Don’t, this is not a probability post where I shall be explaining the odds of getting one of the faces of the dice… and where I could consider that the odds might be constrained by the fact a good thrower could adjust them. Let me know your views in the comments section, anyway.
What I wanted to say is that after a long period off the hook I am rolling again. Since October 2012, when I migrated from Spain escaping the bloody economic crises, I have been lecturing Economics at a private college in London. It has been fun lecturing micro theory, economics of government policies, applied economics, econometrics, and economics of the EU. However, I had stopped with my research to focus on pursuing my career in academia.
Hopefully, this is changing, and I’m moving on: firstly, trying to get back to my PhD thesis (cultural economics); second, working on methodologies for making good use of Big Data. While the former may be a little boring to explain at the moment (find here the briefing), the later I’m quite excited about.
Last January I read from Laura’s blog (Punk Rock OR) she had started to have a look at it. In March I was helping a colleague to proceed with some conventional hypotesis testing with data gathered from the social networks to sadly find after some deceptive results and basic literature review that big samples made these tests useless (even, it seems Pharma tests use bigger samples on purpouse to get higher likelihood of approval rates); a Professor friend of mine recommended Bayesian statistics, while my colleague thinks machine learning might be a better path to follow. We’ll analyse both, though. Further… ops, I’m talking too much: give some months before I can say a word. Cheers! 😉