Find Hidden Patterns in Big Data – A Commentary on MINE, Reshef et al (2011)

You may have read something about ‘Detecting Novel Associations in Large Data Sets’, a paper appearing in Science, 334, 1518 (2011) by David N. Reshef et al.. You can check out the software here. This is an initial commentary and an explanation about what it’s all about. The Longer You Look, The More Likely Error [...]

Dec

18

Siri and Search

Gary Morgenthaler had a few interesting statements to make: “Therefore, when Siri was an independent company, its plan was to map these domains deeply and seamlessly to automate transactions for its users within them. For example, “Buy that Steve Jobs biography book and send it to my dad”; “Send a dozen yellow roses to my [...]

Nov

09

Data Science

Data Science is the mix of computer science, user experience, and statistics. The aim of data science should be: to make things better by influencing people and things to make better decisions, by making people and things more aware of better alternatives, based on better algorithms and more relevant data. Language kept intentionally vague to [...]

Oct

28

Web Analytics Wednesday – October 26 – Wellington

Web Analytics Wednesday is tonight at The Wellington, in downtown Toronto’s analytics alley. It’s generously supported by AT Internet. There are some 40 people – representing among the best of the best, who will be in attendance. It’s a great opportunity for web analysts, social analysts, marketing scientists, data scientists, hackers, developers, and usability professionals [...]

Oct

26

Data and Usability

Not all data is usable on its own. The vast majority of it isn’t in its raw form. Its coal. It has potential. But on its own, it has limited uses. Algorithms are the modern day equivalent to machinery. Fire (combustion) is really just statistical analysis – a violent process that generates waste in the [...]

Oct

24