Computer science ? - Pradipta Sir

Yale Daily News
Published: Monday, January 12, 2009
Kosslyn: Elegance, not tech support
By Justin Kosslyn
The World Beautiful
Computer science is tragically misunderstood. Popular opinion notwithstanding, my
major is not preprofessional.
It does not teach how to fix a printer — though I sometimes wish it did — nor does it
explain why my laptop keeps making disquieting noises. Despite three and a half years of
classes, I have yet to learn how to create a slick Web site or upgrade a hard drive, hack
into servers or configure a firewall. It’s not my fault — none of those topics are in the
computer science curriculum.
What, then, you may wonder, is computer science? It is the study of the structures and the
construction of elegant systems.
Let’s flesh this out. Take Google’s search algorithm as an example. (Full disclosure: I
interned at Google last summer, am currently the campus representative, and will be
returning there next year.) Why did Google beat out AltaVista and all the other early
search engines? Simple: by examining the structure of the World Wide Web.
There is no magic or mystery to the Web’s basic structure, which consists of pages
connected with hyperlinks. If you are reading this online, for example, on the top left of
the page is a large hyperlink to the YDN’s home page. Google’s innovation was to realize
that no page exists in a vacuum, and that by paying attention to the structure of the
hyperlinks — to what was linking where — searches would yield more promising results.
The key to better results was attention to a Web site’s salience — its prominence online
— which is represented as a number from one to 10, known as PageRank. For example,
as of the time of writing, huffingtonpost.com has a PageRank of eight (high salience),
yaledailynews.com has a PageRank of seven, www.yale.edu/rumpus has a PageRank of
five, and thecrimson.com has a PageRank of four (as if we needed Google to tell us that
the YDN is far superior to the Crimson).
Sometimes hyperlinks change. For example, last spring huffingtonpost.com linked to a
number of YDN articles about Aliza Shvarts ’08. Huffingtonpost.com was thereby
effectively endorsing yaledailynews.com. Hundreds of other high-profile external sites
have also linked to the YDN, ranging from Wikipedia to Sports Illustrated. Each of these
mini-endorsements boosts YDN’s PageRank, resulting in the high ranking. And when the
YDN links to another site, some of the YDN’s high PageRank carries over.
You may be surprised by how straightforward this all is. This was a patent-worthy
(number 6,285,999) computer science breakthrough with large practical implications, but
the core of it is exceedingly simple: When important sites link to a new site, the new site
is probably important too. Thus, the quality of Google’s search results arises from the
computer science perspective — understand structure, build elegant systems — applied to
the task of Web searches.
But it is shopping period, and you have a more pressing question at hand: What courses
should you shop? Computer science also has something to say about this kind of
dilemma.
If you really wanted the optimal course list, you might proceed as follows. First,
enumerate aspects of each course: class size, ratings from past years, classroom activities
(discussions, movie screenings, lectures, visiting experts etc.), and so on. Then list out
every course you have taken so far, recording every aspect of each course as well as
whether you enjoyed it.
You — or a computer — are now in a position to build a model of your likes and dislikes,
as well as to understand which dimensions matter more. You may learn, for example, that
having movie screenings significantly boosts your enjoyment of a course while visiting
experts make little difference.
Finally, you can use this understanding to make predictions for the future. By comparing
the predictions against reality, the model can be further refined. In this way you are using
the computer science approach: seeking to understand structure and build an elegant
system to help solve an immediate problem.
This approach can be applied to a wide range of classification problems. For my senior
project, for example, I used a large government database to examine the characteristics of
people at risk of alcohol abuse.
Even if you have no interest in majoring in computer science, a bit of that perspective is
valuable. History, political science, the harder sciences and even literature are replete
with implicit structures that computer science can help frame.
So when you consider the structure of your curriculum this semester, consider computer
science. If you already have a little programming experience — such as programming
your calculator in high school and running analyses in MATLAB or Stata — your course
is Introduction to Computer Science. If you are considering putting a toe in the water for
the very first time, try Introduction to Programming.

And if you need to fix your printer, try calling tech support.

Justin Kosslyn is a senior in Ezra Stiles College.