Here is a somewhat blunt set of general thoughts and recommendations
on masters programs in statistics. I don't intend for them to be
polemic, though some of them may sound like that.
I am going to assume that you are interested in a terminal masters
degree to later go into industry and are not interested in
potentially pursuing a doctorate. Please do not take this reply as
authoritative, though.
Below are several points of advice from my own experiences. I've
ordered them very roughly from what I think is most important to
least. As you choose a program, you might weigh each of them against
one another taking some of the points below into account.
Try to make the best choice for you personally. There are very
many factors involved in such a decision: geography, personal
relationships, job and networking opportunities, coursework,
costs of education and living, etc. The most important thing is
to weigh each of these yourself and try to use your own best
judgment. You are the one that ultimately lives with the
consequences of your choice, both positive and negative, and
you are the only one in a position to appraise your whole
situation. Act accordingly.
Learn to collaborate and manage your time. You may not believe
me, but an employer will very likely care more about your
personality, ability to collaborate with others and ability to
work efficiently than they will care about your raw technical
skills. Effective communication is crucial in statistics,
especially when communicating with nonstatisticians. Knowing how
to manage a complex project and make steady progress is very
important. Take advantage of structured statistical-consulting opportunities, if they exist, at your chosen institution.
Learn a cognate area. The greatest weakness I see in many
masters and PhD graduates in statistics, both in industry and
in academia, is that they often have very little subject-matter
knowledge. The upshot is that sometimes "standard" statistical
analyses get used due to a lack of understanding of the underlying
mechanisms of the problem they are trying to analyze. Developing
some expertise in a cognate area can, therefore, be very
enriching both statistically and professionally. But, the most
important aspect of this is the learning itself: Realizing that
incorporating subject matter knowledge can be vital to
correctly analyzing a problem. Being competent in the vocabulary
and basic knowledge can also aid greatly in communication and will
improve the perception that your nonstatistician colleagues have
of you.
Learn to work with (big) data. Data sets in virtually every
field that uses statistics have been growing tremendously in size
over the last 20 years. In an industrial setting, you will likely
spend more time manipulating data than you will analyzing
them. Learning good data-management procedures, sanity checking,
etc. is crucial to valid analysis. The more efficient you become
at it, the more time you'll spend doing the "fun" stuff. This
is something that is very heavily underemphasized and
underappreciated in academic programs. Luckily, there are now
some bigger data sets available to the academic community that
one can play with. If you can't do this within the program
itself, spend some time doing so outside of it.
Learn linear regression and the associated applied linear algebra
very, very well. It is surprising how many masters and PhD
graduates obtain their degrees (from "top" programs!), but
can't answer basic questions on linear regression or how it
works. Having this material down cold will serve you incredibly
well. It is important in its own right and is the gateway to
many, many more advanced statistical and machine-learning
techniques.
If possible, do a masters report or thesis. The masters
programs associated with some of the top U.S. statistics departments
(usually gauged more on their doctorate programs) seem to have
moved away from incorporating a report or a thesis. The fact of
the matter is that a purely course-based program usually deprives
the student of developing any real depth of knowledge in a
particular area. The area itself is not so important, in my view,
but the experience is. The persistence, time-management,
collaboration with faculty, etc. required to produce a masters
report or thesis can pay off greatly when transitioning to
industry. Even if a program doesn't advertise one, if you're
otherwise interested in it, send an email to the admissions chair
and ask about the possibility of a customized program that allows
for it.
Take the most challenging coursework you can manage. While the
most important thing is to understand the core material very,
very well, you should also use your time and money wisely by
challenging yourself as much as possible. The particular topic
matter you choose to learn may appear to be fairly "useless",
but getting some contact with the literature and challenging
yourself to learn something new and difficult will make it easier
when you have to do so later in industry. For example, learning
some of the theory behind classical statistics turns out to be
fairly useless in and of itself for the daily work of many
industrial statisticians, but the concepts conveyed are
extremely useful and provide continual guidance. It also will
make all the other statistical methods you come into contact with
seem less mysterious.
A program's reputation only matters for your first job. Way too
much emphasis is put on a school's or program's reputation.
Unfortunately, this is a time- and energy-saving heuristic for
human-resource managers. Be aware that programs are judged much
more by their research and doctoral programs than their masters
ones. In many such top departments, the M.S. students often end up
feeling a bit like second-class citizens since most of the
resources are expended on the doctoral programs.
One of the brightest young statistical
collaborators I've worked with has a doctorate from a small
foreign university you've probably never heard of. People can get
a wonderful education (sometimes a much better one, especially at
the undergraduate and masters level!) at "no-name"
institutions than at "top" programs. They're almost guaranteed
to get more interaction with core faculty at the former.
The name of the school at the top of your resume is likely to
have a role in getting you in the door for your first job and
people will care more about where your most advanced degree came
from than where any others did. After that first job, people will care substantially more about what
experience you bring to the table. Finding a school where lots of
interesting job opportunities come to you through career fairs,
circulated emails, etc., can have a big payoff and this happens
more at top programs.
A personal remark: I personally have a preference for somewhat
more theoretical programs that still allow some contact with data
and a smattering of applied courses. The fact of the matter is that
you're simply not going to become a good applied statistician by
obtaining a masters degree. That comes only with (much more) time
and experience in struggling with challenging problems and analyses
on a daily basis.
