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It irritates me every time I hear this ruddy nonsense that keeps spewing out of Seattle and San Fransisco that we're not cranking out enough computer science graduates.

Hey Microsoft! Newsflash! Computer science majors rise and fall as starting salaries rise and fall.

If you want to see more majors, raise your starting salaries. Stop firing everyone and outsourcing to India.

The overall unemployment rate among PhDs in computer science is shockingly low. Per the current Taulbee Survey (see pdf here), unemployment among fresh CS PhD graduates from surveyed institutions (266 North American ones; likely comprising the whole top 100 institutions plus 166 others) is .8%. .8% is well below the frictional unemployment rate; a PhD in CS is almost as good as a civil-service union government job in guaranteeing employment for life.

So ask yourself, what are you totally screwing up. Some previous posters have suggested that perhaps you're shooting way too low (intro programming job) for your talents. This could be the case. It could be that your degree is from a less-than-reputable institution (you didn't say, so we can't comment). You may just be messing up the basics of interviewing --- my PhD prepared me for an academic interview, but not so much for a straight industry job. Asking help from your institution's career services department on interviewing skills could help.

Regardless, there are very well collected statistics that reflect that a CS PhD is a strong benefit to gaining employment; don't blame the PhD.

What is the percentage of black, women, etc people with the skills and training that google, facebook, etc is looking for?

Black: Blacks make up only 3.6% of CS graduates, 6% of CE graduates, and 7% of generic IT graduates at the moment.
Female: Female CS/CE graduates peaked in the '80s at 37%, and has fallen ever since to a current low of only 12%; the previous link also shows them at about 50% higher rates in generic IT, or 17% total.

Sorry if that doesn't give your axes a nice fine edge, folks, but the likes of Google, Yahoo, and Facebook don't hire only misogynist racists for their HR departments - In fact, all three soundly beat the above graduation rates, making them arguably biased against hiring white males.

66% of Computer Science graduates are white, 15% Asian, 3% black, and 5% Hispanic. I'm surprised they have such a high percentage of Asian workers. Of course 60% of students graduating with master's degrees in computer science aren't Americans so maybe that's where they are coming from. Also 80% of Computer Science graduates are male and 20% are female, so it's not surprising that tech companies have primarily male workers.

http://cra.org/uploads/documen...

Here is data for C.S. and C.E. bachelors degree recipients in the U.S. See page 5. About 8.7% of degrees were awarded to blacks and Hispanics, which is about one out of 11. So Silicon Valley isn't far off what one would expect based purely on # of degrees awarded. A significant portion of bay area tech workers are likely immigrants to the United States and got their degrees elsewhere. This group likely contains very few blacks and Hispanics. So, if the discussion were limited to Silicon Valley workers born in the United States the the percentage of blacks and Hispanics may well line up with expectations.

"Yep. That goes a long way towards explaining the complete lack of innovation in the computer industry. Basically nothing has improved or even changed in the last 30 years."

More true than one might think at first: http://developers.slashdot.org/story/13/08/09/1641249/back-to-the-future-of-programming

See also:
"The Real Computer Revolution Hasn't Happened Yet" by Alan Kay
http://www.vpri.org/pdf/m2007007a_revolution.pdf
http://archive.cra.org/Activities/grand.challenges/kay.pdf
http://www.youtube.com/watch?v=oKg1hTOQXoY

Personally, cross-platform reasonable speedy VisualWorks Smalltalk from the 1990s in many ways still has not been surpassed (except in the sense it was not free and open source and somewhat lesser stuff like Python and now Java is). The Newton's 1990s view of a PDA with integrated soups of data is still (in some ways) advanced beyond Android. Or from:
http://inventors.about.com/od/istartinventions/a/internet.htm
"Vannevar Bush first proposed the basics of hypertext in 1945 [in "As We May Think"]. Tim Berners-Lee invented the World Wide Web, HTML (hypertext markup language), HTTP (HyperText Transfer Protocol) and URLs (Universal Resource Locators) in 1990."
Project Xanadu was around in the 1980s doing Hypertext, inspired by Theodore Sturegon's 1950 short story "The Skills of Xanadu".

Don't confuse the eventual implementation of part of old ideas (like Kay's 1970s DynaBook vision being realized in part in today's laptops and smartphones) with the notion of conceptual progress.

