I’ve got a note in my calendar around the beginning of August—I was presumably in a really bad mood at [at least] some point over the past year—to retweet a link to my blog post discussing my fondness for math camps—not!—but in the hazy-lazy-crazy days of summer, I’m realizing this would be rather like sending Donald Trump to meet with the leaders of U.S. allies: gratuitously cruel and largely meaningless. Instead, and more productively, an article in Science brought to my attention a recent report  by the U.S. National Academies of Sciences, Engineering, and Medicine (NASEM)—these people, please note, are a really big deal. The title of the article—”Student-centered, modernized graduate STEM education”—provides the gist but here’s a bit more detail from the summary of the report provided in the Science article:
[the report] lays out a vision of an ideal modern graduate education in any STEM field and a comprehensive plan to achieve that vision. The report emphasizes core competencies that all students should acquire, a rebalancing of incentives to better reward faculty teaching and mentoring of students, increased empowerment of graduate students, and the need for the system to better monitor and adapt to changing conditions over time. … [in most institutions] graduate students are still too often seen as being primarily sources of inexpensive skilled labor for teaching undergraduates and for performing research. … [and while] most students now pursue nonacademic careers, many institutions train them, basically, in the same way that they have for 100 years, to become academic researchers
Wow: reconfigure graduate programs not only for the 21st century but to benefit the students rather than the institutions. What…a…concept!
At this point my readership now splits, those who have never been graduate students (a fairly small minority, I’m guessing) saying “What?!? Do you mean graduate programs aren’t run for the benefit of their students???” while everyone who has done time in graduate school is rolling their eyes and cynically saying “Yeah, right…” With the remainder rolling on the ground in uncontrollable hysterical laughter.
But purely for the sake of argument, and because these are the lazy-hazy-crazy days of summer, and PolMeth is this week and I got my [application-focused!] paper finished on Friday (!!), let’s just play this out for a bit, at least as it applies to political methodology, the NAESM report being focused on STEM, and political methodology is most decidedly STEM. And in particular, given the continued abysmal—and worsening —record for placement into tenure-track jobs in political science, let’s speculate for a bit what a teaching-centered graduate level program for methodologists, a.k.a. data scientists, intending to work outside of academia might look like. For once, I will return to my old framework of seven primary points:
1. It will basically look like a political methodology program
I wrote extensively on this topic about a year ago, taking as my starting point that experience in analyzing the heterogeneous and thoroughly sucky sorts of data quantitative political scientists routinely confront is absolutely ideal training for private sector “data science.” The only new observation I’d add, having sat through demonstrations of several absolutely horrible data “dashboards” in recent months, is formal training in UX—user interface/experience—in addition to the data visualization component. So while allowing some specialization, we’d basically want a program evenly split between the four skill domains of a data scientist:
- computer programming and data wrangling
- machine learning
- data visualization and UX
2. Sophisticated problem-based approaches taught by instructors fully committed to teaching
One of the reasons I decided to leave academia was my increasing exposure to really good teaching methodologies combined with a realization that I had neither the time, energy, nor inclination to use these. “Sage on the stage” doesn’t cut it anymore, particularly in STEM.
Indeed, I’m too decrepit to do this sort of thing—leave me alone and just let me code (and, well, blog: I see from WordPress this is published blog post #50!)—but there are plenty of people who can enthusiastically do it and do it very well. The problem, as the NASEM report notes in some detail, is that in most graduate programs there are few if any rewards for doing so. But that’s an institutional issue, not an issue of the total lack of humans capable of doing the task, nor the absence of a reasonably decent body of research and best-practices—if periodically susceptible, like most everything social, to fads—on how to do it.
3. Real world problems solved using remote teaming
Toy problems and standardized data sets are fine for [some] instruction and [some] incremental journal publications, but if you want training applicable to the private sector, you need to be working with raw data that is [mostly] complete crap, digital offal requiring hours of tedious prep work before you can start applying glitzy new methods to it. Because that, buckeroos, is what data science in the private sector involves itself with, and that’s what pays the bills. Complete crap is, however, fairly difficult to simulate, so much better to find some real problems where you’ve got access to the raw data: associations with companies—the sorts of arrangements that are routine in engineering programs—will presumably help here, and as I’ve noted before, “data science” is really a form of engineering, not science.
