Many places have great introductory data science courses and resources like this course from Columbia, columbiadatascience.com/about-the-class/, a 3 course sequence at UF (each course is 3 credits) on data in Computer Science, and UCLA’s Intro to Digital Humanities (dh101.humanities.ucla.edu). UCLA’s Intro to Digital Humanities covers “Concepts, Methods, and Tutorials for Students and Instructors.”
I’m missing or haven’t found what I would consider to be an introductory data course that covers “Concepts, Methods, and Tutorials for Students and Instructors” at the level that my experience tells me is needed. I’m not confident that I’m correct on this, but I have too many conversations on data with folks from all fields and all levels where there are gaps that could be best supported with more on concepts. Other findings support this need:
After coding and analysis, several major themes emerged from the faculty’s observations of graduate students’ deficiencies in data management. These themes are metadata, standardizing documentation processes, maintaining relationships among data, ethics, quality assurance, basic database skills, and preservation. (muse.jhu.edu/journals/portal_libraries_and_the_academy/v011/11.2.carlson.html)
The full article for the quote above lists core competencies and more completely explains their findings. My reading of it indicate a need for greater emphasis on concepts (as well as being applied to specific data needs). UCLA’s Intro to Digital Humanities is the best model for what I’m looking for, but I’m looking for greater emphasis on data, so removing some of that course and then adding in more resources like those from Columbia’s Intro to Data Science.
In this session, I’d like to discuss developing the framework for such a course, with the understanding that there would be many guest lecturers and teachers, including:
- How is the need covered already? What existing models/examples should be used?
- What would be the concept and one tool that this course can’t live without?
- What elements would be essential for the course? (Scale, unit operations, procedural rhetoric, provenance, metadata as “constructed, constructive, and actionable” alatechsource.metapress.com/content/p3022442071g7655/fulltext.html)
- How should the course be organized? (How much time on project management and working in teams?)
- How to ensure practical/applied learning as well as emphasizing concepts over mechanics (versus a current problem of “mechanics over concepts”: docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1012&context=lib_fspres)?
- Is this a data course, informatics course, DH course? How does this relate to what Cathy Davidson calls “Digital Literacy with a Maker Spirit”?
- How would this course look as a 1 week workshop, perhaps with ½ days on lectures and ½ days on applied?
- How would this course look as a DOCC (Distributed Online Collaborative Course, adanewmedia.org/2012/11/issue1-juhasz/)?
This would be a neat conversation to have. The Columbia course looks interesting. I think the instructors who teach the course would have to pitch it in a way so students don’t feel that data = statistics. It could be a great co-taught, interdisciplinary course.