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[Silence]

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>> This is CS50!

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So this is already week 11,

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which means week 12 is almost
here and thus is the end

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of CS 50, but a few things
remain on the horizon.

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So one, very excitingly,
quiz 1 is this Wednesday,

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not in here to the same
locations as last time.

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See the PDF that's linked
on the course's home page.

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Last night's review
session is already online,

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but genuinely more excitingly

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on the horizon is
the CS50 Hackathon.

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So as promised, this is going
to be an opportunity on Friday,

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December 2, which is going
to be the first evening

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of reading period, to dive
into your final project

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with the couple of hundred of
your classmates alongside you.

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We're going to charter
some harbored shuttles,

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going to drive down to Tech
Square and Kendall Square,

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which is where Microsoft
Nerd Center is,

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which is their Research
and Development Center,

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which is a very sexy place.

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And awaiting you will be scenes
like this as well as pizza

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around 8:00 p.m., Chinese food
from the Kong at 1:00 a.m.,

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and then for those
of you still awake

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at 5:00 a.m. we will get back

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in the shuttle and
take you to IHOP.

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So we will start the
RSVP process for this

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over the next week or two.

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Space is limited, so we may end
up lottering, but more details

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on that on the home page
and via e-mail before long.

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And then this is around 5:30
a.m. last year, here at IHOP.

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We got the Early
Bird Special there --

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still dark outside, you can see.

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So also on the horizon
and the culmination of all

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of CS50 is the CS50 Fair,
so this is on Friday,

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December 9 to which the
entire campus is invited,

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where a spectacle like
this will await you,

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including stress balls
and popcorn and candy

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and final projects galore.

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Think Middle School
Science Fair, but much,

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much cooler with computers.

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And so that will be again, the
climax of your experience here.

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So over the course of last week,
beyond hating some of those bugs

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in the Google Earth plug and
there were some other fun bugs,

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this screenshot was taken by one

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of our students whereby he
somehow accidentally replicated

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just one staff member on his
Google Earth; in fact, many,

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many, many times as you can
see in the 2D Earth there.

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So that was in fact a bug.

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I would be remiss if I didn't
acknowledge my own bug,

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so you'll recall that just as
our Facebook gifts came in,

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I really screwed up
a final demonstration

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of this library called jQuery,
which is meant to simplify you.

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So Javascript, in particularly
technologies like ajax,

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which again allow you to get
more data from server to client,

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even after the initial
web page has loaded.

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So this demo does actually work.

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Realize that foolishly I had
commented out one line of code

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about five minutes prior to
that demonstration, blowing up

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and commenting out an
important line of code,

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turns out is bad
for demonstration.

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So realize the code that you
do have from last week is good.

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But then I proceeded to one-up
myself further last week,

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as you may recall, with a
little demo involving sending

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text messages.

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So let's just fess up.

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I'm very content putting this
admission out on the Internet,

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so this was the while loop,
which we've been using

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since week one, ironically,

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and in this while loop we used
a library called PHPMailer,

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which allows you to create
a male object, so to speak,

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and then add an address
to it, add a body to it,

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and then call a send function,
which if you use the right types

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of e-mail addresses you
can generate text messages,

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and not e-mails per se.

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Unfortunately failure to RTFM
means that I did not realize,

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despite its very clear
name, that adding an address

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to the mail literally
adds an address;

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it does not subtract
the prior address.

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So as a result of this gaff
and specifically this line,

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some of you might have
been all too aware

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that Aaron first
got a text message,

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but then on the second iteration
of this while loop Aaron

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and Abel got a text message,
and then on the third iteration

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of this loop, Aaron and Abel
and Abigail got a text message.

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On the fourth iteration of
this loop, Aaron, Abel, Abigail

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and Adriana got a text
message, each individually.

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SO this kind of scales
up fairly quickly.

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In fact, if you do the math,
1 plus 2 plus dot-dot-dot,

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all the way up, actually
gives us the same running time

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of selection sort,
which if you recall,

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did a whole lot of operations.

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Indeed, if you add all
of these operations

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up you get a little
something in big O(n) squared.

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Big O(n) squared in a
loop with 600 students

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at 10 cents per text message,
tends to add up, so in the 5

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or 8 seconds of cluelessness
that I had here on stage

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and on camera, we sent
10,000 text messages

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to your classmates, and Aaron
has my particular apology

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since he was on the receiving
end of most of those.

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So be very careful when
using loops apparently.

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So without harping too much on
that, where are we headed today?

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Oh, and incidentally, if
you take nothing else away

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from this, too, thank God I
inserted this artificial delay

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into this loop here.

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The goal originally was just
to avoid getting blacklisted

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by a mail server, because if
you send too many mails at once,

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a lot of servers will pull
back on the reigns and say, no,

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you can't keep sending
all these messages.

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Now, in my case this actually
inserted enough of a delay

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that we only got to student
number 138, but again,

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if you add up 1 plus 2 plus
3, dot dot dot all the way

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up to 138, you get about
10,000 text messages.

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So in any case, after
CS50, which is the point

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at which we're almost at,
you can head off in all sorts

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of different directions.

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This is actually the
same chart that's

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on the back of today's handout.

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You got this way
back in week zero

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which is CS50's own
unofficial guide

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to computer science at Harvard.

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So statistically about
half of you will not go

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on to future courses
in computer science,

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and that's perfectly fine.

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Hopefully 50 in particular
will nonetheless serve you well

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as you return to your own field,
but for those of you curious

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in pursuing either a
concentration or a secondary

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or just more coursework
so that you return

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to your own field all the more
empowered with tools and ideas

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that you can solve problems
specific to your own domain,

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realize that from 50 in the
middle here, you can go off

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in all of the directions that
these areas point in here.

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And I'll defer to our
booklet here and also

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to CS50's own course shopping
tool if you'd like to read

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up more on the descriptions,
but just to help you simplify,

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realize that after
taking CS50 you can go off

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in several very specific
directions

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as soon as this spring.

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So the course list you see
here are all of the courses

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for which 50 is the
only prerequisite,

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so you have a whole menu
of options ahead of you,

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and it really does
span the gamut

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of topics in computer science.

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In general, and this is perhaps
a gross oversimplification,

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there's two main branches
in computer science.

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There's systems and there's
theory, and theory tends

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to be more a disservice
to the entire field

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of paper-pencil whereas systems
is more empirical, writing code,

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running experiments, networking
and software and the like.

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And in these several courses
can you get a taste of all

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of these various directions.

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To ward off an FAQ, rather these
here are the Fall 2012 classes,

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and these are all summarized
again in this booklet,

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and we'll put this PDF online.

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But to ward off an FAQ,
between me and all of the TFs

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and CAs we've probably taken all
possible CS courses here as well

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as some even down the road --
bottom right-hand corner --

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at MIT, so this is quite the
laundry list of courses which is

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to say most of us can
advise you on directions

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in which you might want to go.

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A more compelling FAQ -- now,
notice the contrast here --

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look at all of the course
I took, and now look

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at the courses Mark
took, if you would

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like to just take
Mark's courses,

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and Bill only took
these courses here.

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So I'll leave it to
you to infer exactly

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where computer science
can take you here.

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So these guys have
done quite well

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with their background alone.

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Oh, and finally -- and this is
actually a more sincere note,

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because what we are about to
do is introduce you to a number

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of the computer science
faculty here

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to give you a sense not
just of future courses

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that you can take
here specifically,

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but really to give you a sense
of computer science itself

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as a field, and what it means to
be in theory or systems or all

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of the various subfields
of those two big buckets.

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But here, I was going through
my transcripts from college

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and grad school -- when these
were four courses that I took

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over the course of mostly just
senior year in grad school,

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and if I'm allowed to
hop up very briefly

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on my little soapbox,
I really regret

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in retrospect not
having explored more

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when I was in college.

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Thankfully I did take 50,
for instance, sophomore year,

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and that certainly took me
off in a different direction,

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but some of my favorite
course, honestly in senior year

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and in grad school were Dramatic
Arts I, which is a survey class

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of the theater, GOV 1540,
the American Presidency,

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which is like story
time all week long

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with Professor Roger Porter,
Latina, which is me the senior

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with a whole bunch of freshman
filling a language requirement,

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but it was still interesting,
and then Anthro 1010,

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which is Introduction
to Archeology,

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where I had a little admitted
grad school life crisis,

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thinking, wow, this will be a
lot more fun digging in the dirt

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than it would be in the
computer lab, but I stayed true

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to CS there in the end.

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And this is only to say

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that I really do regret not
having explored other course

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instances in CS, or the courses
in other fields altogether.

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And the fact that so many

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of you took 50 this year
is really quite inspiring,

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because so many of
you self-describe

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as not really being bound
for computer science.

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So if you take nothing else
away this entire semester,

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do at least at one point --
and here's the soapbox --

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veer off on whatever path you're
on far sooner than senior year,

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because learning a little bit

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about a new field that's
unfamiliar can definitely change

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your life.

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And with that said, allow
me to introduce three

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of the computer science
faculty in turn:

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Professor Krzysztof Kgajos,
Professor Harry Lewis

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and Professor Hanspeter
Pfister to give you a sense

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of what might lie ahead for you.

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Professor Kgajos?

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[Applause]

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>> Well, too bad that David
stopped my presentation

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with a punch line slide.

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Anyway, my name's Krzysztof.

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I teach CS 179, the
Design of Usable

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and Useful Interactive Systems.

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I feel like very soon I'm going
to change the official title

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of the course because I'm
frankly being a little tired

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of seeing yet another slide
tweaking an application

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that helps college students
have better parties.

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These are useful things, but
there's more to the world

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than just these two things.

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So who should be
taking this class?

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This class is designed
for people who want

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to have a say in what they code.

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If all you want to do in life is
geek out and try fantastic code

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and solve fantastic
engineering problems,

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this is not the class for you.

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If you want to have a say in
what it is that your group,

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your company, your team does,

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00:10:57,696 --> 00:10:59,176
I think this is a
very useful class.

238
00:10:59,176 --> 00:11:01,356
In this class you will
be learning how to look

239
00:11:01,356 --> 00:11:02,856
at the world through
somebody else's eyes,

240
00:11:03,196 --> 00:11:05,086
how to decide what
a good problem is,

241
00:11:05,466 --> 00:11:06,866
and how to evaluate success --

242
00:11:07,136 --> 00:11:08,796
oh, and by the way,
you will also build.

243
00:11:09,976 --> 00:11:14,836
So let me say it again in
a little bit more detail.

244
00:11:14,886 --> 00:11:18,456
In this class we will spend a
lot of time observing people

245
00:11:18,456 --> 00:11:19,866
and understanding
how they function,

246
00:11:19,926 --> 00:11:22,066
and looking for problems
that they have in their lives

247
00:11:22,106 --> 00:11:24,086
that they do not even
know that they have --

248
00:11:24,586 --> 00:11:28,166
problems that are so
entrenched, so pervasive

249
00:11:28,166 --> 00:11:30,796
that we don't even realize
that they're problems,

250
00:11:30,796 --> 00:11:31,756
that they could be fixed

251
00:11:31,756 --> 00:11:33,206
and that they would be
valuable to be fixed.

