What makes computer science a "hard" engineering and statistics not a "hard" subject? I'd hesitate to call statistics an engineering discipline, granted, but I

What makes computer science a "hard" engineering and statistics not a "hard" subject? I'd hesitate to call statistics an engineering discipline, granted, but I object to differentiating statistics from CS on the basis of the word "hard". I hope you're not trivializing statistics simply because of the elementary approach most schools tend to take for the first three or four statistics classes offered at the university level. I would argue that statistics can be just as difficult as computer science, and just like CS has intractable problems, so too does statistics. If your idea of big data is acturial tables, you're not thinking big enough. Actuarial tables are largely still small enough to be handled in orthodox ways using orthodox software (SAS has a deathgrip in managed health--what I do for a living--and it's a 50 year old piece of software). In fact, most of the times I hear people say their data has a very high volume, it really doesn't, and the traditional methods of data analysis can handle it. Once you step into Facebook/Amazon/Google sized data, things change. And that's only looking at "big data" as large volume--the most common definitions of the phrase involve several other variables (see: the four V's). Just because what's commonly done or visible is elementary doesn't mean the entire field is elementary. It would be like assuming CS isn't a hard engineering if all you saw were web designers/developers.