
Advanced Data Analysis from an Elementary Point of View (2017) [pdf] - mindcrime
http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
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minimaxir
I did take this class at CMU in 2012; I liked it a lot and it's good to see
the curriculum hasn't changed much since.

That said, working with highly-bespoke R packages made life crazy, and I'm
thankful that post-graduation ggplot2 was an option for R data visualization
and the development of the rest of the tidyverse packages after I graduated
let me use R now in a much less messy manner.

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audiometry
`This book does not presume that you once learned but have forgotten the
material from the pre-requisites; it presumes that you know that material and
can go beyond it. The book also presumes a firm grasp on linear algebra and
multivariable calculus, and that you can read and write simple functions in R.
If you are lacking in any of these areas, now would be an excellent time to
leave.`

ouch.

~~~
muninn_
>now would be an excellent time to leave

What does the author gain from this statement? Now, if I were to be interested
in this book, I would now be not interested and would encourage others to
avoid it.

Quite rude.

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dsacco
The author doesn’t gain anything, it’s entirely to help the reader understand
their own readiness for the material. It’s not rude, it’s straightforward and
_matter of fact_.

If you don’t meet the prerequisites, you _should_ avoid the material. Not
because the author has stated the obvious, but because you’re not prepared for
it. If you’d like you can interpret it as rudeness, but really it’s intended
to help people save time instead of waste it.

The first thing I do when I open a new textbook is flip to the preface
material to see what the prerequisites are _and_ how firm they are. Sometimes
“passing familiarity” is enough, sometimes “mathematical maturity” is enough.
But there are many treatments where that is _not_ the case, and it’s better
for everyone who’s serious about the material to be upfront about it.

~~~
muninn_
Then the correct approach is "you are likely not ready for this material, I
recommend you do XYZ and come back".

There's no problem with telling a reader they are not ready for a text - in
fact, it can save them time. The way in which you do it though can encourage
or discourage a student.

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LexAckson
The author has a pretty interesting homepage. Especially the notebooks
[http://bactra.org](http://bactra.org)

~~~
cosmic_ape
Second that. Some of these notebooks are excellent literature reviews,
highlighting really neat connections between results.

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isoprophlex
I call myself a data professional or whatever, but looking at this table of
contents I realise there is so much i can still learn!

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jxramos
This author has an amazing writing style, I just learned about the history of
the term "regression" as used in statistics. Definitely worth looking up, it
has nothing to do with curve fitting in general but was inspired by a
canonical example that first inspired drawing a fit.

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savagedata
This is my favorite statistics factoid! Regression/reversion to the mean is
the idea that if you observe an extreme value and remeasure it, it will tend
toward the average value on the second observation. For example, students who
have a very high or very low score on a test will have a more average score if
you retest them.

Sir Francis Galton demonstrated the idea of regression to the mean by
inventing linear "regression." He plotted the heights of children vs the
heights of their parents and showed that very tall parents have children who
are not as tall.

The fact that we call linear regression "regression" has nothing to do with
reverting to the mean other than, coincidentally, linear regression was first
used to illustrate the concept.

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pjmorris
From the book's website:

"This is a draft textbook on data analysis methods, intended for a one-
semester course for advance undergraduate students who have already taken
classes in probability, mathematical statistics, and linear regression. It
began as the lecture notes for 36-402 at Carnegie Mellon University."

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cosmic_ape
There is also an overview of tools for "complex systems", by the same author:
[https://arxiv.org/abs/nlin/0307015](https://arxiv.org/abs/nlin/0307015)

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eanzenberg
Love this book, it's a great intro into statistics based learning.

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justin--sane
Wow.

