In many ways, this is a great book! It’s well written (and well read), and it made me care about baseball in ways I usually don’t. It’s also an interesting story—a great example of the power of statistics and data science to do cool things.

That last part, though, is why I read it. I expected to be critical of the book’s take, and I wasn’t wrong. It cheerleads attitudes about (data) science that I’m skeptical of, like its supposed superiority in terms of objectivity and rationality. It acknowledges the reduction of human beings into abstractions without ever really being skeptical of it. As cool as the core idea is, it’s also kind of a horror story, and we can see some of its scarier implications a couple of decades later.

Trying to figure out whether hate listening to the audiobook of Moneyball has implications for my suitability to teach my department’s Intro to Data Science class.

Sometimes I feel like I’m nagging my data science students when I tell them to use function x in R instead of almost-identical function y. Other times, I remember that function y has awful output that makes grading their work a pain, and I stop worrying.

I am, unbelievably, preparing my fourth offering of my department’s ICT/LIS 661 Intro to Data Science class, and this time around, I’ve decided to add a new section to my “about the class” page in Canvas to head off some concerns that I’ve seen over the past few years. I have a lot of students with no background in either statistics or programming who take my class, and it can be really intimidating for them. I’m not convinced that the advice I give below is everything that I ought to say (or exactly how I ought to say it), but this semester, I want to get out ahead of a lot of the one-on-one pep talks I give throughout the semester.

I have been feeling bad all semester for the students who signed up for my data science class because they enjoyed my games and learning one. I’m the same professor in both, but games and learning is very fun-focused and sociocultural, whereas data science is a firehose of stats and coding.

I have a “dad friend” I see most afternoons as we’re waiting for our kiddos to get out of school. He walks his kid home every day, and I either walk home or bike home with kiddo (a lot of walking recently because weather and a broken bike have been getting in our way). So, we hang out where all the walking parents hang out and chat for a few minutes before heading home. I’m on the list of approved people to pick his kid up, I keep meaning to add him to the list of approved people to pick kiddo up, our kids have been in the same class for two years in a row, and our shared relationship with the school and our kid’s teacher is pretty much the whole basis of our relationship.

Interesting stuff from Doctorow. If I can, I want to work it into my data science textbook for next semester.

I’m spending a chunk of today starting on revisions to my Intro to Data Science course for my unit’s LIS and ICT graduate prograrms. I’d expected to spend most of the time shuffling around the content and assessment for particular weeks, but I quickly realized that I was going to need to update what I had to say in the syllabus about plagiarism and academic offenses. Last year’s offering of the course involved a case of potential plagiarism, so I wanted to include more explicit instruction that encourages students to borrow code while making it clear that there are right and wrong ways of doing so. Likewise, my colleague Josh Rosenberg posted earlier today about ChatGPT’s Code Interpreter, and while I don’t know that my students will even know that’s an option, I wanted to get out ahead of that possibility, too.

I’m pleased to share the publication of a new chapter of an edited volume. The chapter in question is “I’m a French teacher, not a data scientist”: Culture and languages across my professions, and it’s part of a volume called Cultures and languages across the curriculum in higher education. According to the CLAC Consortium, Culture and Languages Across the Curriculum (CLAC) is a:

a curricular framework that provides opportunities to develop and apply language and intercultural competence within all academic disciplines through the use of multilingual resources and the inclusion of multiple cultural perspectives.

Generally, I discourage my intro to data science students from tackling questions they can’t answer at their level of programming, but sometimes I get so interested in the question that I end up writing the code for them so I can see what they do with it.

The new semester at the University of Kentucky starts on Monday, and I am flailing to try to get my data science course ready to go—including putting together an open, alternative textbook for my students. I’ve been borrowing heavily from Catherine D’Ignazio and Lauren Klein’s Data Feminism for my textbook: It’s a fantastic resource, and I’m hoping my students take a lot from it.

Of course, my kid’s semester has already started, and I’ve already blogged a bunch about my frustrations with her new school’s use of ClassDojo this year. It turns out that Data Feminism is also a helpful resource here. Riffing on the common “data is the new oil” metaphor, D’Ignazio and Klein argue that:

Really leaning into ethics and justice elements of data science in my fall class, and I’m wondering how much pushback I’m going to get. I’ve taught about racism, sexism, and colonization in games in another class with very few complaints, but this feels different somehow.

I just barely microblogged something about what I want to say here, but over the past hour, it’s been nagging at me more and more, and I want to write some more about it.

I was introduced to academia through educational technology, and I was introduced to educational technology through a class at BYU taught by David Wiley. This class was not about educational technology, but David’s passion for Web 2.0, Open Educational Resources, and remix culture were so strong that I got hooked. OER and Creative Commons licensing both got firmly planted deep in my thinking, and even though they never became a focus of my own edtech work, they’ve also never left my brain.

This summer, I’m remixing an alternative textbook for my Fall intro to data science class, and I’m pleasantly surprised by how helpful Creative Commons-licensed journal articles are proving. Shows that “open access” is only part of license’s benefits.

We need data to make decisions, but predictive analytics tend to give me the heebie jeebies.

One of my data science students just did a t-test to demonstrate that evil-aligned monsters in D&D 5e tend to have lower Armor Class than good-aligned monsters. This course demands a lot of effort, but moments like this make it worth it.

Teaching R for the first time, and many students are first-time programmers. I’m reminded of teaching French in terms of how easy it is to take for granted things that aren’t obvious to beginners.

Unsatisfied with the Intro to Data Science textbook I’ve inherited. Fortunately, an earlier version is Creative Commons-licensed, as are some other fantastic resources. Guess who’s going to remix himself a new textbook for next Fall!

I know I’m going to make plenty of mistakes teaching Intro to Data Science for the first time, but one thing I’m already proud of is teaching my students to use <code> tags to format code and output in their Canvas posts.

Cory Doctorow has THOUGHTS about data. I may use this in my data science class this semester.