Updike’s Rabbit, Poincare, and the Art of Honest Writing

Cover of "Rabbit, Run"
Cover of Rabbit, Run

As I am reading Rabbit, Run, I am slowly recognizing the literary genius of John Updike and I can not help but to draw parallels to the artists of the second kind — mathematicians.  Updike does not use the tricks of literary construction that are so prevalent in the popular literature and modern blog writing.  There is nothing wrong with clever literary construction of course.  It makes the pages turn, it draws you in and leaves you asking for more.  If you have read John Grisham’s Time to Kill (his first and best novel, I think), you know what I am talking about.  The problem is that this kind of prose gets tiring after a while as you sort of feel like the author is consciously tricking you.

Not so with Updike.  His storyline is quite ordinary as are his characters.  He does not leave you hanging at the end pages and paragraphs.  He simply tells.  The beauty of his writing, it seems to me, is that the prose itself is so cleverly nuanced, yet so vivid, that it infuses extraordinary qualities into ordinary events and actors.  For example, from Rabbit, Run, describing a foreplay with a plump prostitute:

As swiftly, he bends his face into a small forest smelling of spice, where he is out of all dimension, and where a tender entire woman seems an inch away, around a kind of corner.  When he straightens up on his knees, kneeling as he is by the bed, Ruth under his eyes is an incredible continent, the pushed-up slip a north of snow.

When reading Updike, the reading itself is an incredible experience, a total escape into the Updike dimension that is as insightful as it is unique.  This kind of prose seems completely out of reach for mere mortals who need to resort to literary tricks.

Cover of "The Value of Science: Essential...
Cover via Amazon

I get a similar feeling when reading Henri Poincare’s The Value of Science (in English translation) in that his understanding of mathematics is so deep that it feels almost untouchable, yet he simply tells without the drama of other popularizers of science like say Hawking (a brilliant man) or Mlodinow (also no slouch.) Not to be outdone by the literary types, Poincare’s narration is so beautiful that it makes me want to learn French just to read him in the original.  Here is Poincare on the nuances of Number Theory:

He is a savant indeed who will not take is as evident that every curve has a tangent; and in fact if we think of a curve and straight line as two narrow bands, we can always arrange them in such a way that they have a common part without intersecting

And here he is again on the scientific motivation.

The scientist does not study nature because it is useful; he studies it because he delights in it, and he delights in it because it is beautiful.  If nature were not beautiful, it would not be worth knowing, and if nature would not be worth knowing, life would not be worth living.

It was Poincare who noted that:

A scientist worthy of his name, above all a mathematician, experiences in his work the same impression as an artist; his pleasure is as great and of the same nature.

The curious intersection of art and science has been noted by many.  The fact that science has its own aesthetic beauty is not a byproduct of the scientific method.  As Poincare so eloquently points out, it is the reason for its existence.

A Better Way to Learn Applied Statistics, Got Zat? (Part 2)

Earning a PhD for DummiesIn the second semester of grad school, I remember sitting in a Statistical Inference class watching a very Russian sounding instructor fast forward through an overhead projected PDF document filled with numbered equations and occasionally making comments like: “Vell, ve take zis eqazion on ze top and ve substitude it on ze butom, and zen it verk out.  Do you see zat ?”  I did not see zat.  I don’t think many people saw zat.

In case I come off as an intolerant immigrant hater, let me assure you that as an immigrant from the former Soviet block, I have all due respect for the very bright Russian and non-Russian scientists who came to the United States to seek intellectual and other freedoms.  But this post is not about immigration, which incidentally is in need of serious reform.  This is about an important subject, which on average is not being taught very well.

This is hardly news, but many courses in Statistics are being taught by very talented statisticians who have no aptitude or interest in the teaching method. But poor instructors are not the only problem.  These courses are part of an institution, an institution that is no longer in the business of providing education.  Universities predominantly sell accreditation to students, and research to (mostly) the federal government.  While I believe that government-sponsored research should be a foundation of modern society, it does not have to be delivered within the confines of a teaching institution.  And a university diploma, even from a top school (i.e. accreditation), is at best a proxy for your knowledge and capabilities.  For example, if you are a software engineer, Stack Overflow and GitHub provide much more direct evidence of your abilities.

With the cost of higher education skyrocketing, it is reasonable to ask if the traditional university education is still relevant?  I am not sure about medicine, but in statistics, the answer is a resounding ‘No.’  Unless you want to be a professor.  But chances are you will not be a professor, even if you get your coveted Ph.D.

So for all of you aspiring Data Geeks, I put together a table outlining Online Classes, Books, and Community and Q&A Sites that completely bypass the traditional channels. And if you really want to go to school, most Universities will allow you to audit classes, so that is always an option. Got Zat?

Online Classes Books Community / Q&A
Programming Computer Science Courses at Udacity. Currently Introduction to Computer Science, Logic and Discrete Mathematics (great for preparation for Probability), Programming Languages, Design of Computer Programs, and Algorithms.

For a highly interactive experience try Codecademy.

How to Think Like a Computer Scientist ( Allen B. Downey)

Code Complete (Steve McConnell)

Stack Overflow
Foundational Math Singel Variable Calculus Course on Coursera (they are adding others; check that site often)

Khan Academy Linear Algebra Series

Khan Academy Calculus Series (including multivariate)

Gilbert Strang’s Linear Algebra Course

Intro to Linear Algebra (Gilbert Strang)

Calculus, an Intuitive and Physical Approach (Morris Kline)

Math Overflow
Intro to Probability
and Statistics
Statistics One from Coursera. This course includes an Introduction to R language.

Introduction to Statistics from Udacity.

Stats: Data and Models (Richard De Veaux) Cross Validated, which tends to be more advanced
Probability and Statistical
Theory
It is very lonely here… Introduction to Probability Models(Sheldon Ross)

Statistical Inference (Casella and Berger)

Cross Validated
Applied and Computational
Statistics
Machine Learning from Coursera.

Statistics and Data Analysis curriculum from Coursera.

Statistical Sleuth(Ramsey and Schafer)

Data Analysis Using Regression and Multilevel Models (Gelman)

Pattern Recognition and Machine Learning (Chris Bishop)

Elements of Statistical Learning (Hastie, Tibshirani, Friedman)

Stack Overflow especially under the R tag

New York Open Statistical Programming Meetup, try searching Meetups in your city

Bayesian Statistics Not to my knowledge, but check the above-mentioned sites. Bayesian Data Analysis (Gelman)

Doing Bayesian Data Analysis (Kruschke)

I don’t know of any specialized sites for this.