CASI looks very cutting edge, but also covers the origins of the use of computers for statistical methods. So this covers how computers can and have been used to do statistics.
FoDS looks like a very thorough data science book. Less of a "then VS now" book and more of a collection of what you need as a data scientist.
In CASI, I like the chapter on FDR, a very important topic not found in FoDS (?!). FDR is critical for correcting for multiple testing, seems essential for data science but maybe the authors consider it cutting edge and not foundational. However, the wavelet chapter in FoDS makes me happy, a very useful topic for series data.
Statistician/data scientist here. This is one of my favorite texts in the area. It frames and groups methods historical rather than mathematically. I've found it both a valuable teaching tool and an interesting read on its own. Highly recommended.
Another amazing book from Hastie/Efron. The ISLR book was my first foray into ML and landed me my current job. Will be sure to devour this one as well!
Currently a Data Scientist for an MBB consulting firm. Came from a quant role (as a Project Manager) from an Ibank. Everything I do in my current role is pretty much straight from ISLR.
How do you approach a publisher with the free PDF model? Is it something they're generally open to? (e.g. you have a corpus of work on a website that you want to turn into a book)
I don't think statistical rethinking is a good book for general statistic at all.
It's more a good book for introduction bayesian statistic. And it's super math/stat lite.
If you really want to stick with Bayesian Statistic and need a comprehendsive stat for a foundation a better book would be: Doing Bayesian Data Analysis by John K. Kruschke.
This is not an introductory book at all. From the preface: “Our intention was to maintain a technical level of discussion appropriate to Masters’-level statisticians or first-year PhD students.”
Not ridge, that was popularised by Hoerl and Kennard (1970). However, the method of augmenting the diagonals of the X'X matrix (ridge regression) was studied by Tikhonov in the early 40's as a method for solving ill-posed problems.
I believe Hastie has done lots of work on LASSO/L_1 norm related regularization methods and algorithms though.
I've read ISL and ESL. ISL is a practical introduction to concepts, and ESL goes deeper into the algorithms.
This books seems like a historical survey of how things came to be, rather than a practical guide. I believe this is not a book for practitioners, but for someone who is looking to advance the field (researchers, Ph.D. students).
As someone who was in academia in 12 years, I think it is immensely valuable to have a survey like this because too often new researchers don't have a clear idea what has been tried before and why they failed or succeeded.
http://manpages.ubuntu.com/manpages/precise/en/man1/pdfcrop....