Even much of robotics and AI is just old ideas finally being more workable with better hardware.
http://www.transhumanist.com/volume1/moravec.htm
"The stupendous growth and competitiveness of the computer industry is one reason. A less appreciated one is that intelligent machine research did not make steady progress in its first fifty years, it marked time for thirty of them! Though general computer power grew a hundred thousand fold from 1960 to 1990, the computer power available to AI programs barely budged from 1 MIPS during those three decades. "

Still, it is also true there are no doubt many innovations now lurking here or there for which we have not yet hear much of. As WIlliam Gibson said:
http://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly
"The future is already here â" it's just not evenly distributed."

Much of what young kids are interested in is what they have seen in movies, read in stories, or played with in games, and so on. True, they may sometimes put things together in new ways. But its still very often old, old ideas they are working with.

The "Computing Research Association" is a lobbying group. It's not on K Street NW in DC like most lobbyists. It's on L street, one block over. It's a lobby for federal funding for college CS departments.

Here's the actual report. Two charts are upside down. The focus is on race and gender. There's little discussion of CS vs IT vs EE vs CE degrees, although there are some separate table columns. Employment statistics are provided only for PhD graduates.

The data seems to be self-reported by the institutions involved.

"There are more and more [computer science] jobs," says Alexander Repenning, a computer science professor at the University of Colorado Boulder, "but the interest is actually going down, and the interest of women in these kinds of jobs is going down even faster."

That is as far as I got. Every alarm bell in my head was ringing. Quite unpleasant. So I went and found something of possible value: http://www.cra.org/resources/crn-online-view/undergraduate_cs_degree_production_rises_doctoral_production_steady/ . Long read, but take a look at those charts. I think I am willing to step out on a limb and say, "Man people like the idea of getting rich for not much work, and during the dot-com boom CS was the place to pan for gold." I need to find longer term data though. Oh and the claim about women in the above quote is not born out by this data.

It's been nearly 25 years since I taught CS (BYU, 1985-87), and I taught at the peak of CS enrollment, so I had large (200+ students) "Intro to Programming" courses; also, these same classes counted as general education. I'm sure a lot of 'sharing' went on as far as the programming assignments went, but I was never concerned, because (a) that's true in real-life programming as well, and (b) it wasn't going to help them (and actually hurt them) when it came to tests. As a side note, enrollment demand was so high at that time that if you wanted to be a CS major, you had to complete this class, apply to the CS department, and be accepted as a CS major. Ah, those were the days.

The other classes I taught (assembly language, data structures, computer and society) were for CS majors only. The first two required programming, and again I wasn't concerned due to the same programming vs. test performance check. I also wasn't concerned because I knew (from personal experience) how tough the upper-division classes were (compiler design, OS implementation, comparative languages, not to mention the lower-division 'algorithms' class taught using Knuth's "Art of Computer Programming: Fundamental Algorithms"), and I knew that if someone cheated their way through the earlier classes, they would crash and burn eventually. ..bruce..

Also, you're aware that this not some hindsight Bush-bashing here, right? I mean, they actually had Senate hearings on the Tether/DARPA mess back in 2005.

Such a convenient and unjustified answer: "Oh, there are so few women because they don't *want* to do computing! It's just that simple!"

How about some data: Science and engineering degrees granted to women

Female bachelor's degrees in CS peaked in the mid 1980's, and have steadily declined since. Almost every other field has been increasing the number of women who obtain science and engineering degrees, with the notable exception of math (holding steady) and computer science (steadily decreasing).

Nobody claims here that the split *should* be 50% of each gender. However, this data is evidence that the current split of 1:3 or 1:4 isn't natural either, unless you want to claim that women have fundamentally become *less* interesting in computing over the last 25 years. It isn't good enough to state, without justification, "Meh, girls just don't like computing." Not so long ago, more of them did.