252
00:11:33,906 --> 00:11:36,756
These problems are hard to find,
because you need to learn how

253
00:11:36,756 --> 00:11:40,576
to look at the world from
somebody else's vantage point.

254
00:11:42,236 --> 00:11:43,886
You will also learn how

255
00:11:43,886 --> 00:11:46,266
to invent really
creative solutions.

256
00:11:47,316 --> 00:11:50,696
You will learn how to explore a
very broad solution of possible,

257
00:11:51,066 --> 00:11:52,986
very broad space of
possible solutions,

258
00:11:53,756 --> 00:11:57,416
go beyond the obvious, and
then identify the directions

259
00:11:57,416 --> 00:12:01,546
that are most promising
to pursue.

260
00:12:02,266 --> 00:12:05,356
Again, you will learn how
to actually create designs

261
00:12:06,136 --> 00:12:08,276
and designs that are supposed
to serve somebody other

262
00:12:08,276 --> 00:12:10,736
than yourself, and in the
process you will actually learn

263
00:12:11,816 --> 00:12:12,606
what's wrong with you.

264
00:12:12,866 --> 00:12:15,556
There's something fundamentally
wrong with everyone

265
00:12:15,616 --> 00:12:17,106
who is taking a computer
science class.

266
00:12:17,926 --> 00:12:20,636
You are disqualified
from designing products

267
00:12:20,696 --> 00:12:22,886
for other people, because you
are weird, you are unusual,

268
00:12:23,516 --> 00:12:24,416
and it is very important

269
00:12:24,416 --> 00:12:26,666
to understand what makes you
different from other people

270
00:12:26,696 --> 00:12:27,896
and you will learn
it in this class.

271
00:12:29,366 --> 00:12:33,716
You will learn how to evaluate
the products that you build

272
00:12:33,716 --> 00:12:36,026
in such a way that you know
that others can use it,

273
00:12:36,606 --> 00:12:38,746
and you will know whether
you've succeeded or not.

274
00:12:39,906 --> 00:12:41,586
Finally, as I said,
you will build.

275
00:12:42,106 --> 00:12:44,446
You will be building
mobile applications,

276
00:12:46,086 --> 00:12:48,466
and you will be working in
teams, and this will be one

277
00:12:48,466 --> 00:12:49,966
of the hardest parts
of the course.

278
00:12:50,866 --> 00:12:54,536
So I'm going to give you a
few examples of the types

279
00:12:54,576 --> 00:12:58,086
of activities you will be doing
in the class, and also a few

280
00:12:58,086 --> 00:13:02,136
of the intellectual questions
that we'll answer in the class.

281
00:13:03,626 --> 00:13:06,106
So as I said, you'll
do a lot of observing.

282
00:13:06,106 --> 00:13:08,956
You will be looking for ways in
which people misuse their tools

283
00:13:09,306 --> 00:13:11,036
to find what it is
that they really need.

284
00:13:12,196 --> 00:13:15,236
You will learn techniques for
analyzing qualitative data

285
00:13:15,236 --> 00:13:17,406
so that you can actually make
sense of your observations,

286
00:13:17,556 --> 00:13:19,746
find patterns, and find things

287
00:13:19,846 --> 00:13:21,876
that were initially
hidden from you.

288
00:13:23,796 --> 00:13:26,116
You will be trying
out your ideas a lot,

289
00:13:26,116 --> 00:13:28,126
and you will be trying
them very quickly

290
00:13:28,126 --> 00:13:30,446
and you'll be trying them in
a variety of different ways.

291
00:13:31,316 --> 00:13:33,916
You will be trying to
convince yourself and others

292
00:13:33,966 --> 00:13:36,276
that you have a story for
your product, for your idea,

293
00:13:36,636 --> 00:13:38,286
that addresses a real problem

294
00:13:38,626 --> 00:13:40,086
and solves it in
a compelling way.

295
00:13:41,206 --> 00:13:44,496
You'll try out many different
solutions very cheaply before

296
00:13:44,496 --> 00:13:47,516
you actually invest
valuable resources

297
00:13:47,746 --> 00:13:49,536
into your final prototype.

298
00:13:52,476 --> 00:13:53,676
>> Vanilla Ice concert?

299
00:13:54,516 --> 00:13:56,206
Too bad it'll take eight
hours of e-mail chain

300
00:13:56,206 --> 00:13:57,556
to get the block mates together.

301
00:13:58,396 --> 00:14:00,526
>> Oh, I remember when I
used to use e-mail chains.

302
00:14:00,696 --> 00:14:01,596
>> Oh, hey, Caleb.

303
00:14:01,866 --> 00:14:03,066
What do you use instead
of e-mail chains?

304
00:14:03,856 --> 00:14:06,856
>> Check this out -- OrGroup,
the organic event planner.

305
00:14:08,606 --> 00:14:10,536
>> So that's a concert.

306
00:14:11,936 --> 00:14:18,886
>> It starts tomorrow
night at 8 p.m.

307
00:14:18,886 --> 00:14:19,636
>> Invite our block mates?

308
00:14:21,196 --> 00:14:24,286
>> Yeah, and let's call
it Vanilla Ice at Boston.

309
00:14:24,546 --> 00:14:26,346
>> Okay. We're done.

310
00:14:27,686 --> 00:14:28,776
>> That's it?

311
00:14:29,466 --> 00:14:30,286
>> That's right.

312
00:14:30,286 --> 00:14:32,056
Now we just wait to see
what our block mates think.

313
00:14:32,546 --> 00:14:34,966
>> It's somewhat whimsical.

314
00:14:35,246 --> 00:14:36,276
It's a video prototype.

315
00:14:36,406 --> 00:14:38,436
It's an example of
experience prototyping.

316
00:14:38,876 --> 00:14:41,056
These guys, once they
had a pretty good idea

317
00:14:41,056 --> 00:14:44,796
of what it is they wanted to
build, they've generated a movie

318
00:14:45,676 --> 00:14:49,156
that really in fine detail,
tried to tell a story

319
00:14:49,156 --> 00:14:50,706
of how their product
would be used.

320
00:14:50,706 --> 00:14:53,496
After watching this movie, they
and their colleagues were able

321
00:14:53,496 --> 00:14:56,416
to tell whether the idea makes
sense, does it feel right,

322
00:14:56,516 --> 00:14:59,366
does it really --
is it really going

323
00:14:59,366 --> 00:15:00,766
to make a difference
in somebody's life?

324
00:15:00,766 --> 00:15:02,026
So that's yet another activity

325
00:15:02,026 --> 00:15:03,266
that we will be doing
in this class.

326
00:15:03,736 --> 00:15:05,436
And you were laughing
at the fidelity

327
00:15:05,436 --> 00:15:07,316
of the interactive prototype.

328
00:15:07,996 --> 00:15:09,026
It was great, wasn't it?

329
00:15:09,616 --> 00:15:11,516
It took them an hour
to implement,

330
00:15:12,466 --> 00:15:14,216
but it was good enough

331
00:15:14,606 --> 00:15:19,056
to let you know how this idea
would actually work in practice.

332
00:15:20,516 --> 00:15:23,136
And then at each stage when
you create these prototypes,

333
00:15:23,436 --> 00:15:25,946
we'll be using slightly
different tools and methods

334
00:15:26,166 --> 00:15:29,146
to actually find out,

335
00:15:29,376 --> 00:15:33,046
to estimate whether these
prototypes represent something

336
00:15:33,046 --> 00:15:34,496
that will be successful
in practice.

337
00:15:35,006 --> 00:15:40,266
One way in which we'll be trying
to predict the success will be

338
00:15:40,266 --> 00:15:41,906
through peer critiques.

339
00:15:42,286 --> 00:15:45,516
Ours is the only class in
the School of Engineering

340
00:15:45,516 --> 00:15:46,676
that has studio sessions.

341
00:15:46,676 --> 00:15:49,756
You will be meeting once a week
on Fridays, presenting your work

342
00:15:49,756 --> 00:15:51,896
and offering and
receiving critiques.

343
00:15:52,326 --> 00:15:55,866
You will have to learn how to
be insightful, offer critiques

344
00:15:56,646 --> 00:15:57,546
that are to the point,

345
00:15:57,636 --> 00:16:01,496
that actually uncover something
worth uncovering, but are

346
00:16:01,496 --> 00:16:04,156
yet constructive and can help
people design better things.

347
00:16:04,156 --> 00:16:07,606
You will be generating
many ideas, so the one idea

348
00:16:07,606 --> 00:16:09,976
that you have at the
very end is really great.

349
00:16:10,586 --> 00:16:16,196
And as I said, you will build
great things, things that often

350
00:16:16,686 --> 00:16:19,066
that some students choose to
pursue even beyond the class.

351
00:16:20,686 --> 00:16:23,236
So a few examples
of the mysteries

352
00:16:23,236 --> 00:16:25,546
that we'll reveal in 179.

353
00:16:26,876 --> 00:16:30,016
We'll explore the
mysteries of creativity.

354
00:16:30,126 --> 00:16:31,176
What is creativity?

355
00:16:31,366 --> 00:16:32,276
What is creative?

356
00:16:32,746 --> 00:16:34,136
How can you be creative?

357
00:16:34,196 --> 00:16:35,856
How can you make
your team creative?

358
00:16:37,276 --> 00:16:39,056
And is lobster strudel creative?

359
00:16:41,606 --> 00:16:44,226
You will learn how
people form mental models

360
00:16:44,226 --> 00:16:47,716
of complex systems,
why they fail

361
00:16:48,326 --> 00:16:51,096
in operating complex
systems and why you will fail

362
00:16:51,416 --> 00:16:52,786
to predict how they will fail.

363
00:16:55,876 --> 00:16:59,846
You will learn whether the mouse
is ultimate pointing device

364
00:17:00,096 --> 00:17:01,086
or whether you can do better.

365
00:17:02,286 --> 00:17:03,446
This is the first mouse ever.

366
00:17:05,406 --> 00:17:08,196
We'll review the mysteries of
whether Mac is better than PC.

367
00:17:08,336 --> 00:17:13,376
We will look at how
peoples' abilities,

368
00:17:13,566 --> 00:17:15,976
peoples' motor abilities,
perceptual abilities

369
00:17:15,976 --> 00:17:19,526
and cognitive abilities
change the very moment we sit

370
00:17:19,526 --> 00:17:21,206
up and start walking.

371
00:17:21,826 --> 00:17:22,906
Are we the same person?

372
00:17:23,396 --> 00:17:25,576
I'll try to convince you

373
00:17:25,576 --> 00:17:27,116
that we're actually
very different people

374
00:17:27,166 --> 00:17:30,716
and that the way we design
mobile user interfaces is

375
00:17:30,716 --> 00:17:34,376
actually entirely inadequate and
we need a fundamental revolution

376
00:17:34,376 --> 00:17:36,546
in mobile interactive design.

377
00:17:37,436 --> 00:17:41,786
We'll also look at the issues
of esthetics, how do you design

378
00:17:41,866 --> 00:17:45,056
to elicit various
responses in your users,

379
00:17:45,056 --> 00:17:47,886
how do you convey
different moods, feelings,

380
00:17:48,836 --> 00:17:52,456
and other meanings that you
want to attach to your design.

