people who are also doing quant trading, whether they quantitative research, but there's even people who pursue PhDs. Either in computer science or non computer science as well, as well as there's also people doing a lot. It's a pretty diverse group of people wanting to switch their careers, whether that be completely in tech and doing software engineering or just wanting to do a job that's more tech in a core nature. I would say my experience was positive.

10:35And I don't think I would have been able to demand the job that I have right now. If I didn't go through the MCRIT program, then I think a lot of other people would also agree with us statement. It's a master's degree that really elevates your current resume. You want to make that career job. And just that branding as well as the preparation that you get, just by being a whole pool of people, pretty much doing the same thing as you, as well as being exposed to recruiters, does make it pretty worthwhile. Yes. So the cost definitely isn't cheap. I believe it's around 50k each year. I would say it is worth it though, because the starting salaries of most people coming out of here is six figures. So you make that back within one year of working, where you know, one or two years depending on how good you are with money and how much you're making. But I do think overall it is worth it. Friends of admissions. I think the year that I was admitted,

11:36the admission rate was maybe 11% don't quote me on that. They do publish how much it is, how many people are admitted each year. And you do have to do the GRE, you have to go through this application process. So the average GRE verbal was in the high 150s. And for the math, it was 167. So you do have to score pretty highly on the quantitative reasoning portion, which makes sense because this is a quantitative program. I would say, if there's a lot of people who get a GRE scores, who have great GPAs, pretty good recommendations. So if you really want to stand down that pool, the most important consideration would be your personal statement. So you can imagine these reviewers have like tons and tons of applications that pretty much look really similar, unless you're like really interesting background, you're probably pretty similar to the next person in the application pile.

12:39And you're reading all those personal statements, it gets really boring, really fast. So if you want to stand out, make your personal statement good, make it interesting, so that the reviewers can actually remember you after they read your application. And of course, make sure that all your scores are there. I can't think of anything else to talk about. But feel free to reach out to me. If you want to get a little bit more information on something that I didn't mention or I was very confusing about something, and I can help explain that a little bit better. So thank you all for watching. I hope this was useful.