(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Rachel Schutt (the author of the Taxonomy of Confusion) has a blog! for the course she’s teaching at Columbia, “Introduction to Data Science.” It sounds like a great course—I wish I could take it!
Her latest post is “On Inspiring Students and Being Human”:
Of course one hopes as a teacher that one will inspire students . . . But what I actually mean by “inspiring students” is that you are inspiring me; you are students who inspire: “inspiring students”. This is one of the happy unintended consequences of this course so far for me.
She then gives examples of some of the students in her class and some of their interesting ideas:
Phillip is a PhD student in the sociology department . . . He’s in the process of developing his thesis topic around some of the themes we’ve been discussing in this class, such as the emerging data science community.
Arvi works at the College Board and is a part time student . . . He analyzes user-level data of students who have signed up for (and taken) the SATs and has lots of interesting data around where those students hope to go to college; and longitudinal data sets that allow him and his colleagues to examine trends . . .
Adam, Christina and Eurry (respectively, (1)sociology PhD student, (2)data scientist at Nielsen, and (3)”aspring data visualizer” (better term?) from the QMSS program) have taken on the challenge of polling the students and then developing an algorithm to automatically find optimal data science teams and a corresponding visualization.
Matt is a history of science professor who wrote the Curse of Dimensionality post a week ago, and is starting to think about (or revisit) how exploratory text classification could be used in his research.
Jed works as a data analyst at Case Commons, a nonprofit that builds web apps and and databases for state-wide foster care agencies. . . . he read this paper on using Naive Bayes to classify suicide notes, and now has some early ideas of ways he might apply this approach in his own work.
Maryanne is the Executive Director for the Center for Innovation Through Data Intelligence in Mayor Bloomberg’s office. Her office deals with data about the juvenile justice system, homelessness and poverty and she too is thinking about how analyzing data sets could be used to prioritize social worker interventions.
Then let’s not forget the Biomedical Informatics (or variation of that) students/post-doc, Hojjat, Albert and Heather; or Kaushik, the student from operations research interested in journalism; or Yegor, the business school student who has an interest in urban planning and architecture . . .
The comments on the blog from various students are also starting to become interesting. Also let me add Jared’s (our lab instructor) study of his own text messages, after he broke up with his girlfriend, which he just told me about tonight.
This all reminds me of a comment Seth made once, many years ago, that the usual goal (even if not explicitly stated) of a class is for the students to become replicas of the instructor, whereas he (Seth) liked to teach in such a way that each student could bring in his or her special knowledge, interests, and abilities. I don’t know how good a teacher Seth actually is—I have lots of innovative teaching ideas too, but I’m not such a great teacher, in fact in a large part it’s my crappiness as a teacher that inspires me to come up with new teaching ideas (the entire book Teaching Statistics: A Bag of Tricks arose out of my difficulties in getting students actively engaged in class)—but I think his idea is interesting. In my own teaching, sadly, I pretty much have the goal of turning the students into mini-me’s. I mean, sure, I don’t want them all to become me, I want them to develop their own talents, but implicitly I’m acting as if the best way to do so is to first become as much like me as possible.
I think Rachel’s shout-outs above are great, not just because it’s a nice thing to do, but because the act of writing these details about the students helps to bring these project to life, as well as to inspire others.
Rachel also has some thoughts about statistics education:
Traditional Statistics Pedagogy
First, allow me to describe the way traditional statistics classes/textbooks present data analysis. A standard homework problem would be: one is presented with a clean data set, and told to run a regression with y=weight and x=height, for example. I look unfavorably upon this because it takes the creativity and life out of everything, and doesn’t resemble in the least what it’s like to actually be a researcher or statistician in the real world. As mentioned previously, in the “real world” (no offense, classrooms aren’t the real world), no one is going to hand you a nice clean data set (and if they did, I’d be skeptical! Also where’d it come from?), and no one is going to tell you what method to use. (Why are we even running this regression in the first place? What questions are we even trying to answer?). The homework problem might then have some questions about interpreting or understanding the model: “interpret the coefficients” and let’s face it, most people think these are blow-off questions, don’t take them all that seriously, and there are no consequences if they mis-interpret on a homework problem, so they may think about it for . . . a minute, and then write some plausible interpretation.
Oof! Those are the kind of homework assignments that I write. Rachel continues with a visual:
She continues with her preferred model:
followed by:
This all makes sense to me, and it looks a lot like a bunch of notes I took back in 1997 or so, after teaching applied statistics to the Columbia graduate students. I gave them open-ended homework assignments, then in class spent some time giving them the background they needed to know to attack the problems, and spent a lot of time going over the homeworks they had just turned in. The students liked the class, and I had thoughts of trying to write up a general approach to data analysis, trying to formalize what I did and what I taught. After a few years of this, though, I became dissatisfied because I felt that, although the class was a good learning experience for the students, they didn’t actually end up with many useful new skills. And, over the years, I’ve moved to a more structured, textbook-based style of teaching. This semester, in fact, I’m pretty much just going through Bayesian Data Analysis section by section. We do have some open-ended homework assignments on applied statistics—I think this is important—but maybe Rachel is right that the students aren’t getting a clear message on where to go with these.
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