381
00:17:52,806 --> 00:17:57,476
We will talk about how you can
manipulate peoples' perception

382
00:17:57,476 --> 00:18:00,746
of time, how you can make them
think that things happen quickly

383
00:18:00,746 --> 00:18:04,336
when in fact they
took quite awhile.

384
00:18:04,416 --> 00:18:07,876
We will talk about how you can
embed crowds in your programs,

385
00:18:08,486 --> 00:18:12,566
how you can make hundreds
of people work quietly

386
00:18:12,566 --> 00:18:16,346
in the background,
contributing pieces

387
00:18:16,346 --> 00:18:19,386
to your program's execution
in such a way that you end

388
00:18:19,386 --> 00:18:22,996
up creating interactive systems
that appear incredibly smart,

389
00:18:23,336 --> 00:18:26,946
smarter than what any current
artificial intelligence

390
00:18:26,946 --> 00:18:27,636
can accomplish.

391
00:18:28,886 --> 00:18:30,866
We'll also look at how companies

392
00:18:30,866 --> 00:18:36,386
and politicians run large-scale
automated studies every time you

393
00:18:36,386 --> 00:18:40,116
interact with their products
and how they use the results

394
00:18:40,116 --> 00:18:43,396
of these studies to redesign
their products and sites

395
00:18:44,156 --> 00:18:46,136
and tools in such
a way that you end

396
00:18:46,136 --> 00:18:49,276
up doing what they
want you to do.

397
00:18:49,406 --> 00:18:53,246
And finally, you will learn why
hippos are the most dangerous

398
00:18:53,246 --> 00:18:55,416
mammals other than
humans on the planet.

399
00:18:57,726 --> 00:19:04,056
So to take this class, you
need to have taken CS50.

400
00:19:04,056 --> 00:19:05,936
The timing has just changed.

401
00:19:05,986 --> 00:19:09,226
We have just asked the registrar
to move the lectures to Tuesdays

402
00:19:09,226 --> 00:19:11,276
and Thursdays at
10:00 in the morning.

403
00:19:12,076 --> 00:19:13,726
No longer Monday-Wednesday;
we're moving

404
00:19:13,726 --> 00:19:14,986
to Tuesdays and Thursdays.

405
00:19:15,756 --> 00:19:18,336
Apologies for this late change.

406
00:19:18,926 --> 00:19:21,346
This class has mandatory
studios on Fridays.

407
00:19:21,346 --> 00:19:24,316
We may have a late
Thursday option,

408
00:19:24,316 --> 00:19:26,326
but probably just Fridays,

409
00:19:26,766 --> 00:19:29,186
and there will be weekly
assignments mostly done

410
00:19:29,276 --> 00:19:31,826
in teams.

411
00:19:32,286 --> 00:19:37,626
In this class, I try to
subvert your understanding

412
00:19:37,786 --> 00:19:38,946
of what computer science is.

413
00:19:39,896 --> 00:19:42,666
One view of computer
science is that it's all

414
00:19:42,666 --> 00:19:45,446
about the technology, it's about
coding, it's about computers,

415
00:19:45,446 --> 00:19:48,056
it's about the green tongue.

416
00:19:48,156 --> 00:19:50,536
I believe computer science is --

417
00:19:50,906 --> 00:19:54,706
whoa, I forgot I had this
animation; where is it?

418
00:19:54,766 --> 00:19:59,276
Okay. I believe that computer
science is a fantastic

419
00:19:59,666 --> 00:20:01,396
intellectual problem-solving
toolkit,

420
00:20:01,876 --> 00:20:05,406
a problem-solving toolkit
that you can take outside

421
00:20:05,406 --> 00:20:07,496
into the real world to
solve problems [inaudible],

422
00:20:07,496 --> 00:20:11,436
and this is the message
of CS 179.

423
00:20:12,376 --> 00:20:12,816
Harry?

424
00:20:13,516 --> 00:20:19,546
[Applause]

425
00:20:20,046 --> 00:20:21,256
>> So as Harry comes
up, I just wanted

426
00:20:21,256 --> 00:20:23,526
to give a more personal
introduction.

427
00:20:23,526 --> 00:20:25,886
So Professor Lewis was my own
professor back in the day.

428
00:20:25,886 --> 00:20:28,506
I took CS 121, which is
Theory of Computation,

429
00:20:28,786 --> 00:20:32,226
which to be honest, for me was
the hardest class I ever took,

430
00:20:32,226 --> 00:20:33,376
at least up until that point.

431
00:20:33,706 --> 00:20:35,536
But this was this amazing class

432
00:20:35,536 --> 00:20:37,836
which in retrospect I
actually grew really to love,

433
00:20:37,836 --> 00:20:39,036
enough so that I ended up TF'g

434
00:20:39,036 --> 00:20:40,826
for Harry some four
or five times.

435
00:20:41,026 --> 00:20:44,136
His theory of computation,
as you'll perhaps soon see,

436
00:20:44,136 --> 00:20:47,526
is really about understanding
the fundamentals of computers,

437
00:20:47,526 --> 00:20:49,586
what you can, what you cannot
do to them, and it turns

438
00:20:49,586 --> 00:20:52,806
out there are problems out there
that simply cannot be solved,

439
00:20:52,806 --> 00:20:55,886
or at least cannot be solved in
our lifetimes computationally,

440
00:20:56,676 --> 00:20:59,176
even as much as you
might actually want to.

441
00:20:59,176 --> 00:21:02,346
And so what Harry's focus
today in particular will be

442
00:21:02,346 --> 00:21:05,396
on is a new course, CS20, which
is truly one of those courses

443
00:21:05,396 --> 00:21:07,156
that I wish existed
back in my day,

444
00:21:07,636 --> 00:21:09,296
since computer science
theory is quite often

445
00:21:09,296 --> 00:21:10,446
about proving things.

446
00:21:10,446 --> 00:21:13,106
And my only proof background
frankly was Geometry

447
00:21:13,106 --> 00:21:14,886
and like the angle/side/angle
theorems

448
00:21:14,886 --> 00:21:15,856
and those sorts of things.

449
00:21:15,856 --> 00:21:18,526
And so for me not being really
the mathematically-minded kid,

450
00:21:18,526 --> 00:21:21,086
and definitely not among
those more comfortable,

451
00:21:21,256 --> 00:21:24,366
would have loved
this basic framework

452
00:21:24,366 --> 00:21:28,166
with which you get
equipped in CS20.

453
00:21:28,166 --> 00:21:29,026
Professor Lewis?

454
00:21:29,026 --> 00:21:29,716
>> Thank you so much.

455
00:21:29,716 --> 00:21:31,766
It's nice to be back in CS50.

456
00:21:31,766 --> 00:21:34,216
CS50 started under a
slightly different name

457
00:21:34,706 --> 00:21:39,696
when I taught it I think in
'82, so I haven't gotten back

458
00:21:39,696 --> 00:21:42,176
in front of the CS50
class all that often

459
00:21:42,176 --> 00:21:43,046
but it's nice to be here.

460
00:21:43,046 --> 00:21:45,386
And I hope you all become
computer science concentrators.

461
00:21:45,806 --> 00:21:49,826
If by the way, you actually do
want to become a CS concentrator

462
00:21:49,826 --> 00:21:50,926
and you're a sophomore
and you have

463
00:21:50,926 --> 00:21:52,676
to declare your concentration
this week,

464
00:21:52,676 --> 00:21:55,006
I have office hours
all afternoon

465
00:21:55,006 --> 00:21:57,556
and you can visit
my homepage to see

466
00:21:57,556 --> 00:22:00,756
when else you can
get your form signed.

467
00:22:01,186 --> 00:22:03,946
So as David said, I'm
here to talk about CS20,

468
00:22:04,456 --> 00:22:08,526
which is called Discrete
Mathematics for Computer Science

469
00:22:08,526 --> 00:22:11,136
and is subtitled "All
the math you should know

470
00:22:11,136 --> 00:22:13,786
to do computer science that
they won't teach you in Math 1

471
00:22:13,786 --> 00:22:17,676
and Math 21 and the Calculus
and Linear Algebra sequence."

472
00:22:17,676 --> 00:22:19,716
And it's a course we never had,

473
00:22:20,486 --> 00:22:25,166
because it was always assumed
back in the days when most

474
00:22:25,166 --> 00:22:27,666
of our concentrators came
out of Applied Mathematics

475
00:22:27,666 --> 00:22:29,146
and Mathematics and Engineering,

476
00:22:29,626 --> 00:22:33,166
that people could
pick it up as they go.

477
00:22:33,646 --> 00:22:36,476
But we decided to pull it
all together in one place

478
00:22:37,056 --> 00:22:42,146
and teach it as a new course
starting in the Spring.

479
00:22:42,146 --> 00:22:44,486
Now, there's a couple of
different ways to talk

480
00:22:44,486 --> 00:22:47,116
about what this course is about.

481
00:22:47,686 --> 00:22:52,436
This is one way, which is
to throw up some topics.

482
00:22:52,436 --> 00:22:55,766
So these are terms that
may mean something to you

483
00:22:55,766 --> 00:22:58,966
or may not mean something to
you -- graphs, counting, proofs,

484
00:22:59,026 --> 00:23:02,146
logic, probability,
number theory,

485
00:23:02,146 --> 00:23:05,596
and some of them may be familiar
even though they're not familiar

486
00:23:05,596 --> 00:23:07,696
to you by that name.

487
00:23:07,926 --> 00:23:12,866
But you can go to the CS20
website and look at the list

488
00:23:12,866 --> 00:23:15,466
of topics if that's really
what you want to know.

489
00:23:15,466 --> 00:23:17,456
I suspect that's not what
you really want to know,

490
00:23:17,456 --> 00:23:19,486
and in some sense that's not
even really what the course

491
00:23:19,486 --> 00:23:20,306
is about.

492
00:23:20,726 --> 00:23:25,446
What the course is really about
is teaching you how to think,

493
00:23:26,536 --> 00:23:29,516
teaching you how to solve
problems of the kinds

494
00:23:29,516 --> 00:23:32,276
that computer scientists
come across --

495
00:23:32,276 --> 00:23:35,726
not how to write the code for
the problems but how to figure

496
00:23:36,496 --> 00:23:41,266
out how fast something will run

497
00:23:41,346 --> 00:23:47,126
or whether an algorithm
is a good one or not,

498
00:23:47,326 --> 00:23:50,516
teaching you how to
prove things formally,

499
00:23:50,516 --> 00:23:55,176
which is a really useful skill
in life but is a critical skill

500
00:23:55,586 --> 00:23:59,436
in computer science, since
very often the difference

501
00:23:59,436 --> 00:24:01,006
between code that runs and code

502
00:24:01,006 --> 00:24:05,076
that doesn't run is whether
someone has applied good logical

503
00:24:05,076 --> 00:24:09,256
reasoning to how it
works and more generally,

504
00:24:09,256 --> 00:24:10,876
as I say how to think.

505
00:24:10,956 --> 00:24:15,006
Now, so there's a whole grab
bag of tools and processes

506
00:24:15,046 --> 00:24:17,876
that we're going to try to
teach in CS20, which is going

507
00:24:17,876 --> 00:24:21,316
to get us to some interesting
pedagogy, which I'll talk

508
00:24:21,316 --> 00:24:24,036
about at the end of
my few minutes here.

