I recently left my PhD program too (I'm "mastering out" in local parlance ;)) and what this post doesn't address is what I found to be the hardest part: making a decision to leave a world where people that leave are construed as a failures (note: contrary to my expectation, I've only received support in my decision from my awesome and anomalous department.) (Also, I should note that I left a software industry job to do the PhD so I knew I shouldn't have difficulty finding a job.)
For anyone who wants to take the leap but is afraid or unsure, I offer some words that were incredibly helpful for me from some of fantastic friends. To quote my amazing advisor from his response to my "I'm leaving" email:
'We had a Head of Department at Lancaster who used to stomp around the corridors moaning - "I've just lost another student to industry. He's got a great job, has a starting salary bigger than mine, is working on a fabulous project with better resources than we have. In what mad world is this judged as a failure?"'
And another colleague, who's currently a junior professor:
'You know, most Ph.D.ers are smart and successful people. Hence they have a difficulty in saying “This is not for me”. They instead say “I’ve been successful all my life, and I finished everything I started, so I should finish this as well”. By saying that, they choose to hang in there for many years in a depressed state.
Sometimes, the most courageous thing and the best thing to do is to quit when you know you would rather work in another capacity, or when you know you don’t want to work in academia. I congratulate you on your decision and I hope the best for you. ( In case you later decide to come back to academia, it will be waiting for you, so I would not worry about it.)'
Best of luck, those of you that are struggling with the decision. If you're anything like me, if you decide to leave you'll feel better than you have in years =)
I took the same route, "mastering out", and that cultural perception that "leaving your PhD is failing at life" was my biggest hurdle. I knew I could get a job, knew that I could probably find something interesting, but the looks of pity and disgust from my colleagues were not so easy to ignore. I think the comment I heard most often was, "what a waste."
Until I was actually out. Then I started getting furtive emails asking me questions like, "how do you write a resume?", "is the pay ok?", and most often, "are you happy?"
I still think a PhD is absolutely worth it in many cases, and trains you to be a scientist better than any other path. But it's a long hard road, and there are many people out there (me included) who do it because it's presented as the default path. If you're interested in physics, or biology, or any other science, it's just what comes next after undergrad. And that's not a good enough reason.
Edit: 'You know, most Ph.D.ers are smart and successful people. Hence they have a difficulty in saying “This is not for me”. They instead say “I’ve been successful all my life, and I finished everything I started, so I should finish this as well”. By saying that, they choose to hang in there for many years in a depressed state.
Part of that's due to incentives; like a corporation, universities don't primarily think institutionally about what's in your best interests. Hence you also rarely find companies happy that you left to take a better job, though you might find some individual managers who're supportive of a decision to move on.
For universities, people who leave PhD programs earlyish are a particular negative, because at least in the US, typically universities subsidize the masters portion of your education if you enter into a PhD program, out of research funds or departmental TA funds, whereas students who enter explicitly intending to get a masters have to pay tuition, and don't usually receive a stipend (though a few can land TAships). What they expect in return is that later in your PhD you'll publish a bunch of papers which bring them some prestige, help out on grant applications, etc. So if you enter a PhD program, stay for 2 years for the coursework/masters portion, and then leave, from the university's perspective it's like you're really a masters student who somehow tricked them into treating you as a PhD student for 2 years, and got away without paying tuition--- but without staying long enough to produce the expected ROI in publications. It also hurts the graduation-rate statistics in some ways of calculating them, and as education is getting more metrics/assessment heavy, that can matter too.
At least, that's from an administrative/bean-counter perspective; from a cultural perspective among researchers themselves, attitudes are more varied and have more complicated reasons. In areas where industrial partnerships are important, I think the reaction is often fairly positive, because a former student now working at a large company is a good connection for a lab to have.
Some of the negative reactions I think are just due to people not conceiving that other people could have different preferences/aptitudes. You can also find the reverse, where e.g. the attitudes if you left a startup after two years to pursue grad school can range from confused to "you're throwing away a golden opportunity" type views, to outright derisive ("couldn't make it in the real world"). Unfortunately I think it's pretty common for people to be pretty invested in what they did as the definition of success (whether it's research or entrepreneurship or whatever), and to generalize that to thinking that people who quit that route and try another one are therefore failures.
This is funny to me. I decided I was definitely leaving during an internship and most of my coworkers and friends thought it was a great idea. My advisor and closest academic colleagues were supportive as well.
I joked that the PhD program was like a girlfriend no one liked, but they waited to say so until I quit.
Do keep in mind, while we're all bashing academia, that there are those who go the opposite direction. I finished a researchy undergrad, went to industry, basically got miserable, and have gone right back to apply for PhD programs. I'm incredibly happy to know I have at least the one admission, even if it doesn't have guaranteed funding!
> Also, if your degree is in English, the best I can do is point you here (link to Starbucks job page).
I know it's supposed to be funny (i.e., it's "just a joke") and I grant I may be sensitive since I have a BA in Comparative Literature (a graduating class of 1 in my major) and a PhD in Classics, but this came across as completely obnoxious and unnecessary. Don't know anything about Humanities? Fine. Don't talk about them.
Still, thanks for the link to Software Carpentry. I didn't know the site, and it looks like a good place to point people for introductory material on the shell, make, and so on.
