Greater data science, part 2.1 – software engineering for scientists

This is part of an open-ended series of marginalia to Donoho’s 50 Years of Data Science 2015 paper.

In many scientific labs, the skills and knowledge required for the research (e.g. linguistics fieldwork, sociological interview practices, wet-lab biological analysis) are not the same skills involved in software engineering or in data curation and maintenance.

Some scientists thus find themselves as the “accidental techie” in their local lab — maybe not even as the “accidental data scientist“, but doing specific software engineering tasks — you’re the poor schmuck stuck making sure that everybody’s spreadsheets validate, that nobody sorts the data on the wrong field, that you removed the “track changes” history the proposal before you sent it off to the grant agencies, etc.

Scientific labs of any scale (including academic labs, though they probably don’t have the budgets or the incentives) can really benefit from data science, but especially software engineering expertise, even — or perhaps especially — when the engineer isn’t an inside-baseball expert in the research domain.  I list below a number of places an experienced software engineer (or data scientist) can make a difference to a field she (or he) doesn’t know well.

Read the rest of this entry »

Mirrored from Trochaisms.


spelling be hard

I’ve written a half dozen pieces of commentary on David Donoho’s work, all the while spelling his name wrong; at least once in a permalink URL. Oh, well.  At least I can edit the posts here.

Mirrored from Trochaisms.


Greater data science, part 2: data science for scientists

This is part of an open-ended series of marginalia to Donoho’s 50 Years of Data Science 2015 paper.

Many aspects of Donoho’s 2015 “greater data science” can support scientists of other stripes — and not just because “data scientist is like food cook” — if data science is a thing after all, then it has specific expertise that applies to shared problems across domains. I have been thinking a lot about how the outsider-ish nature of the “data science” can provide supporting analysis in a specific domain-tied (“wet-lab”) science.

This is not to dismiss the data science that’s already happening in the wet-lab — but to acknowledge that the expertise of the data scientist is often complementary to the domain expertise of her wet-lab colleague.

Here I lay out three classes of skills that I’ve seen in “data scientists” (more rarely, but still sometimes, in software engineers, or in target-domain experts: these people might be called the “accidental data scientists”, if it’s not circular).

“Direct” data science

Donoho 2015 includes six divisions of “greater data science”:

The activities of Greater Data Science are classified into 6 divisions: 1. Data Exploration and Preparation 2. Data Representation and Transformation 3. Computing with Data 4. Data Modeling 5. Data Visualization and Presentation 6. Science about Data Science

Greater Data Science is all opportunities to help out “other” sciences.

  • methodological review on data collection and transformation
  • representational review ensuring that — where possible — the best standards for data representation are available; this is a sort of future-proofing and also feeds into cross-methodological analyses (below)
  • statistical methods review on core and peripheral models and analyses
  • visualization and presentation design and review, to support exploration of input data and post-analysis data
  • cross-methodological analyses are much easier to adapt when data representations and transformations conform to agreed-upon standards

Coping with “big” data

  • adaptation of methods for large-scale data cross-cuts most of the above — understanding how to adapt analytic methods to “embarrassingly parallel” architectures
  • refusing to adapt methods for large-scale data when, for example, the data really aren’t as large as all that. Remember, many analyses can be run on a single machine with a few thousand dollars’ worth of RAM and disk, rather than requiring a compute cluster at orders of magnitude more expense. (Of course, projects like Apache Beam aim to bake in the ability to scale down, but this is by no means mature.)
  • pipeline audit capacity — visualization and other insight into data at intermediate stages of processing is more important the larger the scale of the data

Scientific honesty and client relationships

data scientists are in a uniquely well-suited position to actually improve the human quality of the “wet lab” research scientists they support.  By focusing on the data science in particular, they can:

  • identify publication bias, or other temptations like p-hacking, even if inadvertent (these may also be part of the statistical methods review above)
  • support good-faith re-analysis when mistakes are discovered in the upstream data, the pipelines or supporting packages: if you’re doing all the software work above, re-running should be easy
  • act as a “subjects’ ombuds[wo]man” by considering (e.g.) the privacy and reward trade-offs in the analytics workflow and the risks of data leakage
  • facilitate the communication within and between labs
  • find ways to automate the boring and mechanical parts of the data pipeline process

Mirrored from Trochaisms.


Relational skills and the three wh’s

There’s a fairly tidy — but imperfect — correspondence between the three wh’s and the relational skillsets I proposed yesterday.

  • how corresponds well to the tooling skillset
  • what roughly corresponds to the data stewardship skillset
  • … leaving why to correspond to the collaboration skillset, which seems apt: why do data science if you don’t have someone you’re doing it with, or for?

Of course, the name “data science” probably isn’t all that, uh, sciencey:


Mirrored from Trochaisms.


Rolling the dice at the Just World Casino

tl;dr: The tech frame of “lean startup”, venture capital funding, “exit strategies”, and relentless “valuation” talk is fundamentally anti-human for nearly all of us.