509
00:24:24,616 --> 00:24:30,706
But by way of background,
this is on the critical path,

510
00:24:30,706 --> 00:24:35,476
on the prerequisite path to
courses like CS 121 and 124

511
00:24:35,476 --> 00:24:40,476
but we don't expect that many
of you, or even most of you

512
00:24:40,476 --> 00:24:43,416
who will be going on in computer
science will necessarily be

513
00:24:43,416 --> 00:24:45,626
taking CS 20.

514
00:24:45,686 --> 00:24:49,836
So in particular, if you
were on the math team,

515
00:24:49,836 --> 00:24:52,266
certainly if you were the
captain of the math team

516
00:24:52,266 --> 00:24:54,506
in high school, don't
take CS 20.

517
00:24:54,506 --> 00:24:56,786
You'll pick up what you need
to know that you don't get

518
00:24:56,866 --> 00:24:59,866
from the regular math
sequence easily enough.

519
00:24:59,866 --> 00:25:04,246
If you've taken Math 23
or Math 25 or God forbid,

520
00:25:04,246 --> 00:25:07,366
Math 55, don't take CS 20.

521
00:25:07,366 --> 00:25:11,846
You'll only intimidate
the rest of the class.

522
00:25:12,406 --> 00:25:15,206
There is a placement test,
a sort of a placement test,

523
00:25:15,206 --> 00:25:21,066
up on the CS20 website, and
there are no answers given,

524
00:25:21,066 --> 00:25:24,156
but just see if you can
recognize what's going

525
00:25:24,156 --> 00:25:25,816
on in a number of the problems,

526
00:25:25,816 --> 00:25:27,636
and if they mostly
look familiar to you.

527
00:25:27,636 --> 00:25:30,846
Even if you couldn't solve them
without doing some checking,

528
00:25:31,286 --> 00:25:32,916
you're probably beyond CS20.

529
00:25:33,226 --> 00:25:35,506
And by all means, stay away

530
00:25:35,506 --> 00:25:38,146
if you can't handle a very
well-intentioned course

531
00:25:38,196 --> 00:25:39,706
that is having startup problems,

532
00:25:39,706 --> 00:25:42,606
because anytime a new course
is offered there are going

533
00:25:42,606 --> 00:25:44,666
to be startup problems
and there'll be glitches

534
00:25:44,666 --> 00:25:47,306
and it will not be
the well-oiled machine

535
00:25:47,306 --> 00:25:49,856
that you're used to from CS50.

536
00:25:49,856 --> 00:25:54,776
Okay? So with that, let me give
you three examples of the kinds

537
00:25:54,776 --> 00:25:59,126
of problems -- I say we're
going to do problem-solving --

538
00:25:59,486 --> 00:26:02,626
problems that come up in
computer science courses.

539
00:26:02,866 --> 00:26:04,466
These are all problems
that I've taught

540
00:26:04,466 --> 00:26:06,946
in other computer science
courses that are kinds of things

541
00:26:06,946 --> 00:26:11,786
that we'll work through in CS20.

542
00:26:12,066 --> 00:26:14,546
And each of these problems
has an interesting bit

543
00:26:14,546 --> 00:26:16,626
of Harvard history
associated with it.

544
00:26:17,056 --> 00:26:18,446
So the first one is this.

545
00:26:19,226 --> 00:26:23,286
There is -- this is not
Annenberg at breakfast time.

546
00:26:23,336 --> 00:26:27,706
This is a stack of
pancakes, and it is --

547
00:26:27,706 --> 00:26:32,426
I want you to imagine
that it is your job

548
00:26:32,426 --> 00:26:36,336
as the waiter carrying the
stack of pancakes to the table

549
00:26:37,136 --> 00:26:40,226
to get the stack in order
so the biggest pancake is

550
00:26:40,226 --> 00:26:43,066
at the bottom, the smallest
pancake is on the top,

551
00:26:43,436 --> 00:26:46,596
and the rest of the
pancakes are, as we would say

552
00:26:46,596 --> 00:26:51,886
in computer science, sorted
from largest to smallest.

553
00:26:52,666 --> 00:26:56,216
And the question is, how
can you efficiently do that?

554
00:26:57,086 --> 00:27:01,356
And the answer is kind of
an interesting problem,

555
00:27:01,816 --> 00:27:05,166
because if you think about
it for a little while,

556
00:27:05,436 --> 00:27:10,176
there's a fairly simple 2n
algorithm where n is the number

557
00:27:10,176 --> 00:27:13,996
of pancakes, namely
-- well, let's see,

558
00:27:14,536 --> 00:27:16,216
there are the five pancakes.

559
00:27:16,216 --> 00:27:17,906
The fifth one is
already on the bottom,

560
00:27:17,906 --> 00:27:19,286
so we don't need to do anything.

561
00:27:19,286 --> 00:27:21,556
We can just sort the top four.

562
00:27:22,426 --> 00:27:26,076
And one thing you can do is
you can flip the top through.

563
00:27:26,076 --> 00:27:28,106
Did I say that the only
thing you're allowed to do is

564
00:27:28,106 --> 00:27:30,236
to grab a wad of
pancakes off the top?

565
00:27:30,236 --> 00:27:33,696
That's an important
constraint, so you grab a wad

566
00:27:33,696 --> 00:27:37,926
of pancakes off the top,
putting your thumb squarely

567
00:27:37,926 --> 00:27:41,026
on the middle of somebody's
pancake, and your finger's

568
00:27:41,026 --> 00:27:44,306
on the bottom of somebody else's
pancakes, and flip the wad over.

569
00:27:44,306 --> 00:27:47,876
So if you flip the top
two pancakes over here

570
00:27:47,876 --> 00:27:49,776
so that four is on the top,

571
00:27:50,276 --> 00:27:53,746
then you can flip the whole
top four over, and four will be

572
00:27:53,746 --> 00:27:56,146
on the bottom, right above
five where it belongs,

573
00:27:56,536 --> 00:27:58,206
and then you can
repeat this process

574
00:27:58,206 --> 00:28:00,286
with the remaining
three pancakes.

575
00:28:00,726 --> 00:28:03,636
So you get two flips to get
one pancake where it belongs,

576
00:28:03,636 --> 00:28:07,236
and it's going to take
about 2n flips, maybe 2n-1,

577
00:28:07,236 --> 00:28:10,256
something like that, to get
all the pancakes in order.

578
00:28:10,646 --> 00:28:12,146
So I posed this problem

579
00:28:13,366 --> 00:28:19,126
in the course that's now
called Applied Math 107,

580
00:28:19,546 --> 00:28:23,356
and one bright young man went
off and worked on it and came

581
00:28:23,356 --> 00:28:25,576
up with a better
algorithm, and let's see

582
00:28:25,576 --> 00:28:26,986
if anybody recognizes him.

583
00:28:26,986 --> 00:28:30,606
This is a photo from about
the time when he did this.

584
00:28:31,146 --> 00:28:37,836
Yes. Are you laughing
because it's a mug shot,

585
00:28:37,836 --> 00:28:40,496
or are you laughing because
you recognize who it is?

586
00:28:41,056 --> 00:28:41,206
Who?

587
00:28:41,206 --> 00:28:42,896
>> It's Bill Gates.

588
00:28:42,966 --> 00:28:43,926
>> Yeah, it's Bill Gates.

589
00:28:44,746 --> 00:28:47,586
That's Bill Gates,
looking like he's going

590
00:28:47,586 --> 00:28:49,106
to own the world
someday, doesn't he?

591
00:28:50,656 --> 00:28:51,376
Anyway, there he was.

592
00:28:51,376 --> 00:28:56,616
So he's actually got a paper
on sorting by prefix reversal,

593
00:28:56,616 --> 00:28:59,296
which is the pancake problem.

594
00:28:59,296 --> 00:29:03,746
So the pancakes are just of
course some kind of metaphor

595
00:29:03,746 --> 00:29:05,706
for some kind of
computer operations

596
00:29:05,706 --> 00:29:07,076
that you would carry
out on data.

597
00:29:07,076 --> 00:29:08,676
That's problem number
1, so that's sort

598
00:29:08,676 --> 00:29:09,546
of a counting problem.

599
00:29:09,826 --> 00:29:10,946
Here's the second problem.

600
00:29:11,836 --> 00:29:14,266
So this is the Monty
Hall problem.

601
00:29:14,266 --> 00:29:16,086
How many people have ever heard
of the Monty Hall problem?

602
00:29:16,086 --> 00:29:18,306
About half of you
-- that's good.

603
00:29:18,406 --> 00:29:20,786
But about half of
you haven't, right?

604
00:29:21,946 --> 00:29:22,836
No, those who haven't?

605
00:29:24,126 --> 00:29:25,736
A few, only a few.

606
00:29:25,736 --> 00:29:27,336
Okay, well, do this quickly.

607
00:29:28,056 --> 00:29:30,346
Monty Hall is a quiz show host.

608
00:29:30,796 --> 00:29:32,176
He's got three doors.

609
00:29:32,756 --> 00:29:36,586
There's a million
dollars behind one door.

610
00:29:36,786 --> 00:29:37,846
You're the contestant.

611
00:29:38,436 --> 00:29:40,306
He asks you to pick a
door, you pick a door.

612
00:29:40,306 --> 00:29:43,616
He opens one of the other doors
and a goat is behind there,

613
00:29:43,616 --> 00:29:46,096
and then he asks you, okay,
now that I've shown you

614
00:29:46,096 --> 00:29:48,936
where the goat is, do you
want to stick with door 3,

615
00:29:48,936 --> 00:29:52,306
which is the one you chose, or
do you want to switch to door 2.

616
00:29:53,556 --> 00:29:55,556
And what's the answer?

617
00:29:55,816 --> 00:29:57,216
>> You should switch.

618
00:29:57,416 --> 00:29:59,026
>> The answer is
you should switch,

619
00:29:59,506 --> 00:30:04,206
which a lot of very smart
people refuse to believe.

620
00:30:04,556 --> 00:30:05,826
And not only should you switch,

621
00:30:05,826 --> 00:30:09,136
but you can calculate how much
you actually should be willing

622
00:30:09,136 --> 00:30:11,966
to pay Monty for the
privilege of switching.

623
00:30:13,316 --> 00:30:16,246
This is a problem in
probability theory.

624
00:30:16,686 --> 00:30:18,866
The thing that makes this
problem particularly interesting

625
00:30:18,866 --> 00:30:21,176
to me was -- I taught
this in a course

626
00:30:21,176 --> 00:30:23,316
and then I wrote a textbook
that came out of the course,

627
00:30:23,836 --> 00:30:28,526
and because we were using Monty
Hall's name, decided to write

628
00:30:28,636 --> 00:30:31,786
to Monty Hall and ask him if
he minded if we used his name,

629
00:30:32,266 --> 00:30:34,346
and Monty Hall wrote back
the following letter.

630
00:30:34,346 --> 00:30:36,136
"Dear Larry" -- Larry
was my co-author.

631
00:30:36,746 --> 00:30:39,076
It said da-da-da, fine,
go ahead and do it,

632
00:30:39,076 --> 00:30:42,256
but I'd like to ask
you a question.