My relatively new suggestion is for these programs to establish links so that problem-solving can be done in teams working remotely. Attractive as the graduate student bullpen experience may be, it isn’t available once you leave a graduate program, and increasingly, it will not be duplicated in many of the best jobs that are available, as these are now done using temporary geographically decentralized teams. So get students accustomed to working with individuals they’ve never met in person who are a thousand or eight thousand or twelve thousand miles away and have funny accents and the video conferencing doesn’t always work but who nonetheless can be really effective partners. In the absence of some dramatic change in the economics and culture of data science, the future is going to look like the “fully-distributed team” approach of parse.ly , not the corporate headquarters gigantism of FAANG.
4. One or two courses on basic business skills
I’ve written a number of blog entries on the basics of self-employment—see here and here and here—and for more information, read everything Paul Graham has ever written, and more prosaically, my neighbor and tech recruiter Ron Duplain always has a lot of smart stuff to say, but I’ll briefly reiterate a couple of core points here.
Outside of MBA programs—which of course go to the opposite extreme—academic programs tend to treat anything related to business—beyond, of course, reconfiguring their curricula to satisfy the funding agendas of right-wing billionaires—as suspect at best and more generally utterly worthy of contempt. Practical knowledge of business methods also varies widely within academia: while the stereotype of the academic coddled by a dissertation-to-retirement bureaucracy handling their every need is undoubtedly true as the median case, I’ve known more than a few academics who are, effectively, running companies—they generally call them “labs”—of sometimes quite significant size.
You can pick up relevant business training—well, sort of—from selectively reading books and magazine articles but, as with computer programming, I suspect there are advantages to doing this systematically [and some of my friends who are accountants would definitely prefer if more people learned business methods more systematically]. And my pet peeve, of course, is getting people away from the expectations of the pervasive “start-up porn”: if you are reasonably sane, your objective should be not to create a “unicorn” but rather a stable and sustainable business (or set of business relationships) where you are compensated at roughly the level of your marginal economic contribution to the enterprise.
That said, the business angle in data analytics is at present a rapidly moving target as the the transition to the predominance of remote work—or if you prefer, “gig-economy”—plays out. In the past couple of weeks, there were articles on this transition in both The Economist’s “The World If…” feature and Science magazine’s “Science Careers” [6 July 2018]. But as The Economist makes clear, we’re not there yet, and things could play out in a number of different ways. Still, it is likely that most people in the software development and data analytics fields should probably at least plan for the contingency they will not be spending their careers as coddled corporate drones and instead will find themselves in one of those “you only eat what you—or you and your ten-person foraging party of equals—kill” environments. Where some of us thrive. Grrrrrrrr. There are probably some great market niches for programs that can figure out what needs to be covered here and how to effectively teach it.
5. Publication only in open-access, contemporaneous venues
Not paywalled journals. Particularly not paywalled journals with three to five year publication lags. As I note in one of several snarky asides in my PolMeth XXXV paper
Paywalled journals are virtually inaccessible outside universities so by publishing in these venues you might as well be burying your intellectual efforts beneath a glowing pile of nuclear waste somewhere in Antarctica. [italics in original]
Ideally, if a few of these student-centered programs get going, some university-sponsored open access servers could be established to get around the current proliferation of bogus open access sites: this is certainly going to happen sooner or later, so let’s try “sooner.” Bonus points: such papers can only be written using materials available from open access sources, since the minute you lose your university computer account, that’s the world you will live in.
It goes without saying that students in these programs should establish a track record of both individual and collective code on GitHub. GitHub (and StackOverflow) having already solved the open access collective action problem in the software domain.
6. Yes, you can still use these students as GTAs and GRAs provided you compensate them fairly
Okay, I was in academia long enough to understand the basic business model of generating large amounts of tuition credit hours—typically about half—in massive introductory classes staffed largely by graduate students. I was also in academia long enough to know that graduate training is not required for students to be able to competently handle that material: You just need smart people (the material, remember, is introductory) and, ideally, some training and supervision/feedback on teaching methods. To the extent that student-centered graduate programs have at least some faculty strongly committed to teaching rather than increasing the revenues of predatory publishers you may find MA-level students are actually better GTAs than research-oriented PhD students.
As far providing GRAs, my guess is that generating basic research—open access, please—out of such programs will also occur naturally and again, with because the programs have a focus on applications these students may prove better (or at least, less distracted) than those focused on the desperate—and in political science, for three-quarters, inevitably futile—quest for a tenure-track position. You might even be able to get them to document their code!
