
Computer Age Statistical Inference: Algorithms, Evidence and Data Science - Schiphol
https://web.stanford.edu/~hastie/CASI/index.html
======
kristjansson
Tangential: for anyone reading the PDF,

    
    
      pdfcrop --verbose --margins "20 30 20 30" --bbox "110 180 440 740" casi.pdf
    

trims the over-large margins without clipping any content.

[http://manpages.ubuntu.com/manpages/precise/en/man1/pdfcrop....](http://manpages.ubuntu.com/manpages/precise/en/man1/pdfcrop.1.html)

~~~
kranner
For these parameters, the latest pdfcrop (v0.4b) dies with the following
errors:

Error! Bounding Box borders imply page width of zero. Error! Bounding Box
borders imply page height of zero.

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graduation
For those who know, How does this book differ from Foundations of Data Science
by Blum/Hopcroft/Kannan _?

_[http://www.cs.cornell.edu/jeh/book%20June%2014,%202017pdf.pd...](http://www.cs.cornell.edu/jeh/book%20June%2014,%202017pdf.pdf)
?

~~~
Real_S
After looking through the Contents of both:

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.

Both great books, thanks for the links!

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awblocker
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.

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uptownfunk
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!

~~~
dapreja
Could you tell me how exactly did you land (and what it is) your current job,
and from what previous possition did you come from?

~~~
uptownfunk
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.

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joshvm
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)

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neves
How much math must one know to be able to read this book? Is this an
introductory book?

~~~
efm
It assumes you know a fair bit. McElreath's _Statistical Rethinking_ is a
better introduction: [http://xcelab.net/rm/statistical-
rethinking/](http://xcelab.net/rm/statistical-rethinking/)

~~~
digitalzombie
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.

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brudgers
direct link to pdf,
[https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf](https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf)

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gbrown
Awesome! I love the recent trend of making these books freely available.

~~~
gaius
This is good too:
[http://genomicsclass.github.io/book/](http://genomicsclass.github.io/book/)
also at
[https://leanpub.com/dataanalysisforthelifesciences](https://leanpub.com/dataanalysisforthelifesciences)

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digitalzombie
Efron is the creator of bootstrap.

Hastie iirc is one of the two responsible for LASSO, Ridge, and I think
elasticnet.

~~~
dcl
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.

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martingoodson
I can really recommend this book. It's an enjoyable read and is very
pragmatic. A useful reference for practitioners.

~~~
wenc
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.

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petters
Now let's hope that we can start using these methods more in science instead
of e.g. p-values.

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gaius
Effect size _in addition to_ p-value, not instead of...

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Houshalter
No there are bayesian replacements for p values as well:
[https://replicationindex.wordpress.com/2015/04/30/replacing-...](https://replicationindex.wordpress.com/2015/04/30/replacing-
p-values-with-bayes-factors-a-miracle-cure-for-the-replicability-crisis-in-
psychological-science/)