And also wrong. My wife left academia from philosophy and is pulling in north of 100k right now doing project management stuff. She got there within 3 years, though we're in NYC so it's easier to get more cash here. It was a hard to transition, most employers don't know what to do with a humanities degree, but she was able to do it. A programmer coworker of mine also worked in philosophy, and transitioned to programming and is doing rather well for himself. He did this over the past few years, so long after computer science was well established. Transitioning out of humanities academia into non-academic jobs is difficult, much harder than for those in the sciences, but it's quite doable
Agreed. I'm an English PhD working in the tech industry and I am very happy. Left a great postdoc a year ago and it took me all of 3 weeks to find a job in industry. Not to mention I'm making double what I would have been making as a starting TT faculty member.
Humanities are the hardest discipline to get a PhD in. The leap from a top student in a CS undergrad program to a CS PhD program is not a big one. Most top students in an undergrad CS program can make the intellectual leap necessary to get a PhD in CS.
But for an undergrad in a humanities discipline, making the leap to getting a PhD is a significant intellectual challenge. Reasoning about ideas, communicating them, and validating them, are infinitely harder in humanities than in CS. I would say, in terms of raw critical thinking skills, i.e., not pertaining to a specific discipline-specific problem, humanities PhDs would outperform CS PhDs.
And I say this as someone who is currently in a CS PhD program. I can read other engineering PhD theses and still make sense of it. When I read a humanities theses, I am completely lost.
Slightly offtopic question: I re-read Odysseus a couple of weeks ago and was wondering about the name Telemachos. My rudimentary knowledge of etymology tells me that he's a "far away man". Given your username and the fact that you have a PhD in classics: what's your opinion of how the name/etymology of the character relates to the role that Telemachos plays in the epic?
Obviously he's far away when Odysseus is out and about, but he's quite present in the beginning and end. It seems like there's more to it than that.
(For those who don't remember / haven't read it, Telemachos is the only son of Odysseus in the epic. He's pretty much a younger copy of Odysseus.)
Salve! I too tend to react negatively to summary judgment of humanities degrees. But the truth is that you and I and hogu's wife are outliers. For most humanities PhDs, those judgments are accurate. In fact, part of the reason I mastered out of my English PhD program was that the faculty and other students just weren't particularly bright (the other part was the academic politics). The humanities themselves are absolutely crucial subjects that all smart people should study -- but most people who study them aren't very smart.
Ha! Another Classics Ph.D. here :-). And there are lots of people in programming with advanced Humanities degrees. STEM majors aren't the only smart people around.
I received my PhD (Physics) in 1999 and went directly to industry. At that time I saw industry as a well paid post doc. My intention was to work a few years and then seek an academic position.
In 2001 I had an offer from a top engineering school in Canada to join their faculty. And I said no.
Academia attracted me with the promise of intellectual freedom -- the ability to work on what ever problems I wanted to. This is a lie. To be successful in Academia, one needs to pick a narrow field and become the biggest expert in the smallest space. Neils Bohr described this as knowing more and more about less an less until you know absolutely everything about nothing.
In Industry I can move around to whatever problems fascinate me most. I've worked in Optical Componetns, designing video games, and now what I call computational sociology on social networks. My pattern has been to work in a field of about five years until I've built up the expertise I desire to have, then move on to a completely different field.
For those recently dropping out of academia or considering dropping out, you know in your heart it is the right choice. Don't worry about what people in that little club think. A decade from now they will be looking at you. With envy because you are free to work on the most exciting and interesting problems while they are stuck in the same shrinking field for the last ten years.
I'm doing a Masters in theoretical physics and I've got to ask, is it common to first go work a few years before going into academia? It surprises me how little we are informed of these things.
Being locked in is my biggest fear for going for a PhD. I don't want to spend the rest of my life on a single topic, perhaps I don't even want to stay in theoretical physics.
If you're in the right areas of computer science, esp. machine learning, the answer seems to be that you have to make active effort to resist leaving academia, but not much active effort to let yourself get hired away. The recruiting at grad schools and even academic conferences from folks like Palantir has gotten pretty aggressive lately! They're particularly good at supplying temptation at that moment when a student's finished everything but writing up the dissertation ("ABD").
I'm finishing my PhD, and starting a post-doc in the fall. I had wanted to break out into industry, because I didn't see myself in academia in the long term, but I didn't get anywhere with job applications, so here I am.
I have a few questions:
1. My work consumes most of my time and energy - how would one find time to work on side projects and build a portfolio, when the academic workload is so all-consuming
2. I felt like I had to apply for industry jobs in my niche, otherwise I would be competing against a much larger pool of general engineers/science graduates. Considering that I've spent most of the last 5-6 years focusing on work in a specific niche, how can this be used for leverage for more general technical positions?
(Because if you're a theoretical chemist trying to become an industrial chemist, the advice below is probably useless. But since you refer to "side projects and a portfolio", I'm going to assume for now that you must be some kind of programmer.)
So:
First, a tiny story: At the corresponding time in my own career, I turned in my thesis, turned down a couple of jobs in my old field, moved into my parents' spare room, and spent three or four months teaching myself web development from online materials and a handful of books. (SICP, Learning Emacs, Introduction to TCP/IP, SQL for Smarties… the usual suspects.) Then I got a job. (It was the peak of the 1990s bubble, a good time to get a programming job. But, then, today is also a good time to get a programming job.)
The right way to save time on your academic workload is to stop doing academic work. Do you see yourself in academia in the long term? No. So why are you working on a postdoc? Who ordered you to get a postdoc? You did. Who is making you spend time and energy on that postdoc? You are. Write yourself a resignation letter, give yourself two weeks' notice, and quit.
The right way to escape a niche is to leave. You don't need to expunge the niche from your permanent record or anything - it's nothing to be ashamed of, it deserves a nice spot on your resume and is good for years of future anecdotes and impromptu lectures. But if the niche doesn't get you paying work, it may be time to let it rest for a while.