I figure since I'm going to be karma burned for what I'm about to say I might as well be upfront on my title.

http://www.cra.org/CRN/articles/may08/taulbee.html
PHD breakdown: http://www.cra.org/CRN/articles/may08/taulbee.html
BS and MS breakdown: http://www.cra.org/CRN/articles/may08/tables9to16.html

I realize we can talk set theory all day, but look at the low numbers of blacks and hispanics graduating. Here are some interesting high points from 2007:

* 430 non-hispanic whites got a phd in CS. Only 20 hispanics and 19 blacks got a phd in CS.
* 1,115 API's got a BS in CS and 5,158 whites got a BS. Only 412 hipsanics and 261 blacks got a BS in Compuer Science.

As a latino with a CS degree, this angers me for a number of reasons.

First and foremost, I don't think it's soley the fault of the "white man". Whites and Asians need to work harder to be more inclusive to minorities. Not by giving them a free pass or admission into college, but by seeking out and mentoring young minority students. Minority students also need to seek out mentors regardless of race or gender. I only had white and asian mentors.

I figure since I'm going to be karma burned for what I'm about to say I might as well be upfront on my title.

http://www.cra.org/CRN/articles/may08/taulbee.html
PHD breakdown: http://www.cra.org/CRN/articles/may08/taulbee.html
BS and MS breakdown: http://www.cra.org/CRN/articles/may08/tables9to16.html

I realize we can talk set theory all day, but look at the low numbers of blacks and hispanics graduating. Here are some interesting high points from 2007:

* 430 non-hispanic whites got a phd in CS. Only 20 hispanics and 19 blacks got a phd in CS.
* 1,115 API's got a BS in CS and 5,158 whites got a BS. Only 412 hipsanics and 261 blacks got a BS in Compuer Science.

As a latino with a CS degree, this angers me for a number of reasons.

First and foremost, I don't think it's soley the fault of the "white man". Whites and Asians need to work harder to be more inclusive to minorities. Not by giving them a free pass or admission into college, but by seeking out and mentoring young minority students. Minority students also need to seek out mentors regardless of race or gender. I only had white and asian mentors.

I figure since I'm going to be karma burned for what I'm about to say I might as well be upfront on my title.

http://www.cra.org/CRN/articles/may08/taulbee.html
PHD breakdown: http://www.cra.org/CRN/articles/may08/taulbee.html
BS and MS breakdown: http://www.cra.org/CRN/articles/may08/tables9to16.html

I realize we can talk set theory all day, but look at the low numbers of blacks and hispanics graduating. Here are some interesting high points from 2007:

* 430 non-hispanic whites got a phd in CS. Only 20 hispanics and 19 blacks got a phd in CS.
* 1,115 API's got a BS in CS and 5,158 whites got a BS. Only 412 hipsanics and 261 blacks got a BS in Compuer Science.

As a latino with a CS degree, this angers me for a number of reasons.

First and foremost, I don't think it's soley the fault of the "white man". Whites and Asians need to work harder to be more inclusive to minorities. Not by giving them a free pass or admission into college, but by seeking out and mentoring young minority students. Minority students also need to seek out mentors regardless of race or gender. I only had white and asian mentors.

As a young professor at a top CS program, I can give a simple reason CS interest in DARPA has waned: because DARPA funding as waned, both in the amount of available grant money and the attractiveness of the terms.

While NSF grants have little oversight, require few deliverables, and have 3-4 year terms, DARPA grants increasingly have 1.5-2 year horizons, require regular reports and site visits, and have go/no-go mid-term decisions. Furthermore, DARPA projects increasingly want deliverables and seek classification. Thus, while NSF still allows you to engage in more blue-sky, high-risk research, DARPA is interested in advanced development. Not quite the thing academics and grad students signed up for. No surprise most DARPA funding has switched from universities to contractors.

Most academics I know would love to return to the DARPA gravy-train of pre-Tony Tether days; the funding terms and dollar amounts just aren't there currently.

This CRA post summarized it well:

    http://www.cra.org/govaffairs/blog/archives/000624.html

http://www.cra.org/wp/index.php?p=139

Switch companies. 53k is peanuts for salaried programming, that's an average starting salary for straight out of college and anybody with experience should be able to do better. One quickly googled source: http://www.cra.org/wp/index.php?p=127 . My experience, companies talk $75k to $100k; you can probably swing more if you code on 6 month contracts which is easily done if you find a headhunter and work on your resume.

Anon because some people look down upon talking $s.

Funding for (computer) science research also got the shaft this year, in the budget for FY 2008, despite a prior commitment to double the budget over the next 10 years.

