Data Moves: the cards metaphor

In the Data Science Games project, we started talking, early, about what we called data moves. We weren’t quite sure what they were exactly, but we recognized some when we did them.

In CODAP, for example (like in Fathom), there is this thing we learn to do where you select points in one graph and, since when you select data in CODAP, the data are selected everywhere, the same points are selected in all other graphs—and you can see patterns that were otherwise hidden.

You can use that same selection idea to hide the selected or unselected points, thereby filtering the data that you’re seeing. Anyway, that felt like a data move, a tool in our data toolbox. We could imagine pointing them out to students as a frequently-useful action to take.

I’ve mentioned the idea in a couple of posts because it seemed to me that data moves were characteristic of data science, or at least the proto-data-science that we have been trying to do: we use data moves to make sense of rich data where things can get confusing; we use data moves to help when we are awash in data. In traditional intro stats, you don’t need data moves because you generally are given exactly the data you need.

Continue reading Data Moves: the cards metaphor

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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