The right way to get any technical position is to demonstrate that you've done and enjoyed the kind of work that the position will ask you to do. If you want to build web sites, build a web site or two. If you want to build iOS applications, build an iOS application.
These things will not be sexy to an academic audience. You have to deal with that.
Don't worry about immersing yourself in the "larger pool". The reason there's a large pool of people doing X is: X is where the money is. And X is where the money is because, no matter how many people are doing X, there always seems to be more work to do than there are talented people to do it. Programming is in an expansionary phase, and there's a lot to be done.
1. My work consumes most of my time and energy - how would one find time to work on side projects and build a portfolio, when the academic workload is so all-consuming
Turn off the TV, close Ph.D. comics stop procrastinating. That's a fairly glib and slightly offensive answer, but it's the best I've got. There are no shortcuts. Getting yourself ready to work in industry will take time and effort.
I can, however, suggest that adopting a more rigid industry-style work schedule can help. In grad school, my time management skills sucked, and I'm pretty sure I wasn't alone in that.
One thing you can sometimes do to speed things up is to take pieces of your research and turn it into side projects. For instance, something I didn't do (but should have) was properly package/test my numpy shared memory library.
2. I felt like I had to apply for industry jobs in my niche, otherwise I would be competing against a much larger pool of general engineers/science graduates.
You overestimate the number of general engineers who are capable of quantitative work. When I said there is value being the guy in the room who understands regression and confidence intervals, I meant it.
Concerning the workload:
As far as I'm concerned, the academic workload is definitely not all-consuming. From watching my colleagues, I came to the conclusion that everybody chooses their own workload. Some of my coworkers come at 9AM and leave at 4PM; others think they absolutely must finish that experiment and stay until 10PM. Some people think they must immediately rush to the lab if their supervisor sends them an email on Saturday afternoon; others just ignore the email until Monday morning.
I have never heard of anybody getting into trouble for turning their computer off on the weekend, or working normal hours; I think much of the pressure is self-inflicted.
Personally, I currently work in academia (doing my PhD), and I run a profitable business on the side (selling my own software). Sometimes this is a bit stressful (when you have to prepare a talk and keep getting emails from customers because of a nasty bug you introduced in the last release), but most of the time this works out just fine.
As a researcher who just accepted a private job, I do have some experience here. My advice is to make use of the fact that academic jobs usually have unsurpassed autonomy and flexibility. If you aren't looking to get a faculty job, think of the postdoc as a relatively low-stress way to prepare for the transition out of academia. Not to say you should blow your job off, but make use of the opportunity to learn things that increase your cross section for industry and are still relevant in your research (even if it would not be the optimal thing to do if you were looking to crank out papers for your career in academia). Most of the academic workload is self-imposed, in the sense that you worry about the rat race for getting the next job, but if you are not interested in continuing your academic career, that's a non-factor.
> 100 passengers have queued up to board a plane, and are lined up in the order of the seats on the plane (n=1..100). However, the first person lost his ticket and selects a random seat. The remaining passengers will occupy their assigned seat if it is available, or a random seat otherwise. What is the probability that passenger 100 sits in seat 100?
It really worried me that I couldn't figure out how to work that out. I thought about what I'd do in an interview if I was asked that, and I figured (if I had my laptop) I'd write some code. So I gave myself 5 minutes by the clock, and wrote a little python program that simulated the situation and counted the results. I ran 5 million iterations, which with pypy took 59 seconds. The number I got out was a 97.335% chance of person 100 being in seat 100.
I have 3 questions:
1. Is that the right answer?
2. How are you supposed to work it out?
3. Would working it out the proper way take less than 6 minutes?
Edit: It seemed like too high a number, which is part of why I asked. I looked through my code and found a dumb error - I forgot to remove the seat from the 'available' list if the person finds their assigned seat. That's what happens when you write code on a 5 minute deadline. Now I'm consistently getting 49.9%, which seems more reasonable.
For 3 passengers the seating possibilities are
{123,213,231,321}, so 3 gets to sit on seat 3 50% of the time.
For 4 passengers the seating possibilities are
{1234,2134,2314,2341,2431,3241,3214,4231}, so 4 gets to sit on seat 4 50% of the time.
From there you can do a proof by induction.
If you like code:
----
import util.{Random=>rng}
import collection.mutable.ArrayBuffer
object passengers {
def main(args:Array[String]) = {
val n = args(0).toInt
val simulations = args(1).toInt
val happyLast = (1 to simulations).map(_=> {
val seats = ArrayBuffer.fill[Int](n+1)(0)
val taken = new ArrayBuffer[Int]
val all = (1 to n).toSeq
val seat = rng.shuffle(all).head
seats(seat) = 1
taken += seat
(2 to n).foreach( p=> {
val seat = seats(p) match {
case 0=> p
case x=> rng.shuffle(all.diff(taken)).head
}
seats(seat) = p
taken += seat
})
seats(n) == n
})
println( happyLast.filter(x=>x).size*1.0d/simulations )
}
}
I think this is more slightly more on-topic as a reply here
> Consider the integers from [0,1000]. Suppose a particle starts at position n. At discrete instants of time t=0,1,2,…, the particle moves up or down with p=0.5. What is the probability that the particle reaches 0 before t=1000?
The answer is 50%, right? I can't explain it properly, but my thinking is that the random walk is symmetric and 0 and 1000 should have equal weights. Maybe a good explanation is harder than the correct answer in this case.
I have no idea if that is correct, but my initial intuition was that it would be a high percentage like you found. However, now that I'm thinking about it, I believe the answer is closer to 50%.