USACM has a nice perspective: http://usacm.acm.org/usacm/weblog/index.php?p=558 and so does the Computing Research Association: http://www.cra.org/govaffairs/blog/archives/000646.html

Unfortunately, pork $$$ in the near-term wins over long-term benefits for the entire country...

Computer science/engineering in particular have made very little progress over the last decade; most of what is touted as progress now is research results from the 70's and 80's finally being implemented, combined with faster machines. That has had a lot of impact on daily life, but research has stagnated.

True, but the way the implementation is going, and the dissemination of technology know-how is what I'm talking about. The ease with which I can cobble-together pieces of software technology is truly amazing. And this is rapidly getting out into the hands of ordinary people. It's this revolution in implementation I am talking about. And as for research stagnating, I doubt that Google has caused that. Also, I would look outside of academia for what's coming down the pike. (This is also a part of the history of Computer Science.)

It takes time for technological revolutions to be fully absorbed into society, and for their full potential to be developed.

http://www.cra.org/Activities/grand.challenges/kay.pdf
http://video.google.com/videoplay?docid=-2950949730059754521

I think many the revolutions to come will be along the lines of human/computer interface.

As for biology, there has been an explosion of new data, but little in terms of fundamentally new understanding.

Again, the revolution here is in implementation. Not only has there been an explosion of new data, but an explosion in the tools that can produce the new data. But you're right, there needs to be some integration now.

I see a few problems with this notion. First of all, when there's a barrier to entry into a specific field, that barrier tends to create a protracted bell curve in the minority which is confronted with the barrier. In other words, assume you have 50 men and 50 women who want to go into IT. 40 men make it and only 10 women make it, due to discrimination. The average ability of those 10 women will be greater than the average ability of those 40 men, because the lower outliers are more likely to have been eliminated from the female camp. However, if this trend is not present it likely indicates either self-selection or simply less of a needed qualification in the smaller camp. Confirming that this is self-selection rather than industry bias is the dramatic drop in number of female students majoring in CS at UCLA. At the height of the tech boom, nearly 3.75% of women and 6.5% of men said they were majoring in CS. Today, about .3% of women and slightly less than 3% of men say they're majoring in CS. These facts essentially show the OP's narrative to be garbage. source

Unless, of course, you find literature or Poli Sci cool. In that case, it's time to start thinking about grad school. Here's a hint, though: unless you're a bright enough prospect that you get a funded free ride with stipend through grad school, you will not earn any money with your PhD or MA.

http://www.cra.org/Activities/craw/

I heard CMU's numbers were on the decline? Is that incorrect?

I think the numbers quoted from the article here were bungled.

> having hired 700 interns worldwide this year including
> 250 computer science PhD candidates in Redmond alone,
> which is roughly 21% of all the computer science PhD
> candidates in the United States."

http://www.cra.org/CRN/articles/may06/taulbee.html

suggests around 1200 CS PhDs *awarded* in 2004-2005 in the USA and Canada. The number for the USA alone may be lower than this, but it might also be higher since 20% of departments surveyed did not respond. But assuming 1200/year is close to the mark, the number of "computer science PhD candidates in the United States" must be several times that, since a PhD takes several years and furthermore a lot of PhD students never complete their degree. I think an average of five years of studentship per PhD awarded would be a reasonably conservative estimate; then the 21% number quoted should be more like 4%.

The 1000 number represents Computer Science PhD degrees granted per year in the United States. At any given time, the number of PhD students is substantially higher than that. See the graph here:
http://www.cra.org/info/education/us/phd.html

> If you really think the majority of professors make $200k, you're nuts. At the school where I work, incoming assistant profs make ~$40k, associate profs with tenure about $55k and the full professors clear about $90k.

Clearly it depends on the school and the professor's field, but those numbers are way low for computer science.

Check out the Taulbee Survey. Scroll down to Table 34, examine the median and mean for tenure track salaries, and take note of the fact that that's a 9-month salary for someone who just put their foot on the stair.

Actually, if you look at the long-term trend, the dot-com era was an anomaly that caused a temporary upswing in CS majors. The number of CS graduates started declining in 1987, which means enrollments started to decline back in 1983.

