Smelling Like Data Science

(Adapted from a panel after-dinner talk for the in the opening session to DSET 2017)

Nobody knows what data science is, but it permeates our lives, and it’s increasingly clear that understanding data science, and its powers and limitations, is key to good citizenship. It’s how the 21st century finds its way. Also, there are lots of jobs—good jobs—where “data scientist” is the title.

So there ought to be data science education. But what should we teach, and how should we teach it?

Let me address the second question first. There are at least three approaches to take:

  • students use data tools (i.e., pre-data-science)
  • students use data science data products 
  • students do data science

I think all three are important, but let’s focus on the third choice. It has a problem: students in school aren’t ready to do “real” data science. At least not in 2017. So I will make this claim:

We can design lessons and activities in which regular high-school students can do what amounts to proto-data-science. The situations and data might be simplified, and they might not require coding expertise, but students can actually do what they will later see as parts of sophisticated data science investigation.

That’s still pretty vague. What does this “data science lite” consist of? What “parts” can students do? To clarify this, let me admit that I have made any number of activities involving data and technology that, however good they may be—and I don’t know a better way to say this—do not smell like data science.

You know what I mean. Some things reek of data science. Google searches. Recommendation engines. The way a map app routes your car. Or dynamic visualizations like these: Continue reading Smelling Like Data Science

Reflection on 538, Trump, and Bayes

Was the run-up to the recent election an example of failed statistics? Pundits have been saying how bad the polling was. Sure, there might have been some things pollsters could have done better, but consider: FiveThirtyEight, on the morning of the election, gave Trump a 28.6% chance of winning.

And things with a probability of 1 in 4 (or, in this case, 2 in 7:) happen all the time.

Prediction by FiveThirtyEight on the morning of election day.

This post is not about what the pollsters could have done better, but rather, how should we communicate uncertainty to the public? We humans seem to want certainty that isn’t there, so stats gives us ways of telling the consumer how much certainty there is.

In a traditional stats class, we learn about confidence intervals: a poll does not tell us the true population proportion, but we can calculate a range of plausible values for that unknown parameter.  We attach that range to poll results as a margin of error: Hillary is leading 51–49, but there’s a 4% margin of error.

(Pundits say it’s a “statistical dead heat,” but that is somehow unsatisfying. As a member of the public, I still think, “but she is still ahead, right?”)

Bayesians might say that the 28.6% figure (a posterior probability, based on the evidence in the polls) represents what people really want to know, closer to human understanding than a confidence interval or P-value.

My “d’oh!” epiphany of a couple days ago was that the Bayesian percentage and the idea of a margin of error are both ways of expressing uncertainty in the prediction. They mean somewhat different things, but they serve that same purpose.

Yet which is better? Which way of expressing uncertainty is more likely to give a member of the public (or me) the wrong idea, and lead me to be more surprised than I should be? My gut feeling is that the probability formulation is less misleading, but that it is not enough: we still need to learn to interpret results of uncertain events and get a better intuition for what that probability means.

Okay, Ph.D. students. That’s a good nugget for a dissertation.

Meanwhile, consider: we read predictions for rain, which always come in the form of probabilities. Suppose they say there’s a 50% (or whatever) chance of rain this afternoon. Two questions:

  • Do you take an umbrella?
  • If it doesn’t rain, do you think, “the prediction was wrong?”

Modeling Hexnut Mass

HexnutIntroLet me encourage you to go to your hardware store and get some hexnuts. You won’t regret it. Now let’s see if I can write a post about it in under, like, four hours.

(Also, get a micrometer on eBay and a sweet 0.1 gram food scale. They’re about $15 now.)

Long ago, I wrote about coins and said I would write about hexnuts. I wrote a book chapter, but never did the post. So here we go. What prompted me was thinking different kinds of models.

I have been focusing on using functions to model data plotted on a Cartesian plane, so let’s start there. Suppose you go to the hardware store and buy hexnuts in different sizes. Now you weigh them. How will the size of the nut be related to the weight?

A super-advanced, from-the-hip answer we’d like high-schoolers to give is, “probably more or less cubic, but we should check.” The more-or-less cubic part (which less-experienced high-schoolers will not offer) comes from several assumptions we make, which it would be great to force advanced students to acknowledge, namely, the hexnuts are geometrically similar, and they’re made from the same material, so they’ll have the same density. Continue reading Modeling Hexnut Mass

DASL Updated. Mostly improved.

Smoking and cancer graph.
Data from DASL, graph from CODAP. LUNG is lung cancer deaths per 100,000. CIG is number of cigarettes sold (hundreds per person). Data from 1960.

The Data and Story Library, originally hosted at Carnegie-Mellon, was a great resource for data for many years. But it was unsupported, and was getting a bit long in the tooth. The good people at Data Desk have refurbished it and made it available again.