When passenger 100 begins to board, there are only two possible configurations he can find the seats in: seat 1 available or seat 100 available. If Passenger 1 sits in seat 100, then passengers 2-99 will sit in their correct seats, leaving only seat 1. If passenger 1 sits in seat 1, passengers 2-99 will sit correctly and leave only seat 100 for passenger 100. If passenger 1 sits anywhere else (say, seat 50), passengers 2 - 49 will fill correctly, passenger 50 will have to sit in seat 1 or 51-100. The moment a passenger randomly selects seat 1 or 100, the remaining passengers will sit correctly (except for passenger 100). It is impossible for any seats other than 1 or 100 to be left for passenger 100 to select.
From looking at it, I don't believe there is any bias for the end configuration to leave seat 100 open more than seat 1, and I believe that in an interview situation, you would be expected to intuit this by "talking it out". If I'm right (and I could still be very wrong), this is more of a probabilistic brainteaser than a programming interview question.
I left a PhD program a few years back (technically graduated, but I don't think of it that way). A year or so before I did, I remember very clearly being out to drinks with my professor and fellow students, and mentioning that I had no intention to continue a career in academia. In fact at the time I was thinking of translation (I've since drifted to web development, but languages and translation are still central to much of what I do).
The response (in Japanese, but I'll translate) was "what a waste" ("mottainai"). What a waste. All that potential I had, and now I was just going to waste it on "work", like everyone else. No doubt my supervisor, who said it with a genuinely disappointed look (echoed with a nod by a fellow student and friend sitting beside me, which only made it worse) meant it in a positive sense, but I never forgave him for it. It stuck with me, somewhere very deep inside me, first as something confusing and distressing, then as a kind of symbol, something emblematic of everything that is wrong with academia.
To anyone who is hesitating: if your only reason for staying in academia is the fear of what will happen if you leave, then it is time to leave.
The post is awesome. Having just "graduated" (dropped out of my PhD program) and also interviewed a bunch of really smart folks who are making the transition to the "real world", I cannot emphasize the importance of the following for a good machine learning/engineering interview:
1. It doesn't matter if you have done research in a topic, do brush up on simple machine learning algorithms. The math after going through a PhD should be really easy for you. However, it is hard for an interviewer to gauge how good your math background is when you have done most of your quantitative work in a totally different field and you don't have enough of a common intersection.
2. Write code! If you are joining a small to middling start up, even if you will eventually be doing quantitative stuff, your peers will be people from engineering and they will evaluate you as a programmer. Brushing through something like the Algorithm Design Manual is going to be amazingly useful. Back in school, there was a linguistics professor who wrote most of his code in Scala, used version control and pivotal tracker. If someone who is working towards tenure can do it, there is no excuse for you. :-)
3. Try to work on tiny projects that involve some data analysis and some programming. If you are an R guy, do it in python and vice versa. Most importantly, think of the whole pipeline: taking the data (Infochimps/ AWS public datasets), cleaning it up, processing it using a random machine learning algorithm (pick any, its an easy and awesome learning opportunity), and then visualizing it. Repeat with cooler tools (E.g. d3.js for the visualization etc). The importance of this process is that it moves you away from the traditional anonymized datasets that you see in academia and helps you encounter real world data and gain intuition for that. This is incredibly helpful in the interview process because it allows you to get good ideas about other people's data and how to think about it.
What about negative anecdotes? Has anyone here regretted his or her decision to leave academia? Particularly, have you found the work less rewarding or interesting?
I ask as a grad student with a background in chemical engineering. I’ve known two professors who’ve done the inverse – industry to academia – and they’ve provided convincing stories for academia. In both cases they found academic research (in engineering and the physical sciences) to be more interesting and personally rewarding than industry.
Is the situation different in software and related industries?
I ask, as I’ve been considering transitioning to such a career myself after grad school (my research is in computational modeling and simulation). My biggest fear is that I’ll find myself in a job that I find boring and will regret my decision. Doubly so, as such a career transition will make it very difficult to reenter my academic field.
Just tossing in that Finance isn't your only option for a healthy PhD paycheck in NYC. With exactly those same skills you could work in silicon alley or at a company like mine which does analysis on bank (or other corporate) data.
Also, you don't have to sell your soul to work outside of Academia. For example, we find terrorists and drug lord hiding cash in USA banks (Anti-Money Laundering) using machine learning. It's very satisfying and the pay is good too.
Here's a simple checklist for all you CS (Systems) PhDs not sure what to do next. When writing a paper, which section of the paper are you most excited by?
A) Introduction (big picture/motivation/related work): You should remain in academia where you will enjoy writing grant proposals, or be a senior exec (not hands-on) in industry.
B) Design/Implementation: Academia is not for you. Join a cool startup or Google/Facebook/... (or of course start your own venture)
C) Experimental Evaluation/Results: Join industry (tech or finance) in a quantitative role.
YMMV. Also, this is not applicable to PhDs in Theoretical CS or in other Sciences.
The default behavior of grep appears to be to not treat {}?() as special characters. Which is generally boneheaded in my experience, and certainly in this case. An irritated man will use egrep--short for grep -E, "extended regexes"--i.e. sane regexes:
egrep "[0-9]{3}-?[0-9]{3}-?[0-9]{4}" filename.txt
This is just a direct translation. If I wrote the regex myself, I would probably also recognize spaces as delimiters (as in "999 999 9999"), and maybe note that the area code might be missing or have parens around it, etc.--depending on just what I was trying to parse. I dunno, maybe he's parsing regular machine output and his original regex would suffice, so I won't go farther than an equivalent translation. But I will, for kicks, mention this:
egrep "([0-9]{3}-?){2}[0-9]{4}" filename.txt
(Note that - is not a special character in either grep or egrep, although command-line programs in general treat specially any argument beginning with -, so it is a good idea to use [-] instead of - if that happens to be the first character of your string. But after that, no.)
