http://research.microsoft.com/en-us/news/features/speechreco... http://research.microsoft.com/en-us/projects/mavis/
That might just be a single application but if it extends into other domains it might end up being very valuable indeed.
It will eventually become just another tool of course just like anything else but if it brings 10-20% improvements in even a few other long-stagnant areas I would agree with saying that it is a big deal.
* Problem definition
* Infrastructure
* Data transformation
* Exploratory analysis (arguably part of model work)
* Results presentation
Then again, this is an ongoing disagreement I have with the Kaggle folks over what constitutes "data science," where I'm pretty confident that "applied machine learning" is a better explanation of what their contests are about.
I'd say data transformation is a part of feature engineering (commonly the bulk of the effort in a ML application). And exploratory analysis is part of model work. W/o those 2 one would be building a model out of dreams and wishes.
Data Science is probably a poorly chosen description. I'd say common use includes infrastructure work which for most of us consists in engineering work.
https://news.ycombinator.com/item?id=4655927
BTW, I'm a big fan of the data analysis that came out of okcupid, is that all your work?
Also, data science involves a lot more than building predictive models. In my experience >95% of effort goes into something other than building a model. In kaggle contests you usually concentrate on that <5%, which IMO is the fun part but it's not the reality of industry. There are many big breakthroughs in data science that don't involve model building.
edit: I haven't listened to the podcast yet (at work), my comment is more about the title.