Historical Course Descriptions for Master's and Ph.D in Statistics/Data Science Programs I'm writing to ask whether there's any references for course descriptions for the classes required of a master's degree back in the 1990s, 1980s, 1970s, 1960s,...., 1900s. I wanted to know how the curriculum for masters degree in statistics has changed over time and how fast or when recent advancements trickle down to graduate education. 

How fast does it take for institutions to change their curriculum with each new 
fad in the development in Statistics? When was bootstrapping invented versus when was it taught in school? What's the newest technique in classification/prediction/machine learning or Statistical Theory or database management and when were those techniques marketed as courses taught in Master's or Ph.D programs? 

Personally, I'm asking because I want to trace the development of Statistics and get a sense of how up to date my professors are in the field and find the most updated program. I'm not sure if they know how pervasive or old their knowledge is. 

I'm not looking for individual program suggestions. It's the case that in computational sciences that things outdate. I don't know if some branches of Statistics are more updated than others are. Unfortunately there isn't a class in school called the History of Statistics and Data Science and Machine Learning and history of math is more famous.
I'll guess that it would be very difficult to get such information in any methodical way. In some fields, such as CS, professional societies publish undergraduate curriculum guides that are followed by many universities, at least in the US. But I've never heard of any such at any graduate level. 

As you note, curriculum tends to change with the times and old courses are replaced with new ones. A university will probably keep an historical record for a few years, since it still has students that started under an earlier regime. Many will keep records for a longer time to help respond to student requests for information from graduates. 

But there is no requirement that they be kept, that I'm aware of, and no regulatory body to assure it. 

The most you could hope for is that some universities might have a local historian who can get access to archives of old course catalogs and compile the local information. But it would be pretty spotty, I think. 

Another avenue that might produce some incomplete data, or provide a starting point, is to set up a workshop at a national or international conference, and invite professors near retirement to reflect on their own experiences with coursework in the field. This might produce some course titles, at least. And some people who taught over a long period might be able to do more. 

But all of that is overkill for your stated purpose. A simpler way to answer your concerns is to just look at what they are publishing now. If nothing, then they are probably not up to date. If a lot, then they are possibly at the forefront. But even that is probably overkill. Most of your career will likely be concerned with things that are only present now in nascent form, or even currently entirely unexplored. 

And for many of us (now retired) most of what we know is old or even obsolete. This is true even if we are at the forefront. Probably more true in CS than in statistics, actually, but still. 

Moreover, for someone truly at the edge of the research world, their knowledge is likely to be deep but narrow. They examine the bark on a particular tree, never mind the tree, or the forest in which it grows. At the MS level all this is probably less important that it seems, though it becomes more so at the doctoral level when you need to find your own tree to study.