Also, for general purposes, grep -P (Perl regexes) is best of all, because it can do *? non-greedy matching. A really irritated man has aliased pgrep to "grep -P --color=auto" in his bash startup file, and has also (perhaps dangerously) aliased grep to it as well. (That breaks fewer things than altering GREP_OPTIONS.)
"In academia, the end product is a publication and your code needs to work only once."
In my experience as ex-academic, the above is all too true in too many computational fields (and many sub-fields of CS). So I ask what scientific value (or any kind of value, for that matter) is being generated by grinding out publications with results that won't be repeatable?
Lots of journals (at least in my biological sciences field) require you to submit your code along with your publication so that a) the editor can repeat your results to accept your publication and b) for other scientists to repeat your stuff. Biostatistics AFAIK has an editor responsible solely for reproduction of results.
Personally saying lots of my code is repeatedly re-used by colleagues who can't program, for example scripts that parse BLAST-output and get the best result for each query etc.
I know this is cliched, but of the ~30 students who started the Computer Science Ph.D. program at Stanford in 2006 with me, maybe 10 have dropped out. A few of those ten have gone onto successful startups and are now far, far, far wealthier than any of us who remained in the program could hope to be :)
This is an interesting sample. What happened to those who dropped out but did not strike it rich in Startupland?
I think one of the hardest parts about leaving is showing that you are moving onto better things; it's easier to _tell_ people you are leaving if you have a 150k+ offer. Leaving to try a startup would be socially difficult to communicate
Seriously? Learn web development?
No one hires PhDs for web development skills. It is only useful if you want to be a pure software developer, not a quant or a data scientist. It might even be a negative when applying for some jobs.
Well they might not be hired to do just web development but I think learning about the web stack is incredibly useful because it helps you get your work out to people. This is important especially in a (early stage) start up where you might have to rapidly prototype solutions and you can't have your own pet designer to whip up visualizations for you. In fact, if you read the blogs of some of the data scientists at twitter/linkedin, its immediately clear that they have some know-how of the web stack in the way they are organizing and visualizing results. (Everyone likes pretty pictures)
No one hires PhDs for web development skills, but knowing some basics certainly opens up some doors. I wouldn't want to hire a PhD who would couldn't build at least a demo himself.
Could you describe a circumstance where such skills would be a negative?
"When you are ready, the teacher will appear" - Buddhist saying. Getting through a PhD is easier and more fun if your mind is into it. Yours is clearly not, so why try to swim upstream? Go where your mind and soul are telling you that you belong. If you ever decide the PhD is the right move for you, you can re-open that chapter and go for it.
I'm getting ready for a change in career, and for the exact same reasons.
He's using Hyde and recommending the article's audience to use Django or Rails? If he's going to point out webdev with only some fleeting interest it might be more helpful just to say something like, "HTML/CSS is your new weekend project. Hardcode it in a couple hours or use whatever system gives you the least pain: SSG, framework, blog-based, etc."
[+] [-] sunahsuh|14 years ago|reply
For anyone who wants to take the leap but is afraid or unsure, I offer some words that were incredibly helpful for me from some of fantastic friends. To quote my amazing advisor from his response to my "I'm leaving" email:
'We had a Head of Department at Lancaster who used to stomp around the corridors moaning - "I've just lost another student to industry. He's got a great job, has a starting salary bigger than mine, is working on a fabulous project with better resources than we have. In what mad world is this judged as a failure?"'
And another colleague, who's currently a junior professor: 'You know, most Ph.D.ers are smart and successful people. Hence they have a difficulty in saying “This is not for me”. They instead say “I’ve been successful all my life, and I finished everything I started, so I should finish this as well”. By saying that, they choose to hang in there for many years in a depressed state.
Sometimes, the most courageous thing and the best thing to do is to quit when you know you would rather work in another capacity, or when you know you don’t want to work in academia. I congratulate you on your decision and I hope the best for you. ( In case you later decide to come back to academia, it will be waiting for you, so I would not worry about it.)'
Best of luck, those of you that are struggling with the decision. If you're anything like me, if you decide to leave you'll feel better than you have in years =)
[+] [-] ajdecon|14 years ago|reply
Until I was actually out. Then I started getting furtive emails asking me questions like, "how do you write a resume?", "is the pay ok?", and most often, "are you happy?"
I still think a PhD is absolutely worth it in many cases, and trains you to be a scientist better than any other path. But it's a long hard road, and there are many people out there (me included) who do it because it's presented as the default path. If you're interested in physics, or biology, or any other science, it's just what comes next after undergrad. And that's not a good enough reason.
Edit: 'You know, most Ph.D.ers are smart and successful people. Hence they have a difficulty in saying “This is not for me”. They instead say “I’ve been successful all my life, and I finished everything I started, so I should finish this as well”. By saying that, they choose to hang in there for many years in a depressed state.
This.