From what I heard the NSF 'increase' reinstates funding that was 'cut' last year to pay for Katrina costs.

http://www.cra.org/CRN/articles/jan06/harsha.html

Not everyone who choses comp sci or some other "geeky" degree is automatically a geek. A lot of people are just in it for the money. If you look at the graph in the one linked article, there are two spikes -- the first one starting in the late 70's and early 80's and peaking in 83-84, which corresponds with the rise and fall of the 8-bit personal computer era; and the second one centered around the internet bubble. When computers were percieved as being a cool and/or profitable career in mainstream culture, a lot of people gravitated for it for the status and/or the money, not because they were computer geeks. When the bubble bursts and computers fall out of the spotlight, the trend-followers leave for greener pastures.

More to the point: given that the vast majority of PhDs must go into industry (since the universe conserves tenure) what, praytell, are you suggesting a CS PhD do out there in the real world?

The full Senate has not yet considered the CJS Appropriations.

I posted this elsewhere on the thread before seeing your post:

They marked it up in subcommittee yesterday (here's a brief report.), but it hasn't been considered by the full Appropriations committee yet. That's tomorrow. Then it will be some time before it finds its way to the Senate floor.

You can track the progress using this page from Thomas.

The bill referenced in the article is the House version of the bill....

They marked it up in subcommittee yesterday (here's a brief report), but it hasn't been considered by the full Appropriations committee yet. That's tomorrow. Then it will be some time before it finds its way to the Senate floor.

You can track the progress using this page from Thomas.

The bill referenced in the article is the House version of the bill....

You simply can't take statistics from one university and assume that they're not indicative of a universal trend either. I teach computer engineering at a major public university in the midwestern US, and we are seeing trends exactly like UCLA. If you follow the link in TFA to the Taulbee survey, which encompasses all of North America, you'll see that the data there is consistent with UCLA's findings.

Except the services we sell to all the other countries who have no clue how to efficiently produce their goods., build their power plants, feed their ever growing populations, and cure their sick.

And once they learn how to do all that stuff, what will they need us for? Or do you think they'll never catch up?

We currently have the best university system available

That depends on government funding for research, funding which is being cut across the board left and right these days. DARPA, NSF, etc, are all cutting funding, especially for pure university-based research which is the most crucial in maintaining America's long-term technological leadership, academic quality, and even tax base that is required for additional funding. Without pure research, technological advancement and the steady stream of neato gadgets we take for granted will dry up.

and that translates into the best educated country in the world.

Sure, that's why American students are always at the top of every published academic ranking and consistently win international contests. I won't bother to link to the recent /. stories on this.

Which translates into valuable services.

An economy can't survive on services alone. There is only one way of creating wealth, and that is by taking raw materials and applying work and ingenuity to turn them into something worth more than the sum of their parts. We used to do take wood and iron and turn it into ships and trains; now we take sand, aluminum, and copper and turn it into microchips. Voila, wealth is created. At best services allow you to ween a little more value out of the products you've created, especially if you see custom software (eg IT consulting) as an enabler of hardware, or something that helps you get more value out of your hardware. At worst, services are simply a wealth transfer, with no additional wealth created at all.

Don't buy into the malarky that America can prosper as we have without actually making anything. As funding is diverted from pure research to military expeditions and whatnot we undermine our base of future product innovation and development, while China learns our manufacturing techniques through outsourcing and educates hundreds of thousands of engineers and scientists in our universities, who are capable of bringing their education, research, and innovativeness home and away from the US.

As American CEO's sometimes cannibalize their companies' future for immediate stock price gains and golden parachutes, so our recent presidents, CEO's, and financiers seem to be doing to our entire country.

