oldprogrammer

UT-Arlington officially only requires that you have completed the math requirements prior to admission. In an email conversation, the graduate advisor felt that the odds of admission without having taken at least a couple CS courses (and done well in them) were very slim. They list the prerequisites in a PDF titled something like "MS Guide". The graduate advisor at UT-Dallas didn't give me a list of courses I would need prior to applying (though they, too, require the math to be taken already). But I would have to take 3 or 4 leveling courses at some point. They did tell me that professional experience with, say, assembler, would take care of the assembler requirement, for example.

And Southern Methodist University (Dallas, TX), tied with UH at #110!

Since their reputation/ranking is roughly equivalent, it's going to be a personal decision. If you intend to pursue a PhD following the MS, then your decision should be based on your research interests. I'm sure both programs have websites with details on faculty research. Pick the program that has more professors you could see yourself doing research with.

USC is undoubtedly a stronger program than UT Dallas, but also dramatically more expensive. As for which is better, it might depend on what your post-MS plans are. Do you intend to try for a PhD? An Industry position? In my mind, USC's MS program has always seemed to be a money-making machine.

Perhaps I should have stated it more as "multivariable calculus is typically required" as some schools will offer a full course in calculus in two semesters as an alternative to taking three semesters. Basically, if you covered the entire book. Definitely should have covered general vector calculus and triple integrals in different coordinate systems. Just look at the admissions requirements for each program you're interested in as the exact details will vary. Some programs will defer your admission entirely if you are missing any of their prerequisite math courses, others will just require you to take the missing courses as soon as possible after you start the program. Some programs will require Linear Algebra or Probability (requiring Multivariable Calculus as a prereq) or both. I've also heard it stated that one's mathematical preparation should be equivalent to that of an undergraduate degree in Computer Science. Put that way, maybe check the math requirements for a BSCS at the schools you are interested in applying to.

I douby you will have a placement problem coming from either school. If you have not visited yet, that might help you decide, as there's no substitute for actually visiting a department and getting a taste of the personalities of the department and your potential advisors. In the end, you probably need to decide based on which school offers better advisors for your potential research area. Start reading papers from prospective advisors and try to decide if there are one or two that you are really drawn to. If that still doesn't help, I would personally choose Cal Tech as I would prefer the smaller department (and Pasadena).

A MS in CS without a prior CS background will take longer than a year. There will typically be about 4-5 leveling courses to get you up to speed before you start on the 30-36 hours for the program. So expect a total of about 42-51 hours. And you will also need 3 semesters of Calculus and Linear Algebra to be admitted to a normal program. And it is very atypical for a MS student to be funded.

Unfortunately, ME programs are not as rigorous as MS programs. If your plans are for career development then it would be good, but if your plans are to eventually pursue a PhD then it would not be.

The MBA and the consulting experience won't help much if your ultimate goal is an academic position. As NewBoston suggested, you would have a better chance by doing a MS degree, getting some research experience in your intended field, and securing a few academic LORs.

I'm curious how you will do a TA/RA without aid at Stanford. Usually, those positions are limited and are the aid itself (coupled with a tuition waiver). As an MS student, I wouldn't count on getting an RA position at Stanford if one wasn't already offered.

Appeal to authority as logical fallacy

Even though I don't have a direct answer, my advice is to start with the typical Top 10 programs, which tend to be strong across the board. Identify the faculty that specialize in Distributed Computing, and then branch out from there by looking at coauthors or referenced authors in their listed papers. After a while you should start to see a trend developing of professors who are consistently referenced by others. CiteSeerX and Google Scholar are really helpful at finding how often a paper is cited by others. And I say all this because your potential advisor's reputation is far more important than a departmental or specialization ranking. When you go to apply for your first academic appointment, it will be your advisor who gets you the job. But you didn't specify MS or PhD, and I imagine this is far less important in industry.

Personally, I would choose free Wisconsin with TAship over Stanford + >60K in debt. And I think the TAship would be very helpful for you regardless of your career aspirations. Helping someone else learn something can often be the best way to master it yourself. And I'm willing to bet that having the TAship (and scholarship) will help in getting personal attention from professors versus being another number in the Stanford MS gravy train.

