M.A. Quantitative Methods in the Social Sciences at Columbia University: Things you should know

I am, as of right now (Fall 2016), in my third and final semester of a master’s degree in Quantitative Methods in the Social Sciences (QMSS) at Columbia University in New York City. There isn’t a whole lot of information about this program on the Internet, so to those who are giving this program some consideration, particularly students from outside the United States, here is some helpful information.

How long is QMSS?

One year, with the option to extend one more semester if you wish. Very rarely, some people will extend by two semesters – these are usually people on externally-funded scholarships that have a set timeframe.

Is it expensive?

Yep. This academic year, a full-time semester costs $28,780 for up to 20 units. (One full-time class is either 3 or 4 units, and the number of units isn’t necessarily a function of workload. QMSS classes are 4 units.) It’s expensive, though cheaper than other quantitative courses such as Mathematics of Finance or Statistics.

If you choose to extend your stay, the additional semester costs $10,944, with the restriction that only up to two non-QMSS classes can be taken that semester.

Unfortunately, hardly any from within Columbia. There might be some merit-based scholarships that’ll give you like 5%. International students are best off seeking external funding. In general, master’s programs that aren’t in public policy tend to be very stingy with scholarships; they need your money to subsidize the Ph.Ds.

So I can take classes outside the program?
Hell yeah. The only required classes are “Theory and Methodology”, which is a survey class of approaches to quantitative social science that’s generally geared towards people who’ve never seen social science research before and is pretty boring and old hat otherwise; the “Research Seminar”, a two-semester sequence where you come in late at night to listen to an academic or practitioner give a talk (which may or may not be interesting), partially meant to expose you as a student to some of the latest research and practice being done, and your master’s thesis. Everyone has to do a thesis. There’s no getting around it.

By the way, Statistics students don’t have a thesis. If theses are your thing (e.g. if you really want to go on to a Ph.D), go for QMSS.

Aside from those classes, you can take any classes you want outside the program as long as a certain number of them are quantitative. If, for example, you find QMSS’s course offerings too easy, or you really have a particular substantive topic you’re looking to learn more about, you can take classes in the Stats, CS, Engineering, Econ, PoliSci, Sociology, and whatever other departments exist at Columbia that’ll let you in. And most classes will let you in provided there’s enough room. It’s only the super-duper high demand ones such as Data Science that won’t let you in… except if you’re in QMSS’s Data Science track.


QMSS students are required to pick one of three tracks. The “Economics” track is exactly what it says on the tin; it used to function as basically an MA in Econ when Columbia didn’t have their MA in Econ yet (it was just launched last year). The “Data Science” track lets you into classes offered by the Institute for Data Science and Engineering such as Algorithms for Data Science, Machine Learning for Data Science, etc. And the “Traditional” track is where everybody else who isn’t specializing in the former two tracks goes.

(There’s also a fourth track, “Experiments”, but to the best of my knowledge practically no one has ever taken that track and it might not even functionally exist anymore.)

I suck at math – what do I do?

QMSS was specifically designed for people with a limited background in quantitative methods. I didn’t even know what a regression was before I entered the program – my own background was in development studies and history. Now I wouldn’t call myself an expert on anything, but I’m more comfortable with this stuff and know where to look to go deeper.

You do need to kinda not suck at math, but if you can nail the Graduate Record Examinations, you’re good. (Not the math-specific GRE, just the general quantitative portion.) I would even say that you can graduate from the program without knowing calculus and linear algebra if you just take all the applied data analysis classes, though how much you actually understand would be a valid question.

How big is the program, and what are the people in it like?

Each cohort ranges from about 60-75 students, including part-time students. About half or so are from East Asia and the other half are from everywhere else, though mostly North America. Backgrounds range from idiots like myself to people who are already data scientists of some sort.

For Chinese people in particular, especially those who are coming straight from undergrad in China, if you’re also looking for a program with a good mix of Mandarin and English speakers, QMSS is for you. By comparison, Columbia’s Stats program is around 90% Chinese.

QMSS has an associated student-led organization, Society for Quantitative Approaches to Social Research (QASR), that organizes social events, alumni networking sessions, outreach efforts, and the like.

Thanksgiving dinner
QMSS students at Google’s offices

So what is in QMSS?

QMSS classes focus on applied quantitative social science. There’s a three-course sequence taught by sociologist and program director Gregory Eirich that starts with applied regression analysis through linear models, generalized linear models, causal inference methods, text mining, methods for longitudinal data, and time series processes. Those classes are light on the technical details and focus on understanding the structure and assumptions of various models and techniques at a high level and putting them to use with data via the open-source R programming language. This is in contrast with an econometrics class, which would cover the same material but with much more theoretical grounding and mathematical proof and much less actual data analysis.

There’s a two-course sequence taught by geographical sociologist Jeremy Porter on geographic information systems & spatial analysis that essentially teaches you how to work with data with some sort of location information attached. It uses the open-source software Quantum GIS and the R programming language.