633
00:30:42,726 --> 00:30:45,096
You say the player
should switch doors.

634
00:30:45,096 --> 00:30:47,196
Now, I don't know
much about algorithms,

635
00:30:47,196 --> 00:30:48,966
but I see it wouldn't
make any difference

636
00:30:48,966 --> 00:30:51,346
after the player
having been shown,

637
00:30:51,346 --> 00:30:53,876
why should he attempt
to switch to door B?

638
00:30:53,876 --> 00:30:56,856
So it turns out Monty Hall
actually didn't understand the

639
00:30:56,856 --> 00:31:00,796
Monty Hall problem, which
is kind of an amazing,

640
00:31:01,856 --> 00:31:03,126
amazing thing to think about.

641
00:31:03,126 --> 00:31:04,676
Okay, so that's problem
number 2.

642
00:31:05,206 --> 00:31:07,176
Problem number 3 is
not really a problem.

643
00:31:07,176 --> 00:31:09,736
I just want to talk about
the abstraction of graphs,

644
00:31:10,096 --> 00:31:13,196
which we'll spend
some time on in CS20.

645
00:31:13,796 --> 00:31:14,666
There's a graph.

646
00:31:14,666 --> 00:31:17,076
Anybody know what
that is a graph of?

647
00:31:17,876 --> 00:31:19,936
That is the Facebook
social network.

648
00:31:19,936 --> 00:31:23,856
It's a graph with
about a billion nodes,

649
00:31:24,106 --> 00:31:25,616
800, something like that.

650
00:31:25,616 --> 00:31:28,316
I forget what the exact number
is supposed to be these days,

651
00:31:28,866 --> 00:31:31,546
and countless --
well, not countless,

652
00:31:31,576 --> 00:31:35,116
but a very large number of
edges connecting the nodes.

653
00:31:35,636 --> 00:31:37,746
The cities and countries
are just shown there

654
00:31:37,816 --> 00:31:39,696
for graphic effect.

655
00:31:39,776 --> 00:31:41,516
This is really just
an abstraction.

656
00:31:42,146 --> 00:31:45,926
And the interesting thing about
this and why you might want

657
00:31:45,926 --> 00:31:47,706
to think that graphs, too,

658
00:31:47,706 --> 00:31:49,526
are something worth
understanding a little bit

659
00:31:49,526 --> 00:31:54,176
about, is another little piece
of theoretical computer science

660
00:31:54,676 --> 00:31:58,706
at Harvard history,
which is this e-mail

661
00:31:59,236 --> 00:32:05,126
which I received
in January of 2004.

662
00:32:05,126 --> 00:32:09,316
Professor, I have been
interested in graph theory

663
00:32:09,316 --> 00:32:12,636
and its implications to social
networks for awhile now,

664
00:32:12,636 --> 00:32:15,966
so I did some research that
has to do with linking people

665
00:32:15,966 --> 00:32:18,176
through articles they
appear in from the Crimson.

666
00:32:18,176 --> 00:32:20,016
I thought people would
find this interesting,

667
00:32:20,496 --> 00:32:22,836
so I've set up a preliminary
site that allows people

668
00:32:22,836 --> 00:32:25,506
to find the connection
through the people and articles

669
00:32:25,506 --> 00:32:28,446
or any person that the most
frequently mentioned person

670
00:32:28,896 --> 00:32:31,346
in the time frame I looked at.

671
00:32:31,346 --> 00:32:32,356
This person is you.

672
00:32:32,356 --> 00:32:34,866
The reason this person
was me of course,

673
00:32:34,866 --> 00:32:38,866
was because I had been
Dean of the College,

674
00:32:38,866 --> 00:32:40,866
and so people were always
throwing things at me

675
00:32:40,866 --> 00:32:43,476
and it came out in the
Crimson pretty much every day

676
00:32:43,476 --> 00:32:45,086
as bad old Dean Lewis.

677
00:32:45,296 --> 00:32:48,756
That e-mail was of course,
from Mark Elliott Zuckerberg,

678
00:32:49,176 --> 00:32:53,856
who had taken my Theory
of Computation course.

679
00:32:53,856 --> 00:32:56,566
I had a very interesting
reaction.

680
00:32:56,566 --> 00:32:58,736
I said, can I see it
before I said yes.

681
00:32:58,736 --> 00:33:00,976
It's all public information,
but there's somehow a point

682
00:33:00,976 --> 00:33:03,936
at which aggregation of
public information feels

683
00:33:03,936 --> 00:33:05,976
like an invasion of privacy.

684
00:33:05,976 --> 00:33:09,386
Imagine that that was my first
reaction when I saw this idea

685
00:33:09,386 --> 00:33:13,516
with me as the central node
in the social network graph.

686
00:33:14,006 --> 00:33:16,446
So but then he showed it
to me and I said, aw, sure,

687
00:33:16,446 --> 00:33:19,796
what the hell, seems
harmless, and let him do it,

688
00:33:19,796 --> 00:33:21,616
which is my way with students.

689
00:33:21,616 --> 00:33:23,596
I'm always quite indulgent
about these things.

690
00:33:23,596 --> 00:33:26,456
All right, so those are
three little examples

691
00:33:26,456 --> 00:33:30,886
of quote unquote,
"theoretical computer science,"

692
00:33:30,886 --> 00:33:33,456
and they explain just by way

693
00:33:33,456 --> 00:33:36,576
of stories why theoretical
computer science is important

694
00:33:36,576 --> 00:33:37,626
to know something about.

695
00:33:38,106 --> 00:33:39,796
Now, a little bit
about the course.

696
00:33:39,796 --> 00:33:42,936
This is a brand new course,
and it's going to be taught

697
00:33:42,936 --> 00:33:45,996
in a new way, never
been done before.

698
00:33:46,276 --> 00:33:50,076
We are renovating a classroom,

699
00:33:50,336 --> 00:33:53,256
which Professor Kgajos is also
going to get to use, I believe.

700
00:33:53,326 --> 00:33:56,616
Maybe not -- I'm not
sure about that actually,

701
00:33:56,696 --> 00:33:58,786
but I will certainly
be using it.

702
00:33:59,056 --> 00:34:02,076
I mean, I don't have
anything to say

703
00:34:02,076 --> 00:34:03,046
about whether he gets to use it.

704
00:34:03,046 --> 00:34:04,376
I think he might
have changed his mind

705
00:34:04,376 --> 00:34:05,886
about whether he
wants to use it.

706
00:34:06,046 --> 00:34:09,676
It didn't come out right.

707
00:34:09,946 --> 00:34:11,966
So because this is a
course that's going

708
00:34:11,966 --> 00:34:15,796
to teach you problem-solving,
we're going to spend class time,

709
00:34:16,056 --> 00:34:17,616
Monday, Wednesday,
Friday at 10:00,

710
00:34:17,876 --> 00:34:20,586
mostly solving problems
in small groups.

711
00:34:21,316 --> 00:34:23,966
So we're going to have
little rearrangable tables

712
00:34:24,286 --> 00:34:25,616
with chairs around them.

713
00:34:25,936 --> 00:34:33,146
Think kindergarten, okay, but
the idea is that we want people

714
00:34:33,146 --> 00:34:36,476
to learn from each other
as well as from us,

715
00:34:37,226 --> 00:34:40,386
the activity of solving
problems.

716
00:34:40,386 --> 00:34:43,366
And the role of the staff
is going to be to go around

717
00:34:43,886 --> 00:34:48,496
and teach and coach people on
how to make progress and how

718
00:34:48,496 --> 00:34:53,536
to be persuasive that their
solutions are actually correct.

719
00:34:54,036 --> 00:34:57,286
So that's my two-bit
introduction to CS20.

720
00:34:58,136 --> 00:35:00,356
Go check out the Web site.

721
00:35:00,356 --> 00:35:04,506
As I say, it's good
background for all kinds

722
00:35:04,506 --> 00:35:05,956
of computer science courses.

723
00:35:06,226 --> 00:35:09,686
We don't have it officially in
the prerequisite chain to any,

724
00:35:09,686 --> 00:35:14,066
because for generations people
have taken those more advanced

725
00:35:14,066 --> 00:35:16,946
courses without having
this in one place,

726
00:35:17,476 --> 00:35:20,636
and if you can't figure out
from the course site whether you

727
00:35:20,636 --> 00:35:23,496
belong in the course or not,
by all means consult me.

728
00:35:23,686 --> 00:35:24,046
Thank you.

729
00:35:25,516 --> 00:35:30,546
[Applause]

730
00:35:31,046 --> 00:35:33,366
>> Lastly let me introduce
Professor Hanspeter Pfister,

731
00:35:33,366 --> 00:35:36,286
who teaches 171, a
course on visualization,

732
00:35:36,286 --> 00:35:40,126
and it's to Hanspeter that even
I often turn anytime CS50's

733
00:35:40,126 --> 00:35:43,066
about to release some handout or
some chart or some new dataset,

734
00:35:43,306 --> 00:35:44,966
we first seek to
get his blessing,

735
00:35:44,966 --> 00:35:47,076
lest we do the field
a disservice.

736
00:35:47,076 --> 00:35:48,056
So Hanspeter Pfister'.

737
00:35:49,166 --> 00:35:51,226
>> Thank you, David, and
thanks a lot of inviting me,

738
00:35:51,226 --> 00:35:53,006
giving me the opportunity
to talk to you all.

739
00:35:53,546 --> 00:35:55,886
It's great to see you all
-- wow, it's a big class!

740
00:35:56,726 --> 00:35:59,326
So I'm teaching CS
171, as David said.

741
00:35:59,326 --> 00:36:02,016
I've been teaching it for about
four years now, so this is going

742
00:36:02,016 --> 00:36:05,446
to be my fifth year, and I'm
very excited to teach this class

743
00:36:05,516 --> 00:36:07,286
because it gives me
an opportunity to talk

744
00:36:07,286 --> 00:36:09,456
about my own research,
which is visualization

745
00:36:09,456 --> 00:36:11,706
and computer graphics.

746
00:36:11,706 --> 00:36:13,586
You probably know
what visualization is.

747
00:36:13,676 --> 00:36:15,546
It's about conveying information

748
00:36:15,546 --> 00:36:19,816
through visual representations
-- so graphs, charts, maps,

749
00:36:20,836 --> 00:36:25,666
social graphs, tree maps, bubble
charts, VAT visualizations,

750
00:36:25,666 --> 00:36:27,216
such as election
maps, et cetera.

751
00:36:27,796 --> 00:36:30,446
And we're going to look at a
lot of examples, in the course,

752
00:36:30,726 --> 00:36:35,256
and one of the goals of the
class s to teach you a form

753
00:36:35,256 --> 00:36:37,656
of visual literacy that
you may not have yet,

754
00:36:37,656 --> 00:36:41,216
which is to recognize when
data is being mistreated

755
00:36:41,216 --> 00:36:42,416
with a bad visualization.

756
00:36:43,056 --> 00:36:45,136
So usually at the
beginning of the class,

757
00:36:45,256 --> 00:36:49,246
we'll do a quick
visualization critique,

758
00:36:49,636 --> 00:36:52,066
which will tell you
basically what to look

759
00:36:52,066 --> 00:36:54,866
for in a visualization, and
how you can distinguish good

760
00:36:55,076 --> 00:36:56,276
from bad visualizations.

