Material/courses for analyzing very large data sets I am looking for good materials to learn more about analyzing very large data sets, say 5 million observations, with a reasonable computational time (say less than 10h).
My main interests is traditional analysis methods, such as linear, Poisson and logistic regression, both mixed and not-mixed. Together with simple illustration and presentation of data, that provide easy understandable interpretation and options for confounder correction.
I am open to alternative methods yielding reasonable similar results with the methods mentioned above, but reducing the computational time. For example, in some cases using a robust sandwich estimator can account for incorrect specified correlation structure but reduce computational time compared with a cluster methods.
I am familiar with the programs R, SAS and STATA, and would prefer methods available in these programs.
If you know of relevant (English/Danish) courses held by universities, I would love to have links for course descriptions for this as well.
I hope to hear from you.

For large data sets, once could use bootstrap aggregation to speed up and gain more accurate estimates.
This article is a good exposition on how to do it:
https://towardsdatascience.com/bootstrap-regression-in-r-98bfe4ff5007.
For glm, one could replace the lm function in the provided code and use whatever distribution family, whether Poisson, Binomial, etc.
For machine learning and other methods more ideal for big data, one can use Elements of Statistical Learning to study the theory:
https://web.stanford.edu/~hastie/ElemStatLearn/.
I'm not familiar with machine learning for R or Stata beyond a few libraries.