For some real numbers, check out the following:

• http://www.anitaborg.org/faq.html
• http://www.cra.org/statistics/trends/Gender/bachpe rc.html
• http://cs.smith.edu/~orourke/fstats.html

• What makes for a good advisor/program/topic
  • There is a famous fable, which states that choice of advisor is more important than choice of topic. While this may be overstated, good advisors have a sense of what is interesting and provide interesting directions. However, be wary of working with really big names, often they are very hard on their students. Try to determine how they treat their students and what sort of time frame and rate do they graduate at.
  • Getting the right advisor is more important than going to a fancy school. E.g. if say, Don Knuth came out of retirement and went to teach at some relatively unknown University with a new Ph.D. program, his recommendation would still carry significant weight. However, good programs tend to have more good people (which is why they are good) and a larger program can carry on more ambitous research projects.
  • Before going to grad school, try to pick one or 2 areas to focus on and target those areas. If you like say Data Mining, read the recent conferences and see who is doing interesting work. Often a few good people will be at the same institution with a small focus group working on a particular problem.
• What the student should be trying to do
  • Learn to finish - you must also learn to say no to projects that distract you from your goals. Pick a project and stick with it. Students who drift between projects often start many and finish none. If you have trouble finishing projects a Ph.D. is not for you.
  • Familiarize yourself with the literature - Read the major conference articles. You can't possibly read everything however, that will paralyze you. Pick a sub topic and survey it.
  • Keep your research active - many students and faculty get paralyzed because no project seems good enough, so they pick some hard open ended problem and get stuck in a "tar pit". Being deep doesn't mean being narrow.
  • Try to do work that gets cited. Writing a lot of papers is important, but being read and known in the community is important.
  • Go to conferences - try to go to one early in your academic program (before you even publish) to see what the leaders in your discipline are doing and to get a sense of the currently interesting research directions. You can pick a hard topic that seems important, but it helps if others agree that it is important.
• Hiring Related Timing matters when searching for a job, especially at the Ph.D. level. A Ph.D. can be thought of as a certification, sort of like a driver's license, it doesn't mean you are Mario Andretti, nor does it mean that people lacking the certification are incompetent. Most Ph.D.s are expected to specialize and extend the state of the art. A Ph.D. in a theoretical topic can generally expect greater difficulty in finding a good position (unless they do landmark work) while a hands on type may fare better. If you are in Computer Science, you would be well advised to look at the Taulbee survey (see the CRA website for this an more information), which gives an annual salary survey and dicsusses the outlook for Ph.D. placement. When I started (early 1990's) the outlook was quite poor, and I went against the grain. I was lucky that I got out at a good (nearly optimal) time.

The poster suggests creation of a group of women who are already interested in CS.

CS is not the same as IT.

Natural-science and engineering fields (a category which for some reason includes CS and math, despite the fact that neither is more "concrete" than philosophy) tend to be male-dominated environments, often to (and past) the point of creating an uncomfortable environment for other folks, even when such folks might otherwise be interested in the subject at hand.

Related, some people in our society are socialized in a way that rewards being good at math and math-like things. Some are not.

Often, people of different genders perceive the world generally. To answer your question directly: if we have more people of different genders (and other sorts of different backgrounds), they are likely to be able to bring different experiences and analyses to the field. This creates a richer environment for all. example

hm, interesting.

• A Ph.D. is hard work, often times it is lonely and psychologically difficult. You'll need some real motivation to make it work. Even if you want to teach, you'll need a Ph.D. to get in.
  
• Not all Ph.D.'s are equal. The primary measures of the quality of a Ph.D. employed at hiring time are number and visibility of refereed publications and the reputation of the advisor.
  Sure good people tend to be in good depts. and at good schools, but it is the advisor who matters.
  
• Most schools have demanding teaching/research loads, expressed as X/Y where X is the number of courses offered in the fall semester, Y is the number of courses taught in the spring, if X != Y, then depending on the department's needs, the order may be swapped. Top 4 year colleges and some places offering masters degrees (but not Ph.D.s) will have a 2/2 teaching load. Top research schools will often have a 1/1 teaching load. Most teaching schools are either 2/3 or more commonly 3/3. Many second tier and lower schools with Ph.D. programs have 1/2 loads.
  
• Schools offering M.S. and Ph.D. courses often provide teaching assistants and graders.
  
• Teaching schools often have high loads but expect one paper a year or so from you before tenure. Research schools expect more, and often require some grant.
  
• The academic market for Ph.D.'s in computer science has large fluctuations. A Ph.D. takes 5 years on average (mine took longer :-(). It is very hard to predict the market that far into advance. Please check out the Taulbee Survey,
  and other information at CRA. Check out some of the old ones about 10 years or so ago, you'll see they aren't so encouraging.
  
