andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1517 knowledge-graph by maker-knowledge-mining
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Introduction: 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 f
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1 for the course she’s teaching at Columbia, “Introduction to Data Science. [sent-2, score-0.232]
2 Her latest post is “On Inspiring Students and Being Human”: Of course one hopes as a teacher that one will inspire students . [sent-4, score-0.716]
3 But what I actually mean by “inspiring students” is that you are inspiring me; you are students who inspire: “inspiring students”. [sent-7, score-0.559]
4 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 . [sent-9, score-0.739]
5 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 . [sent-16, score-1.257]
6 ) 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. [sent-20, score-0.566]
7 Jed works as a data analyst at Case Commons, a nonprofit that builds web apps and and databases for state-wide foster care agencies. [sent-22, score-0.266]
8 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. [sent-28, score-0.475]
9 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 . [sent-29, score-0.306]
10 The comments on the blog from various students are also starting to become interesting. [sent-32, score-0.514]
11 In my own teaching, sadly, I pretty much have the goal of turning the students into mini-me’s. [sent-36, score-0.353]
12 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. [sent-38, score-0.62]
13 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. [sent-39, score-0.424]
14 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. [sent-40, score-0.477]
15 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! [sent-42, score-0.335]
16 Those are the kind of homework assignments that I write. [sent-53, score-0.399]
17 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. [sent-54, score-0.427]
18 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. [sent-55, score-0.613]
19 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. [sent-56, score-0.566]
20 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. [sent-60, score-0.752]
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