This is a pretty crusty and old-fashioned list. Within the sub-areas (e.g., time series), it is also duplicative. I don't think this is a good starting point

This is a pretty crusty and old-fashioned list. Within the sub-areas (e.g., time series), it is also duplicative. I don't think this is a good starting point for the HN readership. I think that starting out with a machine learning (ML) text would be more helpful to someone who's new to the area of modeling and decision-making under uncertainty. If you're already familiar with the ML perspective, and want a more conventional (but not stale) stats approach, "All of Statistics" (Wasserman), or Casella and Berger, would be better choices. If you're familiar with the basic stats outlook, then there are better sources for many of the specific modules listed, in addition to (probably) a need for RSS to re-examine and diversify the modules offered. Old-fashioned-ness has been an issue for the discipline (or else there might not be a thing called Machine Learning, it would be part of Statistics proper).