I’m going to list some oddball potential case study opportunities for my students here. (I’m teaching 3 courses in April, all requiring papers!).
Having a computer and a person you’ve never met pick clothes out for you, based on a style questionnaire and your social media photos, seems an odd concept. But San Francisco’s Stitch Fix and Le To…
Source: Style by subscription: Why clothing-to-your door is so popular – Silicon Valley
This is relevant to GPS students who are considering where they will fit in the data analytics/big data world.
Interview of Emily Robinson, who transitioned from a social science background to a career in data science, recently becoming a data analyst at Etsy.
Source: Emily Robinson, from Social Scientist to Data Scientist – FORWARDS
The research on this seems pretty overwhelming: laptops and cell phones in class hurt learning. Related issue: learning to listen.
Unfortunately in my more quant courses, they can be necessary at times. But if I had a way to turn off the Internet, I certainly would. (FCC makes wireless jamming illegal – for good reason.)
#firstsevenjobs is an interesting example of crowdsourcing research
- Dishwasher (^3) (Exeter, Harvard, and a summer job)
- Lifeguard (Local swimming hole)
- Library assistant (^2) (Harvard. One was work and one was a sinecure. I’m still really fast at putting things in alphabetical order.)
- Sci. programmer (Smithsonian Astrophysical Observatory)
- IT salesman (IT startup company)
- Business programmer (Xerox. My first taste of a really big company, and I hated it.)
- Energy consultant (DC consulting firm)
from Twitter https://twitter.com/RogerBohn
August 10, 2016 at 10:11PM
I just completed teaching a 10 week course on data mining for MS level professional degree students. Most of the material is on a web site, https://irgn452.wordpress.com/chron/ The course assumes good knowledge of OLS regression, but other than that is self-contained.
Software is R, with a heavy dose of Rattle for the first few weeks. (Rattle is a front end for R.) The main algorithms I emphasize are Random Forests and LASSO, for both classification and regression. I emphasize creating new variables that correspond to the physical/economic characteristics of the problem under study. The course requires a major project; some students scrape or mash their own data. Because we have only 10 weeks, I provide a timetable and a lot of milestones for the projects, and frequent one-on-one meetings.
The web site is not designed for public consumption, and is at best in “early beta” status. I am making it available in case anyone wants mine it for problem sets, discussions of applied issues not covered in most books, etc. Essentially, it is a crude draft of a text for MBAs on data mining using R. This was about the fifth time I taught the course.
By the way, a lot of the lecture notes are modestly modified versions of the excellent lecture material from Matt Taddy. His emphasis is more theoretical than my course, but his explanations and diagrams are great. Readings were generally short sections from either ISLR by James et al, or Data Mining with Rattle and R. Both are available as ebooks at many universities. My TA was Hyeonsu Kang.
I teach a course on Data Mining, called Big Data Analytics. (See here for the course web site.) As I began to learn its culture and methods, clear differences from econometrics showed up. Since my students are well trained in standard econometrics, the distinctions are important to help guide them.
One important difference, at least where I teach, is that econometrics formulates statistical problems as hypothesis tests. Students do not learn other tools, and therefore they have trouble recognizing problems where hypothesis tests are not the right approach. Example: when viewing satellite images, distinguish urban from non-urban areas. This cannot be solved well in a hypothesis testing framework.
Another difference is less fundamental, but also important in practice: using out-of-sample methods to validate and test estimators is a religious practice in data mining, but is almost not taught in standard econometrics. (Again, I’m sure PhD courses at UCSD are an exception, but it is still rare to see economics papers that use out of sample tests.) Of course in theory econometrics formulas give good error bounds on fitted equations (I still remember the matrix formulas that Jerry Hausman and others drilled into us in the first year of grad school). But the theory assumes that there are no omitted variables and no measurement errors! Of course all real models have many omitted variables. Doubly so since “omitted” variable includes all nonlinear transforms of included variables.
Here are two recent columns on other differences between economists’ and statisticians’ approaches to problem solving.
Differences between econometrics and statistics: From varying treatment effects to utilities, economists seem to like models that are fixed in stone, while statisticians tend to be more comfortable with variation, by Andrew Gelman.
I recently audited some lectures by friend and China expert Prof. Susan Shirk. She bans computers in her lectures. But one student sitting near me had his machine out and was “busy” with the usual distractions. (Didn’t he know the Associate Dean was a few seats away?) I asked Susan about him after class. “He told me he can’t take notes without a computer.” Obviously the computer is not the big issue on his note taking. Actually, it probably IS the issue – but in a negative way.
Not one computer mirrors the overheads.
James Kwak has beaten the distraction of cell phones – by removing most apps, including browsers.
I know that its enormous powers of distraction also make me lose focus on work, tune out in meetings, stay up too late at night, and, worst of all, ignore people in the same room with me. We all know this. We’re addicted to the dopamine hit we get when we look at our email and there’s actually something good in there, so we keep checking our email hoping to feel it again.
via How I Achieved Peace by Crippling My Phone — Bull Market — Medium.
Clay Shirky, an Internet sociologist, has a good discussion of why he recently banned computers in his classrooms. Excerpt:
I came late and reluctantly to this decision — I have been teaching classes about the internet since 1998, and I’ve generally had a laissez-faire attitude towards technology use in the classroom. This was partly because the subject of my classes made technology use feel organic, …. And finally, there’s not wanting to infantilize my students, who are adults, even if young ones — time management is their job, not mine.
Despite these rationales, the practical effects of my decision to allow technology use in class grew worse over time. The level of distraction in my classes seemed to grow, even though it was the same professor and largely the same set of topics, …
Over the years, I’ve noticed that when I do have a specific reason to ask everyone to set aside their devices (‘Lids down’, in the parlance of my department), it’s as if someone has let fresh air into the room. The conversation brightens, and more recently, there is a sense of relief from many of the students. Multi-tasking is cognitively exhausting — when we do it by choice, being asked to stop can come as a welcome change.
So this year, I moved from recommending setting aside laptops and phones to requiring it, adding this to the class rules: “Stay focused. (No devices in class, unless the assignment requires it.)” …
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