tbl: thinking about data

For the past six months, I have been working in a space where all of my intellectual output was owned by the company I worked for. As a result, there were projects that simply had to sit. That time has passed, and I have two that I want to revisit: the teaching and learning of data science in the broader context of computing, and my own explorations regarding the principles and practices that tooling can embody when it comes to working with data. I might even sneak some IoT/embedded systems in, but there are only so many hours in the day.

I’ll probably sneak some articles about hardware and firmware design in here as well, because that’s part of the data chain, so-to-speak.

teaching and learning of data

Last spring and summer, I was thinking hard about the teaching and learning of data.

Along with my colleague at Fulbright University Vietnam, Sebastian Dziallas, we began laying out a two-course sequence that would introduce students to human-centered principles of collectiong, working with, and questioning data in deep and meaningful ways. Who asks the questions? Who collects the data? How is it collected? What biases do we bring to the analysis? How do we report our findings, and to whom? What hardware and software is needed to support this learning in active and meaningful ways?

This is one space that I will begin documenting and unpacking here. Sebastian and I spent a year discussing related topics prior, and put in weeks of intense work on this during the summer. It has been unpacked (in part) in notes and documents, but should be unpacked more fully before the memory fades completely.

embodied ideas in tooling

One red thread to my time at Bates was thinking hard about how you introduce programming and the analysis of data to students from across the full breadth of the liberal arts. Computation has a place in every discipline, but how and why it is employed varies greatly. Artists might work with real-time data as part of performance, while social scientists generate their data through survey and interview, while natural scientists might use experiment or simulation to develop the data that informs their analysis. The context to each of these matters, the computational tools are not strictly the same, and the metalearning is drastically different in each case. What, then, become the driving principles that might unify these kinds of inquiry, and how can those principles be exemplified in the teaching and tooling that we bring to our students?

To explore this, I began work on tbl, a library of code in Racket that explores these concepts.

Now that I am once again free to author open code and write about my ideas without them explicitly being owned by others, I will be revisiting this work here over the coming weeks.

posts in the series

2020 03 10 tbl: a slice of cake code
2020 03 09 tbl: testing round one code
2020 03 09 tbl: structuring the project code
2020 03 08 tbl: abstractions and imports code
2020 03 07 tbl: thinking about data code

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