I don't love the term 'mansplaining', but if there is a term that describes essentially the same idea but in the context of academic fields, its exactly how I would describe the central thesis of this blog post and a trend I've encountered frequently in the last couple years. There is a rising tide of CS people who have just latched onto the hype of data science, and now go around letting statisticians know that no, they aren't actually interested prediction, they don't actually know how to work with large data, and don't actually work with non-parametric methods. It certainly comes as a shock to all of the statisticians in the world who have indeed been working on these types problems for a long time now.
> It certainly comes as a shock to all of the statisticians in the world who have indeed been working on these types problems for a long time now
Yup on high dimensional data of dimension as fantastic as 12. I feel bad for them though but they have only themselves to blame -- got too comfortable within their small world and lost touch of what the next set of interesting problems are.
Its only after getting kicked in the nuts that I see a course correction and that's enriching both ML as well as Stats.
If one computes stats on 600 data points with 10 dimensions and feels king of the hill, they can continue, but there is likelihood that some one else will be eating your lunch and you will be left behind. Sadly enough, this has already happened and is quite evident if one steps out of the stats bubble. Statistics could have been what machine learning and datamining is now, been the main driving force, the owner of initiative. On the contrary other communities are using statistics and probability motivated approaches but engineering them well to grab (funding) attention, well deserved in my opinion. It is them who got the ball rolling again.
Your response is so entirely off base I don't know where to begin.
> Yup on high dimensional data of dimension as fantastic as 12
Who says that has been the limit of classical statistics.
> If one computes stats on 600 data points with 10 dimensions and feels king of the hill, they can continue, but there is likelihood that some one else will be eating your lunch and you will be left behind.
Again, why do you have this impression? You clearly have no experience in the field if this is what you think statistics is. Unless your intent is to simply construct strawman arguments.
> Statistics could have been what machine learning and datamining is now, been the main driving force, the owner of initiative.
This is entirely based on the assumption that machine learning and datamining and statistics are distinct and separate, which isn't the case and is my entire point.
> On the contrary other communities are using statistics and probability motivated approaches but engineering them well to grab (funding) attention, well deserved in my opinion. It is them who got the ball rolling again.
> Who says that has been the limit of classical statistics.
The professors writing the grants. No not the limit of statistical methods per se but the limit of what they want to consider. Yeah I used to get involved in the review on rare occasions.
> You clearly have no experience in the field if this is what you think statistics is.
Does 18 years count ? I may be wrong about this but you sound like a newish grad student in statistics. If that is true I go back a bit more than you do and know about the state of affairs at the stats departments, their funding/projects/budget woes. I feel glad that the statistics departments got shaken a bit by ML and datamining for statistics to try and become relevant again.
If you follow the link in my parent comment you will see a vigorous argument (presumably by a student of statistics) that there is no reason why statistical packages even need to support 64 bits. This kind of thinking was pervasive, thankfully things are a bit better now and that would not have happened on its own.