761
00:36:56,556 --> 00:36:58,796
And that of course has
the goal of preparing you

762
00:36:58,796 --> 00:37:00,806
to create your own
great visualizations.

763
00:37:02,076 --> 00:37:03,946
Now, why is visualization
important?

764
00:37:04,696 --> 00:37:07,256
Well, first of all it
has to do with the data

765
00:37:07,256 --> 00:37:08,426
that we're generating today.

766
00:37:09,016 --> 00:37:10,616
And I don't need to
explain this to you.

767
00:37:10,616 --> 00:37:11,336
You're living it.

768
00:37:11,456 --> 00:37:14,396
You're actually probably a
prime user of data online.

769
00:37:14,926 --> 00:37:17,596
Data has become so
predominant nowadays

770
00:37:17,666 --> 00:37:19,906
that we simply can't
make sense of it anymore

771
00:37:20,156 --> 00:37:21,556
without computational means.

772
00:37:22,296 --> 00:37:24,586
And computation is
great, but computation

773
00:37:24,586 --> 00:37:25,956
and visualization
is even better.

774
00:37:26,576 --> 00:37:28,446
So we can look at
the data in a way

775
00:37:28,766 --> 00:37:33,206
that is not normally possible
with just computation.

776
00:37:34,606 --> 00:37:36,126
Now, this explosion

777
00:37:36,126 --> 00:37:39,576
of information doesn't just
happen online; it also happens

778
00:37:39,576 --> 00:37:41,846
in the sciences, and as a
matter of fact it happens

779
00:37:41,846 --> 00:37:43,146
in our physical spaces.

780
00:37:43,766 --> 00:37:44,726
So Joe Hellerstein

781
00:37:44,726 --> 00:37:48,116
at UC Berkeley calls this the
Industrial Revolution of Data.

782
00:37:48,766 --> 00:37:51,916
We have machines that
automatically generate data.

783
00:37:51,916 --> 00:37:53,436
We have RFID tags.

784
00:37:53,886 --> 00:37:56,286
We have sensors in our
roads and our bridges.

785
00:37:56,846 --> 00:37:58,936
We have new scientific
instruments

786
00:37:58,936 --> 00:38:01,886
that automatically scan
the sky or that look

787
00:38:01,886 --> 00:38:04,626
for subatomic particles,
et cetera,

788
00:38:04,626 --> 00:38:06,586
and of course we have
all the mobile devices.

789
00:38:07,056 --> 00:38:09,266
And everything around
us is collecting data,

790
00:38:09,716 --> 00:38:12,766
either with our knowledge
or without, so the amount

791
00:38:12,766 --> 00:38:16,626
of data is growing exponentially
and there is no end in sight.

792
00:38:17,606 --> 00:38:21,596
So having a way to make sense of
the data is crucial, I believe,

793
00:38:21,686 --> 00:38:24,556
in basically every job that
you could imagine today.

794
00:38:25,286 --> 00:38:28,706
Now, looking at data this
way isn't very useful.

795
00:38:29,586 --> 00:38:33,276
It's probably a good first step,
but it doesn't tell you much,

796
00:38:33,666 --> 00:38:35,716
and all you see is
a bunch of numbers.

797
00:38:36,236 --> 00:38:40,086
So visualization allows you
to look at your data in a form

798
00:38:40,086 --> 00:38:42,086
that is more digestible
for humans,

799
00:38:42,456 --> 00:38:44,116
and that is through
the visual system.

800
00:38:45,416 --> 00:38:47,576
Now, one of the main
goals of visualization is

801
00:38:47,576 --> 00:38:51,756
to analyze data, so it allows
us to dig into the data in a way

802
00:38:51,756 --> 00:38:54,886
that is not possible
without interactivity

803
00:38:54,886 --> 00:38:56,636
and without a graphical output.

804
00:38:57,356 --> 00:38:59,666
So here is an example
of the baby name wizard.

805
00:39:00,286 --> 00:39:02,326
Let me just ask, how
many of you have looked

806
00:39:02,326 --> 00:39:03,296
at the baby name wizard?

807
00:39:04,816 --> 00:39:06,476
Nobody. How many
of you have babies?

808
00:39:08,106 --> 00:39:09,306
Okay, that explains it.

809
00:39:11,506 --> 00:39:14,986
Let me tell you, once you have
a baby on the way, this is going

810
00:39:14,986 --> 00:39:17,966
to be a favorite activity
of yours and your partners,

811
00:39:19,006 --> 00:39:21,186
which is to bicker
about baby names.

812
00:39:22,606 --> 00:39:26,146
And Martin Wattenberg, who
is now at Google and who,

813
00:39:26,146 --> 00:39:29,696
in between did fabulous work at
IBM with the Many Eyes system,

814
00:39:30,096 --> 00:39:32,876
created this visualization
actually in the service

815
00:39:32,876 --> 00:39:34,676
of a book that his wife wrote.

816
00:39:34,876 --> 00:39:38,016
So the book is basically
statistical information

817
00:39:38,016 --> 00:39:40,676
about different baby
names and the frequency

818
00:39:40,676 --> 00:39:42,636
at which they are used
in the United States.

819
00:39:43,276 --> 00:39:46,566
And what you're seeing in this
visualization is I believe the

820
00:39:46,566 --> 00:39:51,406
top 1,000 baby names in the
U.S., going back to the 1880s.

821
00:39:51,856 --> 00:39:53,576
So this is going
back a long time.

822
00:39:54,116 --> 00:39:55,996
Now, this graph doesn't
look very informative.

823
00:39:56,336 --> 00:40:00,276
It's basically a stacked area
chart, and you can mouse over it

824
00:40:00,376 --> 00:40:03,586
to see the different names,
but it doesn't tell you much

825
00:40:03,586 --> 00:40:06,086
until you click at
one of the names.

826
00:40:07,066 --> 00:40:10,286
And now you're seeing
basically the frequency

827
00:40:10,446 --> 00:40:12,956
at which this name has
been popular over time.

828
00:40:13,966 --> 00:40:16,596
It's important to
look on the axes here,

829
00:40:16,596 --> 00:40:18,146
so on the right we
see the number

830
00:40:18,146 --> 00:40:22,326
of babies per 1 million
that have this name.

831
00:40:22,786 --> 00:40:25,286
And as you see, Anthony
is still pretty popular,

832
00:40:25,286 --> 00:40:26,756
maybe a little bit
on the decline.

833
00:40:27,656 --> 00:40:30,106
Now, this is an interactive
visualization,

834
00:40:30,106 --> 00:40:32,346
so we can actually
start typing here.

835
00:40:32,906 --> 00:40:34,626
So here are all the names
that start with "A,"

836
00:40:34,626 --> 00:40:37,846
and we can look for
example for Ana.

837
00:40:39,316 --> 00:40:42,646
And as we see, Ana used to
be very popular and kind

838
00:40:42,646 --> 00:40:45,796
of lost popularity and might
now be a little bit more

839
00:40:45,796 --> 00:40:46,456
on the upswing.

840
00:40:47,736 --> 00:40:49,236
So tell you a little
personal story.

841
00:40:49,236 --> 00:40:51,456
When we named our
first daughter,

842
00:40:52,086 --> 00:40:53,786
my wife's name is
Jennifer and she had

843
00:40:53,786 --> 00:40:55,476
about six Jennifers
in her class.

844
00:40:55,476 --> 00:40:56,696
It was a very popular name.

845
00:40:57,016 --> 00:41:02,676
We can actually look
at that -- Jennifer.

846
00:41:02,676 --> 00:41:05,646
So that peak, that's when
my wife went to school.

847
00:41:07,396 --> 00:41:10,326
So we wanted to give our
daughter a name that wasn't

848
00:41:10,326 --> 00:41:11,486
so popular, and we
thought, well,

849
00:41:11,486 --> 00:41:14,716
let's go to some older names,
and so we figured, well,

850
00:41:14,716 --> 00:41:16,096
Lilly, Lilly's a great name.

851
00:41:16,396 --> 00:41:17,856
So we named our first
daughter Lilly.

852
00:41:18,406 --> 00:41:23,416
And sure enough, other
people had the same idea.

853
00:41:25,486 --> 00:41:28,606
The point here is not to talk
about baby names, of course;

854
00:41:28,666 --> 00:41:31,716
the point here is to talk
about interactive visualization

855
00:41:32,016 --> 00:41:33,836
and how it's important
and useful

856
00:41:34,076 --> 00:41:35,576
if you want to analyze data.

857
00:41:35,876 --> 00:41:38,276
It's a lot more fun to
play with this application

858
00:41:38,626 --> 00:41:39,486
than to read the book.

859
00:41:39,676 --> 00:41:41,976
Actually, the best thing
is to get the application

860
00:41:41,976 --> 00:41:48,296
and the book and do both.

861
00:41:48,296 --> 00:41:50,476
Another example is from
the "New York Times"

862
00:41:50,976 --> 00:41:53,746
where you can use visualization
to reveal patterns.

863
00:41:54,386 --> 00:41:57,706
So here is one of their
many great examples

864
00:41:57,706 --> 00:41:58,646
of visualizations.

865
00:41:59,116 --> 00:42:02,276
This shows you how different
groups spend their day,

866
00:42:03,156 --> 00:42:06,966
and they collected information
that is publicly available

867
00:42:07,356 --> 00:42:09,876
about how different groups
of people spent their day.

868
00:42:10,426 --> 00:42:12,426
So you can recognize
some activity.

869
00:42:12,516 --> 00:42:15,376
So we're sleeping,
we're watching some TV,

870
00:42:15,576 --> 00:42:18,386
we might be traveling or we
might do household activities.

871
00:42:18,866 --> 00:42:22,526
Of course, every now and then we
do some work or we eat, right.

872
00:42:23,046 --> 00:42:24,866
And now we can drill
into this data.

873
00:42:24,866 --> 00:42:29,036
We can look at how employed
people spend their day compared

874
00:42:29,036 --> 00:42:31,086
to let's say, unemployed people.

875
00:42:31,086 --> 00:42:32,286
What do you expect will happen?

876
00:42:33,746 --> 00:42:37,806
Less work, right, but more TV.

877
00:42:39,066 --> 00:42:45,716
We can look for example, at the
mix of high school graduates

878
00:42:45,716 --> 00:42:48,426
versus bachelor's versus
people with advanced degrees,

879
00:42:49,166 --> 00:42:52,926
so here are high school
graduates, here are people

880
00:42:52,926 --> 00:42:54,086
with a bachelor's degree.

881
00:42:54,086 --> 00:42:56,046
They tend to work
a little bit more,

882
00:42:56,586 --> 00:42:58,996
and people with advanced degrees
tend to work even more --

883
00:42:59,616 --> 00:43:01,956
or maybe they eat
more, I don't know.

884
00:43:02,936 --> 00:43:07,236
If you don't have children,
versus if you have one child,

885
00:43:07,846 --> 00:43:10,426
you can see that you
tend to watch less TV.

