The lies, intimidation and deceit that are described in this book leave me terrified. I have always been very sceptical towards the deification of indThe lies, intimidation and deceit that are described in this book leave me terrified. I have always been very sceptical towards the deification of individuals, in particular as happens a lot in Silicon Valley now. Seeing my suspicious feelings vindicated makes this read of the downfall of Holmes and her company Theranos satisfying. While this justification, along with masterful suspense and just a great story make this book a very smooth read, above all, the terror of what happened at Theranos remains. There are many injustices in this story. Obviously, money has been spent and evaporated, mostly by gullible and rich people. More importantly, Theranos has caused stress, fear and loss of money and time among their patients. It is very painful to read how sociopaths like Holmes and Balwani can continue to intimidate, bully and deceive so many people while making obscene amounts of money, and being praised for the whole thing as though they are really saving the world. Very, very scary beings are the lawyers and lawsuits described in this book that Theranos employed to intimidate and sue former employees and the journalists and experts that brought this story to light. The fact that they just get away with this. ...more
This is a great book, one that will influence me for the rest of my life as a data scientist. The style is informal, adventurous and open. It very mucThis is a great book, one that will influence me for the rest of my life as a data scientist. The style is informal, adventurous and open. It very much treats statistics as an open discipline where many approaches can "make sense", and it's all just a big playground really. This style, which is somehow common among Bayesian statisticians, speaks to me, much more than the rigid, one-correct-test-thinking that you can find in some books on statistics. Statistical Rethinking really inspired me, and the explanations and examples, particularly those in chapter 12 and 13 on multilevel models, correlations and gaussian processes, really improved my understanding of those topics.
I found a couple of issues with the book that make me not award it all five stars. I think these come from individual preferences and background, and for many this could be the perfect introduction to Bayesian modelling. In fact, I have recommended this book to many already. But, first of all, the arguments are sometimes a bit long and wordy. The writing is intellectual and creative, but I would have appreciated some succinctness here and there, perhaps some rigoruous maths in the right places. Second, I don't really like the `rethinking` package. It's not that much more difficult to write `stan` programs as a beginner, and I think experience with `stan` is more valuable. I had a couple of annoyances with the package as well, where the error messages and behaviour were confusing. At one point it took me two hours to find out that I used floats where only integers worked, but I got an error that just wasn't clear at all. I think it's not a good choice to base a book on a package with only a single maintainer, in this case, the author himself. As a last aside, the rest of the code is really old-school R, no `dplyr` or `ggplot2`. This may be the right choice by the author for others, I would have preferred the use of the `tidyverse` throughout the book.
The book is based on a course by the author, and the lectures can be watched on Youtube as well. I really enjoyed those lessons as well. All in all it was a great experience to work through this book and the exercises. ...more