• If you want to be an academic, you better love teaching. Many people who think they like it imagine that all students are motivated, skilled and prepared. Dealing with weak students is not fun, and telling students that they are not good at computing and that they should consider another career is a real problem.
  People would never think that they should be brain surgeons without skill, preparation or background, yet somehow they think they are entitled to money.
  
• Faculty at many institutions are divisive and back stabbing. If jobs are tight, you may wind up in such a place.
  
• Grant agencies are old boy networks. If you don't have the right pedigree/connections, you won't see dime one of funding. Good luck having tenure at a research univeristy if you can't break in.
  
• If you like theory, don't bother unless you are EXTREMELY GIFTED, since theory people have an extremely hard time getting funded. You better be number 1 in a hot theory area to get funded. And even theoreticians these days (at least in the U.S.) must implement to survive.
  
• It is a good idea to be focusing on a popular (that is an area that the research community is currently focusing on) at the time of graduation.
  If you are in an obscure area or are doing interdisciplinary work, it is much harder to get funded. Interdisciplinary projects are best headed by pairs of specialists in each area rather than having people who try to specialize in both areas.
  
• Teaching (even good students) is hard work.
  

I put together a list of key resources in the history of computing for a recent NSF backed workshop on using history to teach computer science better . It has books as well as some links to history sites and other resources. People interested in this topic might also want to check out the site for my computer history and culture course at Colby College -- the pages for each session include additional links and readings." Hackers was one of the main texts -- it's a great book, but more recent. (I posted something anonymously -- sorry to duplicate. I got myself an account now).

I put together a list of key resources in the history of computing for a recent NSF backed workshop on using history to teach computer science better . It has books as well as some links to history sites and other resources.

The Campbell-Kelly and Aspray book is defintely the best overall history, and I use it in my own courses. Another overall history, stronger on Unix and minicomputers, is Paul Ceruzzi's "A History of Modern Computing." If he's interested in the early Univacs, then the detailed book to get is "A Few Good Men from Univac" by David E. Lundstrom -- out of print, but Amazon has it used. There's a ton of books on IBM, but for a good mix of technical and business, he might like "Building IBM: Shaping an Industry and Its Technology" by Emerson W Pugh.

Thomas Haigh

• The Top Four really are superb, but the people who say "choose based on field" are also right. If any of CMU, MIT, Berkeley, or Stanford cover your field and you can get in, move heaven and earth to go to one of them.
• If the Top Four don't take you, try for a school that has several people working in the field you're interested in. Check out their recent publications and see if they're cool; if possible talk to them to find out whether you like them or they're assholes. Talk to their current grad students. Try for a school that has a reputation (easy measure: they consistently get papers into the top conference in the field). Avoid a school that has only one prof in your field: if you hate him/her, or if the research s/he's doing two years from now isn't fun, you're screwed.
• An experienced, understanding advisor at your current school is invaluable.
• Know thyself. Why do you want a graduate degree? The person who said "go straight to work" was partly right. An MS is quick and easy to get, and it will pay off in industry (lots of people are impressed). A Ph.D. is a very specialized degree, and not tremendously useful unless you want to go into research or academia. (Exception: in some consulting positions the prestige factor helps.)
• It is very hard for most people to return to school after time outside. I'm not talking about forgetting how to study, I'm talking about having a life, kids, and car payments. Most people never try, and of those who try, most never finish.
• A Master's usually takes two years. I did it in one under abnormal circumstances; I know a guy who took eleven (full-time!). A Ph.D. in CS usually takes from 4 to 7 years depending on the school and advisor. I know of a guy who did it in 3 (and regrets going so fast) and one who took 13.
• When you're looking for a job after getting a Ph.D., many things matter. Some of the important ones are the quality of your dissertation, the number of publications you have, the name of your school, the names of your references, and the content of your reference letters. All of those are affected to some degree by your choice of school. Employers also care about you, of course (make sure your interview is great!), but the above items are harder to fix late in the game.
• Early in your graduate career, it can be good to do internships at industrial research labs. This approach gives you good dissertation ideas, and also gives you a wider base to draw on when it's time to get reference letters.

