Its economists used to champion big firms, but the mood has shifted
Source: Schumpeter: The University of Chicago worries about a lack of competition | The Economist
There is an emerging consensus among economists that competition in the economy has weakened significantly. That is bad news: it means that incumbent firms may not need to innovate as much, and that inequality may increase if companies can hoard profits and spend less on investment and wages.
Yes, I certainly see this in tech fields.The double consequences are scary.
Thanks to colleague Prof. Liz Lyons for suggesting this.
This is how home IoT ought to work. But overall, this service is going to figure in a lot of divorce lawsuits! Excerpts from the article: Continue reading
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.