I’m happy to share that the Fall 2023 edition of my remixed Introduction to Data Science textbook is now available on my website. This book adapts material from the “ModernDive” Statistical Inference via Data Science course, Catherine D’Ignazio and Lauren Klein’s excellent Data Feminism, a number of other Creative Commons-licensed works, and some of my own contributions to put together a no-cost, openly-licensed textbook for my data science students. I put together the first edition of this book for last Fall’s version of this course, but the first run through taught me a lot, and I’m very happy about this edition (though I do have a small laundry list of errors to fix, and I’d like to eventually get into some fiddlier bits like removing social media icons from the header).
🔗 linkblog: my thoughts on 'A jargon-free explanation of how AI large language models work | Ars Technica'
Haven’t read this yet, but I’m bookmarking for my classes. link to ‘A jargon-free explanation of how AI large language models work | Ars Technica’
🔗 linkblog: my thoughts on 'Pluralistic: The surprising truth about data-driven dictatorships (26 July 2023) – Pluralistic: Daily links from Cory Doctorow'
Interesting stuff from Doctorow. If I can, I want to work it into my data science textbook for next semester. link to ‘Pluralistic: The surprising truth about data-driven dictatorships (26 July 2023) – Pluralistic: Daily links from Cory Doctorow’
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.
This article provides good examples of how the efficacy and efficiency of a given technology is often less important than deeper questions of reliance and roles. link to ‘Too much trust in machine translation could have deadly consequences.’
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.
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.