daveyrocket said:
I'm saying that you shouldn't just be figuring out that there are significant gaps in your knowledge that you should bridge to get a non-academic job a few months before you need a job.
But what happens with Ph.D.'s is that they often don't look for a job until they are "ready" which is a bad thing. If you find that there are a lot of jobs that call for R or SAS, then while you are waiting for the book to arrive from Amazon, you still should be sending out resumes and trying to get interviews.
One serious, serious mistake that I've seen people do is to put everything on hold until they get the right skills. This misses the issue that the job hunt is part of your education. For example, if you go into an interview for a SAS job, and it becomes obvious that you are just not qualified, this is *VERY* useful because it tells you what you should study for, for the next interview.
I *have* heard at least one professor give the advice that you shouldn't pursue any interests outside of physics. The academic physics environment, at least in my experience, is chalk full of the attitude of "give me a faculty position or give me death."
Sure, and that's a problem.
Another really common attitude is that if you don't get an academic job, you can always get a job in industry easily because physicists are smart and you can learn anything you need on the job.
This tends to be true. However, it is one of those "this is true but..." It's true that a physics Ph.d. can get a job in industry, but it requires learning a different set of skills.
It's rather arrogant and if you carry that attitude to a job interview you can kiss the job good-bye.
Depends on the details. Also, on the one hand, you don't want to look too arrogant in an interview, but on the other hand figuring out how to do that isn't quantum field theory.
Also, a little arrogance is a good thing. You can get into equal amounts of trouble by being "not arrogant enough." If you come into an interview with the attitude "I'm a loser that's begging for a job" that will kill your chances just as much as coming across too strong. Doing well in a job interview is a skill, and one thing that you should expect to do is to totally bomb your first few job interviews, but that's part of your education. You want to be humble but not desperate. Self-confident, but not arrogant.
Tricky, but it's not QFT.
You've said it yourself that one of the reasons you found success outside of academics was because you spent time learning skills that are useful outside of physics. Not a single one of my peers has ever mentioned being encouraged to do that.
I'm encouraging you. :-) :-)
One thing that is true is that I was *actively discouraged* from doing what I did.
But if you count on your dissertation research being useful in industry 5 years after you start it, you may find yourself in for a rude awakening. (Mine is not nearly as useful as I'd like.)
It turns out that mine was extra useful. One thing that's not clear to me is whether my dissertation topic is inherently more useful to industry, or whether I've just read enough about sales and marketing so that I was able to "sell" my research more effectively.
Sales and marketing is a "problem-solving" issue. I've got X. The customer wants Y. How do I bridge X and Y? Also part of it involves being *active*. How do I *make* my research useful to people with money.
It doesn't matter what I do now with R, I won't be able to say much other than "well I've played around with it in my spare time."
That's today. What can you get done in three months? What can you get done in three weeks? What can you get done in three days? What can you get done in three hours?
You can put a positive spin on it, but it's just not going to carry the same weight as being able to point out specific achievements.
Right, but if you have three weeks, you can make some specific achievements. Download the software distribution, go to the bug list, find a bug and fix it.
ANOVA might be simple, but if you put "statistics" on your resume because you've done statistical mechanics, and an interviewer asks you if you've ever done an ANOVA, you don't want to say, "I don't know what that is, but I'm sure it's easy to learn."
The answer is yes I have done an ANOVA. I have some Ph.D. data that I ran ANOVA against, and this is what I got. It turns out that ANOVA isn't useful for my data because of X, Y, and Z.
Also, interviewer asks you want ANOVA is. Answer, I have no clue. Game over, you lose. But th at's not the end. You go home, go over the questions you bombed, study ANOVA. Next day, another interviewer with a different employer. What is ANOVA? ANOVA is a statistical technique for spliting up variances into those that are based on models and random non-model variances.
One thing that you'll find is that interviewers will tend to ask exactly the same questions, so if you bomb a question, go home, find the answer, and you'll be good when they ask you the same question.
Graduate school is a fairly unique opportunity where you get to pick your tools and make them work for you. If you learn to use SAS and an interviewer asks you what you've done with it you can say "I did this project with SAS, and we published this paper thanks to that work," that's a lot better than saying "yeah I spent two days learning how to do an ANOVA, so it's cool."
But if you've published papers using SAS, the interviewer may toss your application for being overqualified. At least for the jobs involving SAS, that I'm aware of, it involves data processing for medical experiments. If you've published papers using SAS, you are overqualified, and will not get the
job.
There *are* jobs in which they are looking for Ph.D.-level statisticians, and you will not get those jobs. However, there are a lot of jobs which are basically entry level data entry, and if you spend a week teaching yourself ANOVA, that's enough for those jobs. There are jobs in which I'm massively overqualified for, but statistics is not one of them.
Also different employers want different things. A job interview is a lot like dating, and what one employer will hate, another employer will love.