Things to consider about masters programs in statistics It is admission season for graduate schools. I (and many students like me) am now trying to decide which statistics program to pick.


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*What are some things those of you who work with statistics suggest we consider about masters programs in statistics? 

*Are there common pitfalls or mistakes students make (perhaps with regard to school reputation)? 

*For employment, should we look to focus on applied statistics or a mix of applied and theoretical statistics?


Edit: Here is some additional information about my personal situation: All of the programs I am now considering are in the United States. Some focus on the more applied side and give masters degrees in "applied statistics" while others have more theoretical coursework and grant degrees in "statistics". I'm personally not that intent on working in one industry over another. I have some programming background and know the tech industry a little better than, say, the genomics or bioinformatics industry. However, I'm primarily looking for a career with interesting problems.
Edit: Tried to make the question more generally applicable.
 A: 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.
A: I would advise to either get in the best school possible with a brand name (like MIT), or the best overall deal (e.g. a decent public school with in-state tuition). I would not waste money on second rate private schools.
The brand name schools payoff. The price difference between a school like MIT and second tier schools like GWU is not big enough to justify the difference in the brand power.
On the other hand, some public schools, e.g. William and Mary, while being dirt cheap offer decent education. Some of them even have comparable brand power, e.g. Berkeley vs. Stanford. Thus due to the significant cost difference, they're an alternative to best private schools.
A: Take a look at Pharmacoepidemiology. In particular as it relates to Drug safety. This is a very new area of research with a lots of very interested questions.
