Mining data

Random winter scene: Old Mission Point Light, MI, USA

Random winter scene:
Old Mission Point Light, MI, USA

Last week I went to a one-day conference in London on High Performance Computer and Big Data.  We were talking about computers with 96,000 processors and datasets in the exascale, which means the number of pieces of data they contain is one with eighteen noughts after it.  We were just across the street from the Houses of Parliament and David Willetts, the UK Minister of Universities and Science, addressed us and told us that ‘future scientific advances are dependent on our ability to accumulate and analyse big data’.  For the industrialists amongst us the slogan from the Director of the UK’s biggest computer was ‘to compute is to out compete’.

Suzy Moat and Tobias Preis of Warwick Business School made a great presentation about the link between online behaviour and economic decision making around the globe.  They have found that the frequency terms such as ‘debt’, ‘stocks’ and ‘portfolio’ are predictors of subsequent stock market movement.  They performed some of their research by mining data available from Google Trends – if you have never visited this bit of Google’s domain then I recommend a visit, its interesting at all sorts of levels.

Another Google data-miner is Seth Stephens-Davidowitz who has revealed that American parents want their boys to be smart and their girls skinny.  Parents are two and half times more likely to ask Google ‘Is my son gifted?’ than ‘Is my daughter gifted?’ despite the fact that in American schools girls are 11 percent more likely to be in gifted programs.  And conversely, parents are twice as likely to ask ‘Is my daughter overweight?’ than ‘Is my son overweight?’ even though roughly equal proportions of girls and boys are overweight in the USA.  In his article in the NYT, Seth concludes by asking ‘How would girls’ lives be different if parents were half as concerned about their bodies and twice as intrigued about their minds?’

Perhaps, one answer is that they would be more likely to opt for what are perceived at school as the hard subjects, i.e. mathematics and physics.  See my earlier post entitled ‘Chemical Imbalance’ on October 2nd, 2013, in which I bemoaned the low proportion of girls taking A-level Physics at school.  As professional engineers and university teachers many of us are working hard to redress the gender imbalance in engineering but now I wonder if we are have identified a new handicap, i.e. parents are undermining their daughters’ confidence to enter the ‘problem-solving’ professions.


Seth Stephens-Davidovitz, ‘Is my son a genius?’ in the International New York Times on Monday 20th January, 2014.

Pries, T., Moat, H.S., Stanley, H.E., 2013, Quantifying trading behaviour in financial markets using Google Trends, Scientific Reports 3, 1684. www.nature.come/strep/2013/130425/srep01684/pdf/srep01684.

2 thoughts on “Mining data

  1. Pingback: Emergent inequality | Realize Engineering

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