886
00:43:10,636 --> 00:43:12,706
If you have two or more
children, God bless you,

887
00:43:12,706 --> 00:43:14,176
and you can't watch TV at all.

888
00:43:16,176 --> 00:43:19,646
Anyways, so this is just
a really nice way to look

889
00:43:19,646 --> 00:43:22,106
at this data and to reveal
patterns, and of course,

890
00:43:22,516 --> 00:43:25,776
I can also click on this and
get the actual distribution,

891
00:43:26,216 --> 00:43:28,946
and again, I can still
drill into it and see how

892
00:43:28,946 --> 00:43:30,316
that distribution changes.

893
00:43:32,046 --> 00:43:36,436
So revealing patterns through
interactive visualizations,

894
00:43:36,606 --> 00:43:38,736
again, something that is
not possible if you look

895
00:43:38,736 --> 00:43:40,266
at this data just
in a spreadsheet.

896
00:43:40,786 --> 00:43:46,796
And finally, visualizations are
great for presentations, right,

897
00:43:46,796 --> 00:43:50,216
so when you present something
at your future workplace,

898
00:43:50,216 --> 00:43:53,056
or even now, when you're
in school, it's important

899
00:43:53,056 --> 00:43:55,026
to have good visualizations
of your data.

900
00:43:55,796 --> 00:43:58,326
But it's also important to
tell a story with our data.

901
00:43:58,786 --> 00:44:01,986
And again, the "New York Times"
is doing a fabulous job at this.

902
00:44:02,526 --> 00:44:04,766
And I'd like to play you
a movie where they talk

903
00:44:04,766 --> 00:44:09,986
about what I would consider
an arcane baseball fact,

904
00:44:10,426 --> 00:44:13,256
which probably some of you
are recognizing it a really

905
00:44:13,256 --> 00:44:16,696
important thing, which again,
I didn't grow up with baseball

906
00:44:16,696 --> 00:44:19,926
so I may not recognize it as
that, but the visualization

907
00:44:19,926 --> 00:44:21,446
that you'll see and the story

908
00:44:21,446 --> 00:44:24,056
that you'll hear will
convince you that wow,

909
00:44:24,056 --> 00:44:26,826
this is really cool,
and just looking

910
00:44:26,826 --> 00:44:29,686
at the raw data wouldn't
actually have that effect.

911
00:44:30,456 --> 00:44:31,186
So let me play this.

912
00:44:32,256 --> 00:44:33,586
>> Mariano Rivera is one

913
00:44:33,586 --> 00:44:35,346
of the most dominant
closers in history.

914
00:44:36,076 --> 00:44:38,366
But what may be most
remarkable is that he's done it

915
00:44:38,366 --> 00:44:39,416
by confounding hitters

916
00:44:39,416 --> 00:44:42,066
with mostly one pitch,
his signature cutter.

917
00:44:42,836 --> 00:44:45,206
John Flaherty of the Yes
Network, faced Rivera

918
00:44:45,206 --> 00:44:47,876
as a hitter and also caught him
when he played for the Yankees.

919
00:44:48,306 --> 00:44:50,666
>> From a hitter's standpoint,
he's out on the mound.

920
00:44:50,666 --> 00:44:53,476
It feels like he's not even
putting any effort into it

921
00:44:53,666 --> 00:44:57,206
and the ball explodes on you,
and from a catching standpoint,

922
00:44:57,206 --> 00:44:59,146
he's the easiest
guy ever to catch

923
00:44:59,206 --> 00:45:00,896
because he throws the ball
right where you want it.

924
00:45:01,436 --> 00:45:03,966
>> Rivera uses a seemingly
effortless delivery,

925
00:45:03,966 --> 00:45:06,856
which he can flawlessly
repeat, pitch after pitch.

926
00:45:07,146 --> 00:45:09,196
His cutter is thrown very
much like a fast ball,

927
00:45:09,196 --> 00:45:11,586
but the pitch has
significant lateral movement.

928
00:45:12,056 --> 00:45:13,836
He creates and adjusts
this movement

929
00:45:14,086 --> 00:45:15,386
with the different
pressure he puts

930
00:45:15,386 --> 00:45:16,626
on the ball with his fingers.

931
00:45:17,336 --> 00:45:20,066
The pitch's lateral movement
keeps it off the bat's sweet

932
00:45:20,066 --> 00:45:23,036
spot, moving in on the hands
of a left-handed batter

933
00:45:23,036 --> 00:45:24,826
and toward the end of
the bat of a righty.

934
00:45:25,556 --> 00:45:27,706
To a hitter, Rivera's
cutter first appears

935
00:45:27,706 --> 00:45:29,926
like a straight fast
ball, making it hard

936
00:45:29,926 --> 00:45:32,586
to distinguish the two pitches
during the first fractions

937
00:45:32,586 --> 00:45:34,846
of a second when the
hitter must decide if,

938
00:45:35,146 --> 00:45:36,466
when, and where to swing.

939
00:45:37,396 --> 00:45:39,606
Hitters often rely on
reading a pitch's spin

940
00:45:39,606 --> 00:45:42,826
to determine what pitch is
coming, but Rivera's fast ball

941
00:45:42,826 --> 00:45:45,576
and cutter have what appear to
the hitter as the same spin.

942
00:45:45,966 --> 00:45:48,276
Many pitchers throw their
cutters more like sliders,

943
00:45:48,446 --> 00:45:50,686
with their fingers pulling
down on the side of the ball.

944
00:45:51,026 --> 00:45:53,206
This can create more
downward and lateral movement

945
00:45:53,206 --> 00:45:56,156
than a cutter, but it also
creates the signature spin

946
00:45:56,156 --> 00:45:58,206
of a slider, a spinning red dot

947
00:45:58,716 --> 00:46:00,996
that the hitter can
recognize and adjust to.

948
00:46:01,596 --> 00:46:04,276
With identical deliveries and
spins on Rivera's pitches,

949
00:46:04,536 --> 00:46:07,746
hitters are at a loss to
identify and then attack a pitch

950
00:46:08,276 --> 00:46:10,166
until it is too late,
and the balls end

951
00:46:10,166 --> 00:46:11,796
up in very different locations.

952
00:46:13,046 --> 00:46:15,716
Here are the nearly 1,300
pitches that Rivera threw

953
00:46:15,716 --> 00:46:18,456
in 2009, each frozen
at the point

954
00:46:18,456 --> 00:46:20,626
when the batter must
make his swing decisions,

955
00:46:21,086 --> 00:46:24,376
but with few clues to determine
the pitch's ultimate location,

956
00:46:24,756 --> 00:46:27,206
the batter can be faced with
guessing at these outcomes.

957
00:46:28,356 --> 00:46:30,856
Here are the cutters
to left-handers.

958
00:46:31,526 --> 00:46:33,186
Here are the cutters
to right-handers

959
00:46:33,866 --> 00:46:35,516
and fast balls to right-handers.

960
00:46:35,946 --> 00:46:38,136
He throws almost no
fast balls to lefties.

961
00:46:38,306 --> 00:46:41,486
As this map of his
2009 pitches shows,

962
00:46:42,006 --> 00:46:44,696
Rivera is remarkably adept
at hitting the corners,

963
00:46:45,166 --> 00:46:47,166
keeping the ball away from
the middle of the plate,

964
00:46:47,516 --> 00:46:49,866
the easiest spot for a
batter to make good contact.

965
00:46:50,486 --> 00:46:52,796
>> Looking from this
perspective it's not surprising

966
00:46:52,796 --> 00:46:55,216
that the real hot spot
is that side on a leftie.

967
00:46:55,216 --> 00:46:58,086
I think he can hit that
spot with his eyes closed.

968
00:46:58,406 --> 00:47:01,546
>> Rivera's simple but effective
formula has made him baseball's

969
00:47:01,546 --> 00:47:02,626
most dominant closer.

970
00:47:03,176 --> 00:47:07,196
>> How many of you knew what
a cutter is before this?

971
00:47:08,326 --> 00:47:10,426
Okay, a good ten
percent -- that's good.

972
00:47:11,976 --> 00:47:13,076
So consider this.

973
00:47:13,266 --> 00:47:17,316
You're reading this story in
the paper with text only, right.

974
00:47:17,316 --> 00:47:19,456
I think you'd have a
very different impression

975
00:47:19,696 --> 00:47:21,736
of what's going on, and I
probably wouldn't read it

976
00:47:21,736 --> 00:47:23,656
because I'd be bored
after the first paragraph.

977
00:47:24,806 --> 00:47:27,236
And then consider the other
alternative is you look

978
00:47:27,236 --> 00:47:28,146
at the raw data.

979
00:47:28,146 --> 00:47:31,536
The raw data of course, is
trajectories of his pitches

980
00:47:32,076 --> 00:47:35,976
over a season or more, and
just looking at that data

981
00:47:36,086 --> 00:47:37,786
in itself wouldn't
be that interesting.

982
00:47:38,326 --> 00:47:40,606
What's really making this
come alive is the story

983
00:47:40,606 --> 00:47:43,086
that they're telling around
it, and this combination

984
00:47:43,086 --> 00:47:45,686
of storytelling and
visualization

985
00:47:46,046 --> 00:47:48,506
that I think makes the
point very effectively

986
00:47:48,806 --> 00:47:49,946
and that will be memorable.

987
00:47:51,316 --> 00:47:53,306
So visualization
help you analyze,

988
00:47:53,406 --> 00:47:55,896
they help you reveal
pattern in your data

989
00:47:55,896 --> 00:47:58,726
and they help you tell
stories or present your data.

990
00:47:59,156 --> 00:48:05,166
And as Donald Norman, a famous
user interaction person said,

991
00:48:05,386 --> 00:48:06,956
it is things that make us smart.

992
00:48:07,156 --> 00:48:09,226
It goes back to the
beginning of humanity.

993
00:48:09,556 --> 00:48:12,786
We invented writing to make us
smarter, we invented graphics

994
00:48:12,946 --> 00:48:15,216
to make us smarter,
and visualization is

995
00:48:15,366 --> 00:48:17,216
on the same line of evolution.

996
00:48:17,696 --> 00:48:18,486
And why's that?

997
00:48:18,576 --> 00:48:21,286
Well, it's because we have
limited cognitive abilities.

998
00:48:21,766 --> 00:48:25,276
We can only store so much
information, and we'll discuss

999
00:48:25,276 --> 00:48:28,156
that in the course and
we'll show you many examples

1000
00:48:28,156 --> 00:48:31,706
of how the perceptual system is
very good at revealing patterns,

1001
00:48:32,096 --> 00:48:34,176
and also how sometimes
it really fails us.

1002
00:48:37,806 --> 00:48:39,616
So visualization helps us think.

1003
00:48:41,186 --> 00:48:44,846
Many of you might use
doodling or sketching

1004
00:48:44,846 --> 00:48:47,276
or mind mapping even,
to help you think

1005
00:48:47,276 --> 00:48:48,346
about certain problems.

1006
00:48:49,276 --> 00:48:51,616
It reduces the load
on the working memory,

1007
00:48:51,616 --> 00:48:53,566
which is very limited,
as I mentioned,

1008
00:48:54,156 --> 00:48:55,626
and it offloads cognition.