[+] [-] _delirium|14 years ago|reply
For universities, people who leave PhD programs earlyish are a particular negative, because at least in the US, typically universities subsidize the masters portion of your education if you enter into a PhD program, out of research funds or departmental TA funds, whereas students who enter explicitly intending to get a masters have to pay tuition, and don't usually receive a stipend (though a few can land TAships). What they expect in return is that later in your PhD you'll publish a bunch of papers which bring them some prestige, help out on grant applications, etc. So if you enter a PhD program, stay for 2 years for the coursework/masters portion, and then leave, from the university's perspective it's like you're really a masters student who somehow tricked them into treating you as a PhD student for 2 years, and got away without paying tuition--- but without staying long enough to produce the expected ROI in publications. It also hurts the graduation-rate statistics in some ways of calculating them, and as education is getting more metrics/assessment heavy, that can matter too.
At least, that's from an administrative/bean-counter perspective; from a cultural perspective among researchers themselves, attitudes are more varied and have more complicated reasons. In areas where industrial partnerships are important, I think the reaction is often fairly positive, because a former student now working at a large company is a good connection for a lab to have.
Some of the negative reactions I think are just due to people not conceiving that other people could have different preferences/aptitudes. You can also find the reverse, where e.g. the attitudes if you left a startup after two years to pursue grad school can range from confused to "you're throwing away a golden opportunity" type views, to outright derisive ("couldn't make it in the real world"). Unfortunately I think it's pretty common for people to be pretty invested in what they did as the definition of success (whether it's research or entrepreneurship or whatever), and to generalize that to thinking that people who quit that route and try another one are therefore failures.
[+] [-] rgraham|14 years ago|reply
I joked that the PhD program was like a girlfriend no one liked, but they waited to say so until I quit.
[+] [-] eli_gottlieb|14 years ago|reply
[+] [-] arethuza|14 years ago|reply
That was very much the view when I left - is it a view that is particularly common in the UK?
[+] [-] telemachos|14 years ago|reply
I know it's supposed to be funny (i.e., it's "just a joke") and I grant I may be sensitive since I have a BA in Comparative Literature (a graduating class of 1 in my major) and a PhD in Classics, but this came across as completely obnoxious and unnecessary. Don't know anything about Humanities? Fine. Don't talk about them.
Still, thanks for the link to Software Carpentry. I didn't know the site, and it looks like a good place to point people for introductory material on the shell, make, and so on.
[+] [-] hogu|14 years ago|reply
[+] [-] squishypirate|14 years ago|reply
[+] [-] LetBinding|14 years ago|reply
But for an undergrad in a humanities discipline, making the leap to getting a PhD is a significant intellectual challenge. Reasoning about ideas, communicating them, and validating them, are infinitely harder in humanities than in CS. I would say, in terms of raw critical thinking skills, i.e., not pertaining to a specific discipline-specific problem, humanities PhDs would outperform CS PhDs.
And I say this as someone who is currently in a CS PhD program. I can read other engineering PhD theses and still make sense of it. When I read a humanities theses, I am completely lost.
[+] [-] oskarth|14 years ago|reply
Obviously he's far away when Odysseus is out and about, but he's quite present in the beginning and end. It seems like there's more to it than that.
(For those who don't remember / haven't read it, Telemachos is the only son of Odysseus in the epic. He's pretty much a younger copy of Odysseus.)
[+] [-] FreakLegion|14 years ago|reply
[+] [-] apdinin|14 years ago|reply
[+] [-] hcayless|14 years ago|reply
[+] [-] unknown|14 years ago|reply
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[+] [-] kentonwhite|14 years ago|reply
In 2001 I had an offer from a top engineering school in Canada to join their faculty. And I said no.
Academia attracted me with the promise of intellectual freedom -- the ability to work on what ever problems I wanted to. This is a lie. To be successful in Academia, one needs to pick a narrow field and become the biggest expert in the smallest space. Neils Bohr described this as knowing more and more about less an less until you know absolutely everything about nothing.
In Industry I can move around to whatever problems fascinate me most. I've worked in Optical Componetns, designing video games, and now what I call computational sociology on social networks. My pattern has been to work in a field of about five years until I've built up the expertise I desire to have, then move on to a completely different field.
For those recently dropping out of academia or considering dropping out, you know in your heart it is the right choice. Don't worry about what people in that little club think. A decade from now they will be looking at you. With envy because you are free to work on the most exciting and interesting problems while they are stuck in the same shrinking field for the last ten years.
[+] [-] kmm|14 years ago|reply
Being locked in is my biggest fear for going for a PhD. I don't want to spend the rest of my life on a single topic, perhaps I don't even want to stay in theoretical physics.
[+] [-] _delirium|14 years ago|reply
[+] [-] narkee|14 years ago|reply
I have a few questions:
1. My work consumes most of my time and energy - how would one find time to work on side projects and build a portfolio, when the academic workload is so all-consuming
2. I felt like I had to apply for industry jobs in my niche, otherwise I would be competing against a much larger pool of general engineers/science graduates. Considering that I've spent most of the last 5-6 years focusing on work in a specific niche, how can this be used for leverage for more general technical positions?
Thanks for the interesting article!
[+] [-] mechanical_fish|14 years ago|reply
(Because if you're a theoretical chemist trying to become an industrial chemist, the advice below is probably useless. But since you refer to "side projects and a portfolio", I'm going to assume for now that you must be some kind of programmer.)
So:
First, a tiny story: At the corresponding time in my own career, I turned in my thesis, turned down a couple of jobs in my old field, moved into my parents' spare room, and spent three or four months teaching myself web development from online materials and a handful of books. (SICP, Learning Emacs, Introduction to TCP/IP, SQL for Smarties… the usual suspects.) Then I got a job. (It was the peak of the 1990s bubble, a good time to get a programming job. But, then, today is also a good time to get a programming job.)
