MS in US for data science?
If you are not in US, and want to get into data science, one question you might have to think about is “Should I pursue MS in US?” If you are an engineer in early 20s in some part of the developing world, you might have thought about this question at some point. In this blog we will talk about this question from a few sides. Being an Indian, I am going to talk from an Indian point of view, but the points are generic.
A short background on me: I was an analog circuit design engineer, and then I moved to US for MS in 2014 at Columbia University. Here I realized how much impact data science was going to make in our lives, and I have gradually moved into this field. So basically, I had to start from absolute scratch. I’ll cover about getting started in another blog. So let’s get started with today’s topic:
1. Location matters more than rankings.
Honestly, I don’t totally believe much in the rankings. US News might have one University over another one year, and the next year another company might have other set of rankings. I do believe in rankings to an extent that the University is somewhere in the ball park of “top-20” or “top-n”. I think once you land into one of these Universities, what you make of your experience, is completely up to you (and luck).
If there is one thing which I feel many people coming from India don’t research much is location of the University.
Points to consider:
a) How cold does it get in winters? There is a huge difference between -10C and -30C. Both are “cold” in Indian terms but it makes complete difference to how much outdoors you can be, your lifestyle etc.
b) Is there some form of vegetarian / Indian food available? How far are Indian grocery stores from where the university is? Can I find some form of vegetarian food?
c) Is driving necessary? If you like driving that’s great. Make sure to bring your smart card driving license. If you do not want to drive and prefer taking public transport, that leaves a few cities like Boston, Washington, Chicago and New York. The public transport on the west coast and mid-west is bad and you need to mostly drive.
d) Are there data science networking opportunities? Having an ability to network and go to meetups in your college town is a good way to find internships, meet people from industry and network. Is there Facebook office in this location? Is there Microsoft location nearby? Does Amazon hold hackathons in this city? This point I feel is the most overlooked point by people researching universities in US.
2. The Professors are (usually) great — talk to one before applying.
Having an Asian experience where professors were usually just “there” and not quite available to talk or discuss, US experience is a breath of fresh air. Here are the reasons why:
1. Professors respect the students. They listen carefully to your questions and try to give a detailed answer calling forth all their experience.
2. They give examples of their peers and others who are pioneers in the field. So students get the ‘inside’ of how the inventors think and work.
3. They give complete autonomy on students and the projects and do not believe in giving step-by-step procedures. Same goes for classes. There are no step-by-step tutorials, and usually no lecture notes. You have to ‘figure’ out on your own — this prepares really well for real world projects.
4. Some of the pioneers of the field frequent the campuses to give talks or to do projects or guest lectures. Seeing them in person is a great way to get inspired and build stuff.
5. They focus on basics. In my machine learning class, I learnt about the statistical and mathematical basis to all ML algorithms — something not quite available in MOOCs as such.
Keeping all this in mind, before applying, it is a nice idea to actually email a few professors and talk to them and see if they have research opportunities with them. Read their papers, and see if you like a topic or two.
For myself, Professor Kinget showed interest in me doing research work with him. This helped me during the application and also in their short interview.
3. The culture is built around “enabling” people — and requires a lot of hard work.
I feel that the culture is one of the major “pro” points towards pursuing MS in US. Students rock. Ok not everyone. But still. Students are here for a mission and purpose. The locals in particular have an incredible work ethic. They are doing engineering because they are passionate. They like to build things. On weekends, they are doing “fun projects” — work is not exactly a chore for them as it’s coming from a place of passion and not karna pad raha hai (have to do, what to do)
I feel that the culture is about “enabling” people, no one is frowning if you ask questions. People’s voices matter. Yes, the opinions need to be backed by data but if you have a good enough point, professors, and people will value your opinion. This has truly been eye-opening for me, something I haven’t perhaps fully explored yet.
As a data scientist with a mission, you’ll find yourself surrounded by some of the most tough problems in this field today. Not every assignment or course is fun, not every interaction memorable. But, overall, you are going to come away having developed several folds of what you were before you started.
All this requires insane, mind-bending amount of hard work. Being from Asian Universities makes us used to things being handed out to us, but here it is all about “go figure it out”. Talking to other people in the field is actually encouraged. If you are motivated, want to go the next level, don’t mind putting in 100 hours a week — the rewards are also awesome.
So far so good. Now for the Counter Points. These points you have mostly already heard or thought about, so going over them briefly.
4. Cost is High.
The cost can run close to the ballpark of $100,000 US dollars. This is a large chunk of money. I feel that if you and your family have to sacrifice enormously to raise this money, maybe all the advantages I listed above are not really worth it. I will talk about a few alternatives in another blog post.
Cost of Living is really high. Esp. with the cities having all the ticks in Point 1.
Having some work experience before coming will allow you to not only save some money before coming, but will also allow you to “craft” your curriculum with things you really want to learn.
5. Visa situation is uncertain.
Yes, you have heard it. F1 visa students have 36 month OPT after an engineering MS. Yes. But in my time, they were about to cancel this and this led to huge stress among everyone. So these kind of things happen. My friends from chemical and mechanical engineering had huge points of stress when they learnt they cannot pursue Curricular Practical Training as it’s only available for computer and electrical engineering students. Make sure you check with University regarding this before applying.
Other things to keep in mind:
a) In current regulations, spouses are not allowed to work under dependent visa.
b) H1B visa is a lottery where 300,000 people apply for 85,000 positions. MS students get priority in the first 20,000 seats though.
c) You need to have done 2 semester of studies to pursue internship under CP. Keep this in mind if you are planning to come in Spring semester, you’ll likely miss out on paid internships in the summer.
d) You need to take at least 12 credits to maintain F1 status. This translates to about 4 courses per semester. I have seen locals take 2–3 courses also(most of them are doing part time work anyways). The workload of 4 courses for international students can be large.
e) You are far away from family. Is going back home for Diwali important to you? You might have to miss a few weddings of your cousins. These are just some of the things to think about.
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Overall, the decision is fully yours. But I wanted to break a few myths and also some things which I wished I knew before coming. Overall I feel it is very worth it esp. in upcoming areas of data science and computer science. Some of the public universities might have lower tuition, and might offer aid. The more you put, the more rewards you’ll get. On that note, I will end this blog.
If you have any questions in particular, drop them in comments or reach out to me on LinkedIn. As always, thanks for your support, and I wish you the very best.