Other electives include Social Network Analysis, also taught by Eirich, and Data Visualization, which is either a really good or a really crappy class depending on who’s teaching it. If you’re familiar with the Harry Potter series, it’s like Defense of the Dark Arts – the professor changes every year for some reason or another, and the syllabus completely changes every year in step.

There are also two classes taught by political scientist Benjamin Goodrich. In the fall, Data Mining for Social Science is an introduction to techniques such as tree-based models, neural networks, principal components analysis, etc., that are commonly used when the goal is to predict new data. It’s essentially an intro to machine learning class that’s decent preparation for more advanced classes, and it doubles as an actual introduction to R in the first couple of weeks. The other classes use R, but this class actually teaches it from the ground up at an accelerated pace.

In the spring, Bayesian Statistics for the Social Sciences is an introduction to Bayesian modelling, which is a different way of approaching a statistical problems that puts heavy emphasis on probability distributions. It’s the most math-heavy class in the program and also doubles as a shameless plug for the statistical modelling language Stan, which is being actively developed at Columbia by a team including Goodrich and others, which allows the user to specify flexible models for particular situations instead of relying on canned packages. Columbia is also the home of Andrew Gelman, one of the foremost Bayesian statisticians out there, and his influence looms large.

If none of those sounded exciting to you, remember that you don’t even have to take any of those classes if you don’t want to. Already a crackerjack who wants to go full-on into machine learning? Head over to Stats/CS/Engineering. Want to go into finance? Columbia Business School classes can be hard to get into but it’s doable. Not satisfied with the fairly high-level approach of Eirich’s classes and want to go really deep into the weeds? Either the Econ or PoliSci departments are for you.

There’s also one last thing. If you wish, QMSS can match you with professors from across the university who are looking for research assistants, which is a great opportunity to get your feet wet with social science research outside of a classroom setting.

I don’t want a Ph.D – I just want a better job or a change of career.

QMSS is perfect for that as well – while initially designed as a Ph.D preparation program, most people opt to go into industry for at least a while after graduation, and the flexible nature of the program allows for diverse interests. Data science and analytics are the flavor of the year, but policy research, tech, consulting and finance aren’t far behind. And for those last two industries in particular, there’s hardly a better place than New York City.

QMSS also qualifies as a Science, Technology, Engineering and Mathematics (STEM) program, which for international students who want to work in the United States means that they would qualify for special preferences given to STEM majors.

How easy would it be for me to find a job?

Jobs are everywhere for people skilled in quantitative methods. Many students in the program aren’t even particularly interested in the “social science” part. The barriers to entry are much higher for international students, however. Columbia University’s career office is a great resource, with lots of events such as job fairs, industry talks, and interview prep sessions. This being New York, though, their offerings are heavily skewed towards consulting, finance and tech. The good news is that larger firms are generally also more open to international hires.

Part of the reason some people stay for a third semester is to give themselves additional breathing room in the job hunt – instead of graduating in May and having to have a full-time job within three months or risk losing their visa status, they can graduate in December and hopefully score a summer internship beforehand.

Why shouldn’t I just major in Statistics?

Sure, you could. In fact, if your goal is to get a Ph.D in Statistics, or to get a top-level engineering job in some data science firm, then you would need much more quantitative stuff than the average QMSS student learns, although you could pursue those same classes within QMSS if you wanted to due to its flexibility.

Regarding Columbia specifically, the primary advantage to QMSS over the Statistics program are that it’s slightly cheaper and has 60-75 students as opposed to Stat’s 200+, meaning that it’s easier to raise concerns and get attention from the program administration.

If your goal is a Ph.D in a social science, QMSS is excellent preparation. Many social science Ph.Ds are actually somewhat behind when it comes to quantitative methods despite American social science’s heavy focus on it.

Why would QMSS not be for me?

The cost is a huge factor, honestly. Tuition aside, cost of living in New York City can be ridiculous. Food, transportation, and that kind of stuff isn’t very expensive, but rent…

Barring that, it does say “quantitative” in the title, which represents a particular approach to social science that isn’t by any means all-encompassing. If you’re interested in being the kind of researcher who can do detailed case studies, thick descriptions, in-depth interviews, etc., then the program probably isn’t for you. You might even encounter stuck-up people who will scoff at you for your ‘inferior’ methods, though it’s not as prevalent as it seems. Quantitative methods are generally useful for every social scientist to familiarize themselves with, but a program devoted to them isn’t for everybody.

If your bachelor’s degree was a fairly quant-heavy social science program, QMSS will be redundant.

Finally, if you intend to use the program’s flexibility to take courses in things like data science, it isn’t going to be immediately obvious to employers that you’ve taken those courses if they see QMSS on your resume. Your chosen track isn’t reflected on your transcript or diploma. So if you really do want to go into something more specific like stats/data science, you may want to consider going for the degree that actually says stats/data science. (Or you could self-study, build a portfolio, etc.)

On a lighter note, “Quantitative Methods for the Social Sciences” is also way too long of a course name. Imagine telling someone that in an elevator.




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