In either role, however, please provide those students with full tuition, a living wage and decent benefits, eh? The first law of parasitism being, of course, “don’t kill the host.” If that doesn’t scare you, perhaps the law of karma will.
7. Open, transparent, unambiguous, and externally audited outcomes assessments
Face it, consumers have more reliable information on the contents of a $1.48 can of cat food than they have on the outcomes of $100,000 business and law school programs, and the information on professional programs is usually far better than the information on almost all graduate programs in the social sciences. In a student-centered program, that has to change, lest we find, well, programs oriented towards training for jobs that only a quarter of their graduates have any chance of getting.
In addition to figuring out standards and establishing record-keeping norms, making such information available is going to require quite the sea change in attitudes, and thus far deans, associate deans, assistant deans, deanlets, and deanlings have successfully resisted open accountability by using their cartel powers. In an ideal world, however, one would think that market mechanisms would favor a set of programs with transparent and reliable accountability.
Well, a guy can dream, eh?
See y’all—well, some subset of y’all—in Provo.
1. Paywalled, of course. Because elite not-for-profit organizations sustained almost entirely by a combination of tax monies and grants from sources who are themselves tax-exempt couldn’t possibly be expected to make their work accessible, particularly since the marginal cost of doing so is roughly zero.
2. What’s that old joke from the experimental sciences?: if you’re embarking on some procedure with potentially painful consequences, better to use graduate students rather than laboratory rats because people are less likely to be emotionally attached to graduate students.
3. The record for tenure track placement has gotten even worse, down to 26.3%, which the APSA notes “is the lowest reported figure since systematic observation began in the 2009-2010 academic year.”
4. Or if you want to try for the unicorn startup—which is to say, you are a white male from one of a half-dozen elite universities—you at least understand what you are getting into, along with the probabilities of success—which make the odds of a tenure-track job in political science look golden in comparison—and the actual consequences, in particular the tax consequences, of failure. If you are not a white male from one of a half-dozen elite universities, don’t even think about it.
5. Science would do well to hire a few remote workers to get their web page functioning again, as I’m finding it all but inoperable at the moment. Science is probably spending a bit too much of their efforts breathlessly documenting a project which using a mere 1000 co-authors has detected a single 4-billion-year-old neutrino.
6. And for what it’s worth, this is a place where Brett Kavanaugh could be writing a lot of important opinions. Like maybe decisions which result in throwing out the vast kruft of gratuitous licensing requirements that have accumulated—disproportionately in GOP-controlled states—solely for the benefit of generally bogus occupational schools.
7. And recently received a mere $7.5-billion from Microsoft for their troubles: damn hippies and open source, never’ll amount to anything!
8. Though speaking of cartels—and graduate higher education puts OPEC, though not the American Medical Association, to shame on this dimension—the whole point of a cartel is to restrict supply. So a properly functioning cartel should not find itself in a position of over-producing by a factor of three (2015-2016 APSA placements) or four (2016-2017 placements). Oh…principal-agent problems…yeah, that…never mind…
9. Watch the presentation, but for a quick summary, her main point is that the increasingly popular notion that a successful company has to be large, loss-making, and massively funded is bullshit: if you actually know what you are doing, and are producing something people want to buy, you can be self-financing and profitable pretty much from the get-go. “Winner-take-all” markets are only a small part of the available opportunities—though you wouldn’t know that from the emphasis on network effects and FOMO in start-up porn, now amplified by the suckers  who pursue the opportunities in data science tournaments rather than the discipline of real markets—and there are plenty of possibilities out there for small, complementary teams who create well-designed, right-sized software for markets they understand. Thanks to Andy Halterman for the pointer.
10. Okay, “suckers” is probably too strong a word: more likely these are mostly people—okay, bros—who already have the luxury of an elite background and an ample safety net provided by daddy’s and mommy’s upper 1% income and social networks so they can afford to blow off a couple years doing tournaments just for the experience. But compare, e.g. to Steve Wozniak and Steven Jobs—and to a large extent, even with their top-1% backgrounds, Bill Gates and Paul Allen—who created things people actually wanted to buy, not just burning through billions to manipulate markets (Uber, and increasingly it appears, Tesla)