<story><title>Learning About Statistical Learning</title><url>http://measuringmeasures.blogspot.com/2010/01/learning-about-statistical-learning.html</url></story><parent_chain></parent_chain><comment><author>hamilton</author><text>Thanks to liebke for posting this, and Bradford for writing it.&lt;p&gt;I have two points.&lt;p&gt;First, your point about programming is incredibly important.  I&apos;ve worked with people who had amazing insights about statistical problems, but went cross-eyed upon being asked about SVN and Git. This makes a CS homework assignment unpleasant, and a real world research project impossible.&lt;p&gt;Second, this really begs another post.  It should be called &quot;Learning how to read a textbook on your own.&quot; Successful self-learners don&apos;t just __read__ a textbook.  They toil with it, try proving things on paper themselves, work through exercises, attempt to apply it to some real-world situation, and hunt down someone who&apos;s smarter than they are to explain something that seems unclear.&lt;p&gt;Not all textbooks - certainly not every one on your list - can be read with a great application in mind.  A reader must interrogate mercilessly the book on analysis or the rigorous probability book mentioned in the post.&lt;p&gt;This seems intuitive to someone who can successfully learn on their own, but most people are not taught to do that. The difficulties of relicating a portion of the classroom learning experience is a major barrier to entry.  This is why online intro lectures for programming, math, and certain CS topics like algorithms can steer a learner in the right direction.  Stanford Engineering Everywhere and of course MIT&apos;s OCW, links to which have been posted on HN at least once a month, are great starts.</text></comment>
<story><title>Learning About Statistical Learning</title><url>http://measuringmeasures.blogspot.com/2010/01/learning-about-statistical-learning.html</url></story><parent_chain></parent_chain><comment><author>tel</author><text>I like his language toolbelt for this kind of work.&lt;p&gt;&lt;i&gt;At a minimum, I would recommend learning python (numpy/scipy), R, and at least one nice functional language (probably Haskell, Clojure, or OCaml).&lt;/i&gt;&lt;p&gt;This is effectively what I use as well. Python as a general purpose data munging library that&apos;s good for all of your dirty work whenever you need it. R for graphing, graphing, graphing, running statistical tests other people already wrote and foolproofed, database munging, and then more graphing. Haskell for prototyping and reasoning with types and then that occasional algorithm that screams for functional implementation or the not so occasional one that requires more speed than Python can provide.&lt;p&gt;I also write a few things in C/C++, though I try to avoid it. It&apos;s mostly there for standing on the backs of other people and that occasional need to blaze.</text></comment>