Which program at Oxford, if you are able to elaborate? And what is the funding situation? I'm not too familiar with how the funding works at UK schools, but it seems to be much more difficult than in the US. Also, I don't think I've see too many Oxford computer scientists here in the US.

The moral of the story, if it were true, would be to not stalk faculty (for years) to get a letter of recommendation.

Are the NYU and JHU programs also MEng programs? The consensus seems to be that the NYC job market is really bad right now. And I would guess that Cornell's reputation would outweigh JHU's reputation by most people doing the hiring (unless you want a job in Baltimore).

In addition to Arizona, some of the other IS programs with "technical" faculty are Maryland, Penn, NYU, Georgia Tech, Indiana, UIUC, Purdue, and Washington. This is not a comprehensive list or even an attempt to list programs that are predominately technical. Some of those programs might have just a couple technical profs, but it's a start. For myself, with an interest in applications of machine learning to data mining, I'll be applying to a mix of CS and IS programs based on specific faculty of interest. My personal take on the field of IS is that it needs to evolve. As an undergraduate curriculum, it is worthless at most schools in preparing students for decent employment. And the graduate curriculum is broken in two. And it's a strange thing when an undergraduate curriculum in a field is not considered worthwhile preparation for its own graduate work. I feel like the position IS should be taking up is in the area of Software Engineering, which I think of as Applied Computer Science. An IS graduate should be able to understand what a business needs, write up the spec, write the code, test it, and deliver a solution. The opportunity for differentiation from Software Engineering offered in CS programs is to have a much lower barrier of entry. I'm thinking no more math than is required of a business major, and no need to know Assembly or even C (unless a student takes it as an elective). The coursework should focus on the software development lifecycle, object oriented programming, web development, and database development.

My opinion is that Carnegie Mellon is a better choice than Georgia Tech, and you have not really given us much to consider for the European choice. I guess if you plan to work in Europe then the European school might be the best choice, but if you plan to work in the US then my money is on CMU.

Another one from YouTube: YouTube - EDU.

Yes, I have run across Professor Sikora. I am very interested in the type of work he does. In fact, it was his research background, and that of Eric Zheng at UT-Dallas, that made me first consider MIS programs in addition to CS programs for my PhD. But Prof Sikora does seem out of place in UTA's MIS department.

Another post also had this link: Michael Franz A CS professor at UC-Irvine puts the dropout rate for graduate school at 50%. He doesn't qualify what he means by that specifically, but it seems in line with Duke's numbers.

If you've already done work with computer vision, a MS may or may not help. If you go to a top tier school in a place like Silicon Valley then it may help a lot (perhaps mostly because of recruiting/networking initiatives). Most employers will consider *what you can do* to be of the most importance. So, it would be vital during your MS to make sure you are involved in as many projects as you can and that you can clearly communicate in a resume your significant contributions to those projects. There's always that trade-off: real work experience versus additional education. My advice would be to search for jobs that sound interesting to you on a site like indeed.com, and pay close attention to what they are looking for in a candidate. And, no, I don't think applying for an MS with an interest in a field other than computer vision would hurt you. I think for MS applicants, the AdCom really just wants to know that you will be able to complete the program. So they will probably look mostly at the GRE-Q, GRE-CS (if not from a well-known program or lacking formal coursework), relevant coursework, and GPA.

I was thinking about another (specific) scenario. Say you successfully follow this advice while working on a MS thesis, and then apply to a different school for your PhD. It seems to me that your research interests should be well-focused at that point you start the PhD, such that it would indeed be appropriate to go from Topic --> Advisor. I don't mean this to negate what he said, which I think makes sense for most cases of going from BS -> MS/PhD.

That was a very interesting read, and makes a lot of sense to me. His opinions on younger versus older advisors was enlightening. I had already assumed younger faculty would be more interested in publishing, but I hadn't thought they would be the better "hand-holders". Obviously, he's writing of extremes and most potential advisors will be somewhere in the middle. And what he writes about personality and "clicking" with an advisor is something we kind of touched on in an earlier thread or two. One program may be better than another, but you really do have to consider how well you're going to get along with the potential advisors at each. Professor Z may be world renown and may even be really nice, but if you're on a completely different wavelength you're just going to end up frustrated in the end.