Enough random advice. Here are some books and URLs:

• Tomorrow's Professor: Preparing for Academic Careers in Science and Engineering, by Richard M. Reis. This book contains absolutely essential advice, starting with how to pick a graduate school and ending with advice on surviving your first year as a professor. If you are thinking about grad school, or in it, this book is a MUST! I only wish it had been written before the last year of my doctorate. Even so, it made a huge difference in my eventual success. I owe my current job to many people, but Dr. Reis is unquestionably one of them.
• A Ph.D. is Not Enough: A Guide to Survival in Science, by Peter J. Feibelman. This book has some very realistic, sometimes cynical advice for prospective scientists.
• How to be a Professor: Some Good Books is a Web site devoted to helping people adjust to academia. For a prospective grad student, it can also serve as an introduction to what to expect.
• Rank PhD Programs in Computer Science from CRA gives graduate-program rankings, though they're somewhat dated. (Take all rankings with a grain of salt, though.) The Computing Research Assocation is a useful resource in general (check out their salary survey).
• The US News rankings are also useful.

As usual, I've run on and on, so I'll close with a wish for your success and one last thought: grad school was the most fun thing I ever did!

• The Top Four really are superb, but the people who say "choose based on field" are also right. If any of CMU, MIT, Berkeley, or Stanford cover your field and you can get in, move heaven and earth to go to one of them.
• If the Top Four don't take you, try for a school that has several people working in the field you're interested in. Check out their recent publications and see if they're cool; if possible talk to them to find out whether you like them or they're assholes. Talk to their current grad students. Try for a school that has a reputation (easy measure: they consistently get papers into the top conference in the field). Avoid a school that has only one prof in your field: if you hate him/her, or if the research s/he's doing two years from now isn't fun, you're screwed.
• An experienced, understanding advisor at your current school is invaluable.
• Know thyself. Why do you want a graduate degree? The person who said "go straight to work" was partly right. An MS is quick and easy to get, and it will pay off in industry (lots of people are impressed). A Ph.D. is a very specialized degree, and not tremendously useful unless you want to go into research or academia. (Exception: in some consulting positions the prestige factor helps.)
• It is very hard for most people to return to school after time outside. I'm not talking about forgetting how to study, I'm talking about having a life, kids, and car payments. Most people never try, and of those who try, most never finish.
• A Master's usually takes two years. I did it in one under abnormal circumstances; I know a guy who took eleven (full-time!). A Ph.D. in CS usually takes from 4 to 7 years depending on the school and advisor. I know of a guy who did it in 3 (and regrets going so fast) and one who took 13.
• When you're looking for a job after getting a Ph.D., many things matter. Some of the important ones are the quality of your dissertation, the number of publications you have, the name of your school, the names of your references, and the content of your reference letters. All of those are affected to some degree by your choice of school. Employers also care about you, of course (make sure your interview is great!), but the above items are harder to fix late in the game.
• Early in your graduate career, it can be good to do internships at industrial research labs. This approach gives you good dissertation ideas, and also gives you a wider base to draw on when it's time to get reference letters.

Enough random advice. Here are some books and URLs:

• Tomorrow's Professor: Preparing for Academic Careers in Science and Engineering, by Richard M. Reis. This book contains absolutely essential advice, starting with how to pick a graduate school and ending with advice on surviving your first year as a professor. If you are thinking about grad school, or in it, this book is a MUST! I only wish it had been written before the last year of my doctorate. Even so, it made a huge difference in my eventual success. I owe my current job to many people, but Dr. Reis is unquestionably one of them.
• A Ph.D. is Not Enough: A Guide to Survival in Science, by Peter J. Feibelman. This book has some very realistic, sometimes cynical advice for prospective scientists.
• How to be a Professor: Some Good Books is a Web site devoted to helping people adjust to academia. For a prospective grad student, it can also serve as an introduction to what to expect.
• Rank PhD Programs in Computer Science from CRA gives graduate-program rankings, though they're somewhat dated. (Take all rankings with a grain of salt, though.) The Computing Research Assocation is a useful resource in general (check out their salary survey).
• The US News rankings are also useful.

As usual, I've run on and on, so I'll close with a wish for your success and one last thought: grad school was the most fun thing I ever did!

It is hard to believe, especially since the 1999 Taulbee Survey shows how enrollement doubled between 1995 and 1997, staying put for 1998. Perhaps they are taking longer to graduate?