The right way to save time on your academic workload is to stop doing academic work. Do you see yourself in academia in the long term? No. So why are you working on a postdoc? Who ordered you to get a postdoc? You did. Who is making you spend time and energy on that postdoc? You are. Write yourself a resignation letter, give yourself two weeks' notice, and quit.
The right way to escape a niche is to leave. You don't need to expunge the niche from your permanent record or anything - it's nothing to be ashamed of, it deserves a nice spot on your resume and is good for years of future anecdotes and impromptu lectures. But if the niche doesn't get you paying work, it may be time to let it rest for a while.
The right way to get any technical position is to demonstrate that you've done and enjoyed the kind of work that the position will ask you to do. If you want to build web sites, build a web site or two. If you want to build iOS applications, build an iOS application.
These things will not be sexy to an academic audience. You have to deal with that.
Don't worry about immersing yourself in the "larger pool". The reason there's a large pool of people doing X is: X is where the money is. And X is where the money is because, no matter how many people are doing X, there always seems to be more work to do than there are talented people to do it. Programming is in an expansionary phase, and there's a lot to be done.
[+] [-] yummyfajitas|14 years ago|reply
Turn off the TV, close Ph.D. comics stop procrastinating. That's a fairly glib and slightly offensive answer, but it's the best I've got. There are no shortcuts. Getting yourself ready to work in industry will take time and effort.
I can, however, suggest that adopting a more rigid industry-style work schedule can help. In grad school, my time management skills sucked, and I'm pretty sure I wasn't alone in that.
One thing you can sometimes do to speed things up is to take pieces of your research and turn it into side projects. For instance, something I didn't do (but should have) was properly package/test my numpy shared memory library.
2. I felt like I had to apply for industry jobs in my niche, otherwise I would be competing against a much larger pool of general engineers/science graduates.
You overestimate the number of general engineers who are capable of quantitative work. When I said there is value being the guy in the room who understands regression and confidence intervals, I meant it.
The much larger pool of general engineers/scientists is the target audience of blog posts like this one: http://www.zedshaw.com/essays/programmer_stats.html
Do things like Naive Bayes, SVMs and Black Scholes seem straightforward to you? If so, you are a quant. Now you just need to become a developer.
[+] [-] jakobe|14 years ago|reply
I have never heard of anybody getting into trouble for turning their computer off on the weekend, or working normal hours; I think much of the pressure is self-inflicted.
Personally, I currently work in academia (doing my PhD), and I run a profitable business on the side (selling my own software). Sometimes this is a bit stressful (when you have to prepare a talk and keep getting emails from customers because of a nasty bug you introduced in the last release), but most of the time this works out just fine.
[+] [-] lutorm|14 years ago|reply
[+] [-] noelwelsh|14 years ago|reply
It's your job to apply for positions, and their job to filter the applicants. Don't think you have to do their job for them.
[+] [-] robertskmiles|14 years ago|reply
It really worried me that I couldn't figure out how to work that out. I thought about what I'd do in an interview if I was asked that, and I figured (if I had my laptop) I'd write some code. So I gave myself 5 minutes by the clock, and wrote a little python program that simulated the situation and counted the results. I ran 5 million iterations, which with pypy took 59 seconds. The number I got out was a 97.335% chance of person 100 being in seat 100.
I have 3 questions:
1. Is that the right answer?
2. How are you supposed to work it out?
3. Would working it out the proper way take less than 6 minutes?
Edit: It seemed like too high a number, which is part of why I asked. I looked through my code and found a dumb error - I forgot to remove the seat from the 'available' list if the person finds their assigned seat. That's what happens when you write code on a 5 minute deadline. Now I'm consistently getting 49.9%, which seems more reasonable.
[+] [-] dxbydt|14 years ago|reply
For 3 passengers the seating possibilities are {123,213,231,321}, so 3 gets to sit on seat 3 50% of the time.
For 4 passengers the seating possibilities are {1234,2134,2314,2341,2431,3241,3214,4231}, so 4 gets to sit on seat 4 50% of the time. From there you can do a proof by induction.
If you like code: ----
----- >scala -cp classes passengers 100 100000 >0.4997[+] [-] VMG|14 years ago|reply
> Consider the integers from [0,1000]. Suppose a particle starts at position n. At discrete instants of time t=0,1,2,…, the particle moves up or down with p=0.5. What is the probability that the particle reaches 0 before t=1000?
The answer is 50%, right? I can't explain it properly, but my thinking is that the random walk is symmetric and 0 and 1000 should have equal weights. Maybe a good explanation is harder than the correct answer in this case.
[+] [-] dkokelley|14 years ago|reply
When passenger 100 begins to board, there are only two possible configurations he can find the seats in: seat 1 available or seat 100 available. If Passenger 1 sits in seat 100, then passengers 2-99 will sit in their correct seats, leaving only seat 1. If passenger 1 sits in seat 1, passengers 2-99 will sit correctly and leave only seat 100 for passenger 100. If passenger 1 sits anywhere else (say, seat 50), passengers 2 - 49 will fill correctly, passenger 50 will have to sit in seat 1 or 51-100. The moment a passenger randomly selects seat 1 or 100, the remaining passengers will sit correctly (except for passenger 100). It is impossible for any seats other than 1 or 100 to be left for passenger 100 to select.
From looking at it, I don't believe there is any bias for the end configuration to leave seat 100 open more than seat 1, and I believe that in an interview situation, you would be expected to intuit this by "talking it out". If I'm right (and I could still be very wrong), this is more of a probabilistic brainteaser than a programming interview question.