Here is the link. If you teach stats, make a bookmark:

The site includes scores of data sets organized by content topic (e.g., sports, the environment) and by statistical technique (e.g., linear regression, ANOVA). It also includes famous data sets such as Hubble’s data on the radial velocity of distant galaxies.

One small hitch for Fathom users:

In the old days of DASL, you would simply drag the URL mini-icon from the browser’s address field into the Fathom document and amaze your friends with how Fathom parsed the page and converted the table of data on the web page into a table in Fathom. Ah, progress! The snazzy new and more sophisticated format for DASL puts the data inside a scrollable field — and as a result, the drag gesture no longer works in DASL.

Fear not, though: @gasstationwithoutpumps (comment below) realized you could drag the download button directly into Fathom. Here is a picture of a button on a typical DASL “datafile” page. Just drag it over your Fathom document and drop:


In addition, here are two workarounds:

Plan A:

  • Place your cursor in that scrollable box. Select All. Copy.
  • Switch to Fathom. Create a new, empty collection by dragging the collection icon off the shelf.
  • With that empty collection selected, Paste. Done!

Plan B:

  • Use their Download button to download the .txt file.
  • Drag that file into your Fathom document.

Note: Plan B works for CODAP as well.

Model Shop! One volume done!

The Model Shop, Volume 1Hooray, I have finally finished what used to be called EGADs and is now the first volume of The Model Shop. Calling it the first volume is, of course, a treacherous decision.

So. This is a book of 42 activities that connect geometry to functions through data. There are a lot of different ways to describe it, and in the course of finishing the book, the emotional roller-coaster took me from great pride in what a great idea this was to despair over how incredibly stupid I’ve been.

I’m obviously too close to the project.

For an idea of what drove some of the book, check out the posts on the “Chord Star.”

But you can also see the basic idea in the book cover. See the spiral made of triangles? Imagine measuring the hypotenuses of those triangles, and plotting the lengths as a function of “triangle number.” That’s the graph you see. What’s a good function for modeling that data?

If we’re experienced in these things, we say, oh, it’s exponential, and the base of the exponent is the square root of 2. But if we’re less experienced, there are a lot of connections to be made.

We might think it looks exponential, and use sliders to fit a curve (for example, in Desmos or Fathom. Here is a Desmos document with the data you can play with!) and discover that the base is close to 1.4. Why should it be 1.4? Maybe we notice that if we skip a triangle, the size seems to double. And that might lead us to think that 2 is involved, and gradually work it out that root 2 will help.

Or we might start geometrically, and reason about similar triangles. And from there gradually come to realize that the a/b = c/d trope we’ve used for years, in this situation, leads to an exponential function, which doesn’t look at all like setting up a proportion.

In either case, we get to make new connections about parts of math we’ve been learning about, and we get to see that (a) you can find functions that fit data and (b) often, there’s a good, underlying, understandable reason why that function is the one that works.

I will gradually enhance the pages on the eeps site to give more examples. And of course you can buy the book on Amazon! Just click the cover image above.


The Index of Clumpiness, Part Four: One-dimensional with bins

In the last three posts we’ve discussed clumpiness. Last time we studied people walking down a concourse at the big Houston airport, IAH, and found that they were clumped. We used the gaps in time between these people as our variable. Now, as we did two posts ago with stars, we’ll look at the same data, but by putting them in bins. To remind you, the raw data:


Continue reading The Index of Clumpiness, Part Four: One-dimensional with bins

The Index of Clumpiness, Part Three: One Dimension

In the last two posts, we talked about clumpiness in two-dimensional “star fields.”

  • In the first, we discussed the problem in general and used a measure of clumpiness created by taking the mean of the distances from the stars to their nearest neighbors. The smaller this number, the clumpier the field.
  • In the second, we divided the field up into bins (“cells”) and found the variance of the counts in the bins. The larger this number, the clumpier the field.

Both of these schemes worked, but the second seemed to work a little better, at least the way we had it set up.

We also saw that this was pretty complicated, and we didn’t even touch the details of how to compute these numbers. So this time we’ll look at a version of the same problem that’s easier to wrap our heads around, by reducing its dimension from 2 to 1.  This is often a good strategy for making things more understandable.

Where do we see one-dimensional clumpiness? Here’s an example:

One day, a few years ago, I had some time to kill at George Bush Intercontinental, IAH, the big Houston airport. If you’ve been to big airports, you know that the geometry of how to fit airplanes next to buildings often creates vast, sprawling concourses. In one part of IAH (I think in Terminal C) there’s a long, wide corridor connecting the rest of the airport to a hub with a slew of gates. But this corridor, many yards long, had no gates, no restaurants, no shoe-shine stands, no rest rooms. It was just a corridor. But it did have seats along the side, so I sat down to rest and people-watch.

Continue reading The Index of Clumpiness, Part Three: One Dimension