[ETA (immediately after publication):]

The kneejerk libertarianism and Randian resistance to collective action among (white, male) tech workers has led to red-in-tooth-and-claw job insecurity and instability, the “[mono]culture fit”, fetishization of youth a la The Circle, and a Just World Fallacy (“meritocracy”) of increasingly dire proportions.  In particular, rewards are wildly skewed away from effort or collective valuation, and seem to track with luck, or deep enough pockets to roll the dice often.

Big winners are the poker players lucky enough to be the first ones to loot (excuse me; I mean “disrupt”) a previously protected commons (excuse me; “fish”); some of the rest of us are settling for steady jobs as dealers, wait staff, or (for the truly ambitious) pit bosses. But the big game — besides being the house — is in bringing in the big fish unicorns.

Though unicorns make for flashy external advertisements (“Sue Anne won $10,000 at Lucky Strike yesterday! will you be next?”), the core casinos themselves are relentless in taking their cut on every big win and all the small losses.  AI fantasists (whether paranoid like Bostrom or optimist like Kurzweil and Yudkowsky) would like to think that the real questions are how to deal with “superhuman” intelligence, but the real concern is how to deal with non-human intelligence; specifically, the survival of humanity in the face of increasingly-automated bureaucracy.

Their “slow takeoff” has been burning since the East India Corporation, but has hit a recent elbow (a “fast takeoff”) with the “gig economy” (“sharing” is a bridge too far).  Some of these insecurities are bleeding into the white-collar segments of the gig economies, as with the space-sharing institutions that are beginning to collect rent from players hoping to bag a unicorn:

Oh, and this isn’t working out great, even for the casino’s winners (don’t worry, though: the house is still doing just fine).

If you like this sort of terrifying doom-saying, I recommend @PhilSandifer‘s Kickstarter:

Mirrored from Trochaisms.


“Grad school” is a collaboration anti-pattern

To quote Wikipedia: an anti-pattern is:

pattern used in social or business operations or software engineering that may be commonly used but is ineffective and/or counterproductive in practice. [emphasis mine]

I’ve been exploring patterns for actually working on software — not for designing it — and I realized that I myself spent a lot of time living inside one particular pattern, which we might call the Grad School collaboration anti-pattern.

Grad school — especially the process of writing a PhD — values three things, no matter your department or specialty:

  • novelty – what you create must be different from what everybody else until now has done
  • individual effort – what you create must be your own work, not something produced by a team
  • completion over sustainability – sometimes called “PhinisheD”, or “the point of a PhD is to finish a PhD”.

Each of these targets is critical to the idea that a PhD is a work of heroic individual effort to expand the boundaries of science. This idea is a fiction, and — like so many useful fictions — is a useful fiction, though it’s rarely true in practice (my PhD, for example, was a product of my labmates and fellowship [ETA: and of course my own effort!]).

But each of these is actually a collaboration anti-pattern of its own:

  • novelty can spiral off into Not Invented Here — and frequently does
  • individual effort fosters “Colleague, pronounced as “competitor”
    – many, many escape grad school (or not) with absolutely vicious attitudes towards others working on related projects
  • completion over sustainability encourages the Just Ship It antipattern — most research code is so heavily grown into the bench that it cannot be run outside of the lab — often even the implementer him- or herself can no longer run it, by the time dissertation defense rolls around.

It sometimes astounds me that so many brilliant researchers survive PhD land — and it worries me: so many good software designers and implementers must be turned off by the dysfunction implied by each of these three.

Mirrored from Trochaisms.


“Bank heist” collaboration pattern

Here’s my favorite collaboration pattern so far: the Bank Heist collaboration pattern. This pattern, which we know from The A-TeamOcean’s 11 and Leverage, among others, shares many properties with an excellent developer team:

  • You don’t have to like following orders to be on the team.
  • Everybody’s a generalist, and an expert in one area (pickpocket, cat burglar, safe-cracker, grifter, etc) but nobody is an expert at everything.
  • “Building the team” is part of the fun.
  • There is – or should be – mutual respect for complementary skills.
  • Everybody on the team needs to do their part and get out of the other people’s way.
  • Prima donnas ruin the whole party.
  • There’s even a role for management: the Nate Ford/Danny Ocean “mastermind” character is an ideal manager: he can do enough of all the other players’ roles to see how they can all work together and set up the whole job.

I don’t know if identifying this collaboration pattern is actually useful, or if it’s just entertaining, but it is undoubtedly attractive: most people I’ve shared this collaboration pattern with get very excited to work with a team that uses this collaboration pattern. If you or a team you’re on derives some benefit from this pattern, drop me a note.

A few afterthoughts (connecting to the “theater ensemble” thoughts from Beth on Twitter):

Heist movies pick up the drama when the team starts to violate these prescriptions: when the grifter decides he’d be a better mastermind than the current leader, for example. This opens up two perspective games I like to play:

  • heistify: take your boring office politics (“QA is dawdling because they were convinced the dev will botch it anyway”) and rewrite into a bank heist: “safe-cracker didn’t bother bringing his stethoscope because he figured the second-story man wouldn’t be able to kill the alarms”. Much more fun, isn’t it?
  • shyster: make heist movies boring again by inverting the transformation above.

Finally, heist movies have awesome soundtracks. Who wouldn’t want their workday scored with horn stings?  (And, as Josh points out: you’d have a sweet van.)

Mirrored from Trochaisms.