[+] [-] bearmf|14 years ago|reply
[+] [-] shioyama|14 years ago|reply
The response (in Japanese, but I'll translate) was "what a waste" ("mottainai"). What a waste. All that potential I had, and now I was just going to waste it on "work", like everyone else. No doubt my supervisor, who said it with a genuinely disappointed look (echoed with a nod by a fellow student and friend sitting beside me, which only made it worse) meant it in a positive sense, but I never forgave him for it. It stuck with me, somewhere very deep inside me, first as something confusing and distressing, then as a kind of symbol, something emblematic of everything that is wrong with academia.
To anyone who is hesitating: if your only reason for staying in academia is the fear of what will happen if you leave, then it is time to leave.
[+] [-] eshvk|14 years ago|reply
1. It doesn't matter if you have done research in a topic, do brush up on simple machine learning algorithms. The math after going through a PhD should be really easy for you. However, it is hard for an interviewer to gauge how good your math background is when you have done most of your quantitative work in a totally different field and you don't have enough of a common intersection.
2. Write code! If you are joining a small to middling start up, even if you will eventually be doing quantitative stuff, your peers will be people from engineering and they will evaluate you as a programmer. Brushing through something like the Algorithm Design Manual is going to be amazingly useful. Back in school, there was a linguistics professor who wrote most of his code in Scala, used version control and pivotal tracker. If someone who is working towards tenure can do it, there is no excuse for you. :-)
3. Try to work on tiny projects that involve some data analysis and some programming. If you are an R guy, do it in python and vice versa. Most importantly, think of the whole pipeline: taking the data (Infochimps/ AWS public datasets), cleaning it up, processing it using a random machine learning algorithm (pick any, its an easy and awesome learning opportunity), and then visualizing it. Repeat with cooler tools (E.g. d3.js for the visualization etc). The importance of this process is that it moves you away from the traditional anonymized datasets that you see in academia and helps you encounter real world data and gain intuition for that. This is incredibly helpful in the interview process because it allows you to get good ideas about other people's data and how to think about it.
[+] [-] hagy|14 years ago|reply
I ask as a grad student with a background in chemical engineering. I’ve known two professors who’ve done the inverse – industry to academia – and they’ve provided convincing stories for academia. In both cases they found academic research (in engineering and the physical sciences) to be more interesting and personally rewarding than industry.
Is the situation different in software and related industries?
I ask, as I’ve been considering transitioning to such a career myself after grad school (my research is in computational modeling and simulation). My biggest fear is that I’ll find myself in a job that I find boring and will regret my decision. Doubly so, as such a career transition will make it very difficult to reenter my academic field.
[+] [-] Rickasaurus|14 years ago|reply
Also, you don't have to sell your soul to work outside of Academia. For example, we find terrorists and drug lord hiding cash in USA banks (Anti-Money Laundering) using machine learning. It's very satisfying and the pay is good too.
[+] [-] bearmf|14 years ago|reply
[+] [-] dude_abides|14 years ago|reply
A) Introduction (big picture/motivation/related work): You should remain in academia where you will enjoy writing grant proposals, or be a senior exec (not hands-on) in industry.
B) Design/Implementation: Academia is not for you. Join a cool startup or Google/Facebook/... (or of course start your own venture)
C) Experimental Evaluation/Results: Join industry (tech or finance) in a quantitative role.
YMMV. Also, this is not applicable to PhDs in Theoretical CS or in other Sciences.
[+] [-] unknown|14 years ago|reply
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[+] [-] waterhouse|14 years ago|reply
Also, for general purposes, grep -P (Perl regexes) is best of all, because it can do *? non-greedy matching. A really irritated man has aliased pgrep to "grep -P --color=auto" in his bash startup file, and has also (perhaps dangerously) aliased grep to it as well. (That breaks fewer things than altering GREP_OPTIONS.)
[+] [-] yummyfajitas|14 years ago|reply
[+] [-] imaginaryunit|14 years ago|reply
In my experience as ex-academic, the above is all too true in too many computational fields (and many sub-fields of CS). So I ask what scientific value (or any kind of value, for that matter) is being generated by grinding out publications with results that won't be repeatable?
[+] [-] a_bonobo|14 years ago|reply
Lots of journals (at least in my biological sciences field) require you to submit your code along with your publication so that a) the editor can repeat your results to accept your publication and b) for other scientists to repeat your stuff. Biostatistics AFAIK has an editor responsible solely for reproduction of results.
Personally saying lots of my code is repeatedly re-used by colleagues who can't program, for example scripts that parse BLAST-output and get the best result for each query etc.
[+] [-] gammarator|14 years ago|reply
http://insightdatascience.com/fellowship.html
It's a paid six week post-doctoral training fellowship in SV that ends with interviews with companies looking for data scientists.
[+] [-] pgbovine|14 years ago|reply
[+] [-] xxbondsxx|14 years ago|reply
I think one of the hardest parts about leaving is showing that you are moving onto better things; it's easier to _tell_ people you are leaving if you have a 150k+ offer. Leaving to try a startup would be socially difficult to communicate
[+] [-] bearmf|14 years ago|reply
[+] [-] eshvk|14 years ago|reply
[+] [-] yummyfajitas|14 years ago|reply
Could you describe a circumstance where such skills would be a negative?
[+] [-] zafiro17|14 years ago|reply
I'm getting ready for a change in career, and for the exact same reasons.
[+] [-] evoxed|14 years ago|reply
[+] [-] yummyfajitas|14 years ago|reply
Do you believe I should have used Django or Rails for my personal website? Was Hyde the wrong tool for the job?
[+] [-] elazungu|14 years ago|reply