Dare I Even Suggest…?

April 15, 2015: BART ridership between Rockridge and Embarcadero stations, by hour.

If you’ve been following along, and reading my mind over the past six months while I have been mostly not posting, you know I’m thinking a lot about data science education (as opposed to data science). In particular, I wonder what sorts of things we could do at K–12 —especially at high school — to help students think like data scientists.

To that end, the good people at The Concord Consortium are hosting a webinar series. And I’m hosting the third of these sessions Tuesday July 25 at 9 AM Pacific time.

Click this link to to to EventBrite to tell us you’re coming.

The main thing I’d like to do is to present some of our ideas about “data moves”—things students can learn to do with data that tend not to be taught in a statistics class, or anywhere, but might be characteristic of the sorts of things that underpin data science ideas—and let you, the participants, actually do them. Then we can discuss what happened and see whether you think these really do “smell like” data science, or not.

You could also think of this as trying to decide whether using some of these data skills, such as filtering a data set, or reorganizing its hierarchy, might also be examples of computational thinking.

The webinar (my first ever, crikey) is free, of course, and we will use CODAP, the Common Online Data Analysis Platform, which is web-based and also free and brought to you by Concord and by you, the taxpayer. Thanks, NSF!

We’ll explore data from NHANES, a national health survey, and from BART, the Bay Area Rapid Transit District. And whatever else I shoehorn in as I plan over the next day.

More about Data Moves—and R

In the previous post (Smelling Like Data Science) we said that one characteristic of doing data science might be the kinds of things you do with data. We called these “data moves,” and they include things such as filtering data, transposing it, or reorganizing it in some way. The moves we’re talking about are not, typically, ones that get covered in much depth, if at all, in a traditional stats course; perhaps we consider them too trivial or beside the point. In stats, we’re more interested in focusing on distribution and variability, or on stats moves such as creating estimates or tests, or even, in these enlightened times, doing resampling and probability modeling.

Instead, the data-science-y data moves are more about data manipulation. [By the way: I’m not talking about obtaining and cleaning the data right now, often called data wrangling, as important as it is. Let’s assume the data are clean and complete. There are still data moves to make.] And interestingly, these moves, these days, all require technology to be practical.

DS GraphicThis is a sign that there is something to the Venn diagram definitions of data science. That is, it seems that the data moves we have collected all seem to require computational thinking in some form. You have to move across the arc into the Wankel-piston intersection in the middle.

I claim that we can help K–12, and especially 9–12, students learn about these moves and their underlying concepts. And we can do it without coding, if we have suitable tools. (For me, CODAP is, by design, a suitable tool.) And if we do so, two great things could happen: more students will have a better chance of doing well when they study data science with coding later on; and citizens who never study full-blown data science will better comprehend what data science can do for—or to—them.

At this point, Rob Gould pushed back to say that he wasn’t so sure that it was a good idea, or possible, to think of this without coding. It’s worth listening to Rob because he has done a lot of thinking and development about data science in high school, and about the role of computational thinking. Continue reading More about Data Moves—and R

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