Sunday data/statistics link roundup (4/28/2013)

  1. What it feels like to be bad at math. My personal experience like this culminated in some difficulties with Green’s functions back in my early days at USU. I think almost everybody who does enough math eventually runs into a situation where they don’t understand what is going on and it stresses them out.
  2. An article about companies that are using data to try to identify people for jobs (via Rafa).
  3. Google trends for predicting the market. I’m not sure that “predicting” is the right word here. I think a better word might be “explaining/associating”. I also wonder if this could go off the rails.
  4. This article [ 1. What it feels like to be bad at math. My personal experience like this culminated in some difficulties with Green’s functions back in my early days at USU. I think almost everybody who does enough math eventually runs into a situation where they don’t understand what is going on and it stresses them out.
  5. An article about companies that are using data to try to identify people for jobs (via Rafa).
  6. Google trends for predicting the market. I’m not sure that “predicting” is the right word here. I think a better word might be “explaining/associating”. I also wonder if this could go off the rails.
  7. This article](http://www.r-bloggers.com/faster-higher-stonger-a-guide-to-speeding-up-r-code-for-busy-people/?utm_source=feedly&utm_medium=feed&utm_campaign=Feed:+RBloggers+(R+bloggers)) in terms of describing the ways that you can speed up R code. My favorite part of it is that it starts with the “why”. Exactly. Premature optimization is the root of all evil.
  8. A discussion of data science at Tumblr. The author/speaker also has a great blog.