1009
00:48:55,696 --> 00:48:58,626
It's sort of an external
device that helps us think

1010
00:48:58,626 --> 00:49:00,116
about the data and
about problem.

1011
00:49:00,576 --> 00:49:03,216
And of course, it uses the
power of human perception,

1012
00:49:03,676 --> 00:49:06,756
so we have about 50 percent
of our brain dedicated

1013
00:49:06,756 --> 00:49:11,506
to visual processing, and
it's our most important organ,

1014
00:49:11,826 --> 00:49:15,366
and so it's just fitting that
we use it to analyze data.

1015
00:49:16,646 --> 00:49:19,306
So the goals in the
course are first of all,

1016
00:49:19,306 --> 00:49:22,566
to teach you principles of
effective visualizations,

1017
00:49:23,016 --> 00:49:27,896
and as I mentioned to give you
a visual vocabulary or a way

1018
00:49:27,896 --> 00:49:30,816
to think about good
and bad visualizations.

1019
00:49:31,746 --> 00:49:33,866
Then we'll learn
about gathering data.

1020
00:49:34,006 --> 00:49:37,586
We'll use Python as our
tool, so you'll learn how

1021
00:49:37,586 --> 00:49:40,826
to scrape Web sites
for your pleasure

1022
00:49:40,936 --> 00:49:42,926
and hopefully not illegally.

1023
00:49:44,096 --> 00:49:45,516
We'll also learn how

1024
00:49:45,516 --> 00:49:48,406
to implement interactive
visualizations using a language

1025
00:49:48,406 --> 00:49:51,156
called Processing, which
is a Java framework

1026
00:49:51,546 --> 00:49:54,656
that is relatively easy to
pick up and that allows it

1027
00:49:54,656 --> 00:49:56,716
to create interactive
visualization

1028
00:49:56,716 --> 00:49:59,126
such as you've seen in
the baby name wizard.

1029
00:49:59,956 --> 00:50:02,756
And throughout the
course, there'll be a mix

1030
00:50:02,756 --> 00:50:03,976
of programming homeworks

1031
00:50:04,036 --> 00:50:07,326
and there'll also be a final
group project where you get

1032
00:50:07,326 --> 00:50:09,306
to create your own
visualizations.

1033
00:50:09,926 --> 00:50:12,996
The group project is really sort
of the culmination of the class,

1034
00:50:13,456 --> 00:50:17,906
and we make it as open-ended as
possible, so the description is,

1035
00:50:17,976 --> 00:50:20,316
you find a problem
that's of interest to you,

1036
00:50:20,676 --> 00:50:23,806
you have to collect
data that solves some

1037
00:50:23,956 --> 00:50:28,006
of the questions you might have
for this particular problem,

1038
00:50:28,506 --> 00:50:31,276
and then you create an
interactive visualization to dig

1039
00:50:31,276 --> 00:50:33,676
into the data and to hopefully
answer your questions.

1040
00:50:35,396 --> 00:50:37,446
So let me show you
couple of examples

1041
00:50:37,446 --> 00:50:39,736
of what previous students
have done in the class.

1042
00:50:41,126 --> 00:50:42,556
First is a few screenshots.

1043
00:50:42,556 --> 00:50:45,806
So this is a project that
was done by Samir Paul

1044
00:50:45,806 --> 00:50:48,556
and Jesse Rader about
three or four years ago,

1045
00:50:48,556 --> 00:50:51,716
and it's been very popular
because it's a visual interface

1046
00:50:52,146 --> 00:50:55,636
for the queue guide, and it
allows you to filter the data.

1047
00:50:56,106 --> 00:50:59,306
You can adjust workload rating,
you can adjust the difficulty,

1048
00:50:59,746 --> 00:51:00,886
and you can find of course,

1049
00:51:00,956 --> 00:51:02,886
the scores of all the
different courses,

1050
00:51:03,356 --> 00:51:06,586
and the bar charts give you the
distribution of this course.

1051
00:51:07,246 --> 00:51:09,496
And it's great to find
the easiest courses

1052
00:51:09,526 --> 00:51:12,396
that give the highest grades.

1053
00:51:12,396 --> 00:51:15,316
Jason Gao looked
at the distribution

1054
00:51:15,316 --> 00:51:17,776
of energy consumption
on the Harvard campus.

1055
00:51:17,816 --> 00:51:19,746
Again, this is data
that is available

1056
00:51:20,176 --> 00:51:22,996
but that nobody really
looks at because it's just

1057
00:51:22,996 --> 00:51:26,076
in a spreadsheet, so by
putting the data onto a map

1058
00:51:26,496 --> 00:51:29,336
and by making the map
interactive, we could dig

1059
00:51:29,336 --> 00:51:33,786
into the data and find at
what time of year which kind

1060
00:51:33,896 --> 00:51:39,056
of facilities and houses
used what kind of energy.

1061
00:51:39,166 --> 00:51:41,536
Naveen Sinha is a foodie.

1062
00:51:41,536 --> 00:51:45,636
He's now a grad student at CES,
and he created a visualization

1063
00:51:45,636 --> 00:51:48,156
to look at different restaurants
in the Boston neighborhood,

1064
00:51:48,436 --> 00:51:50,346
and he compared the
restaurant ratings

1065
00:51:50,346 --> 00:51:54,256
of four different publications,
the Boston Magazine, the Globe,

1066
00:51:54,256 --> 00:51:55,396
the Herald and the Phoenix.

1067
00:51:55,976 --> 00:51:58,326
And as you can see, these
ratings are quite different

1068
00:51:58,326 --> 00:52:01,816
and so he normalized them and
he looked at the distribution,

1069
00:52:01,816 --> 00:52:03,146
and you can actually
look at that

1070
00:52:03,146 --> 00:52:06,506
and you can select restaurants
based on the ratings or based

1071
00:52:06,506 --> 00:52:08,456
on what you might perceive
as the best rating.

1072
00:52:09,946 --> 00:52:12,426
Karen Hansen, she is
a distance student.

1073
00:52:12,926 --> 00:52:14,466
She actually lives
in California,

1074
00:52:14,466 --> 00:52:18,706
so CS 171 is also taught through
the Extension school and we have

1075
00:52:18,706 --> 00:52:21,676
about 20 to 30 distance
students each year.

1076
00:52:22,156 --> 00:52:24,736
And she was in the Admissions
Office at UC Berkeley,

1077
00:52:24,736 --> 00:52:27,386
and she looked at the
distribution of students

1078
00:52:27,466 --> 00:52:31,206
that applied to UC Berkeley and
the distribution of how many

1079
00:52:31,206 --> 00:52:33,936
of those got accepted and where
they came from, et cetera.

1080
00:52:34,716 --> 00:52:37,786
Actually this would be fun
to do for Harvard, I believe.

1081
00:52:38,926 --> 00:52:44,696
Xiao He looked at publications
and how they change over time,

1082
00:52:44,696 --> 00:52:48,226
so he looked at publications in
the Systems Biology department,

1083
00:52:48,776 --> 00:52:51,076
and was particularly
interested to see,

1084
00:52:51,406 --> 00:52:54,766
did the publication pattern
change before or after tenure.

1085
00:52:55,416 --> 00:52:59,936
And I'll let you free to guess
what the pattern was he found.

1086
00:53:00,266 --> 00:53:03,396
And it'd be actually fun to
do this on a larger scale

1087
00:53:03,396 --> 00:53:06,786
for different departments,
such as Computer Science.

1088
00:53:07,796 --> 00:53:10,856
And lastly let me show you
David Jacopille's project,

1089
00:53:10,856 --> 00:53:12,516
which he produced last year.

1090
00:53:13,116 --> 00:53:15,836
In each of the projects
we ask the students

1091
00:53:15,876 --> 00:53:18,576
to create a little screen
cast, two-minute screen cast

1092
00:53:18,576 --> 00:53:20,146
that we show at the
end of the course.

1093
00:53:20,696 --> 00:53:22,846
And so I'll show you
David's screen cast,

1094
00:53:22,846 --> 00:53:23,936
just to give you an idea

1095
00:53:24,336 --> 00:53:26,736
of the interactive
visualization that he created.

1096
00:53:28,176 --> 00:53:29,416
>> Hi, I'm Dave Jacopille.

1097
00:53:29,706 --> 00:53:30,946
I love the New York Times,

1098
00:53:31,006 --> 00:53:34,106
but I couldn't tell you
quantitatively why, so I decided

1099
00:53:34,106 --> 00:53:35,646
to get some data
and visualize it.

1100
00:53:36,266 --> 00:53:39,656
My dataset includes 24 years
of New York Times articles,

1101
00:53:39,756 --> 00:53:43,396
from '87 to 2010, 2.2
million articles total.

1102
00:53:44,146 --> 00:53:46,126
Those articles are positioned
here on the timeline.

1103
00:53:46,126 --> 00:53:50,606
By hovering over the timeline
you can see a one-day bin

1104
00:53:50,986 --> 00:53:54,136
of all the articles that day,
particularly around 2 to 300.

1105
00:53:55,386 --> 00:53:57,976
The World Map highlights all of
the countries mentioned that day

1106
00:53:57,976 --> 00:53:59,116
and helps give an impression

1107
00:53:59,116 --> 00:54:01,036
of the global emphasis
of the times.

1108
00:54:02,176 --> 00:54:04,826
Pending across the timeline
shows Africa being the most

1109
00:54:04,826 --> 00:54:05,636
lightly covered.

1110
00:54:06,716 --> 00:54:11,066
By hovering over the
countries on the map,

1111
00:54:11,066 --> 00:54:12,066
you have a different
view of the data.

1112
00:54:12,066 --> 00:54:13,676
Here you are seeing
all of the days

1113
00:54:13,676 --> 00:54:16,356
that a country was
mentioned in the Times.

1114
00:54:17,166 --> 00:54:19,356
China is clearly
on the Times radar.

1115
00:54:19,906 --> 00:54:23,406
Nvidia was covered
about every ten days.

1116
00:54:25,236 --> 00:54:26,566
Going back to the timeline,

1117
00:54:26,996 --> 00:54:28,986
you can see there's
something plotted that's kind

1118
00:54:28,986 --> 00:54:29,626
of spiky here.

1119
00:54:30,456 --> 00:54:33,346
This is currently article count,

1120
00:54:33,346 --> 00:54:35,196
and these spikes are
the Sunday Edition.

1121
00:54:36,956 --> 00:54:38,966
Apparently it isn't
just advertisements;

1122
00:54:38,966 --> 00:54:40,426
you get twice the
articles on Sunday.

1123
00:54:41,966 --> 00:54:45,676
If we zoom out, take a
look at all 24 years,

1124
00:54:46,936 --> 00:54:48,436
you can see larger trends.

1125
00:54:48,506 --> 00:54:50,816
Article count, for instance,
is dropping a little bit

1126
00:54:50,816 --> 00:54:55,076
in the past couple of years,
but to compensate for that,

1127
00:54:55,076 --> 00:54:58,106
if you switch to words per
article you can see there's a

1128
00:54:58,106 --> 00:55:00,246
bit of an upward trend
in the words per article.

1129
00:55:00,586 --> 00:55:01,816
Articles are getting
a little longer.

1130
00:55:03,736 --> 00:55:06,426
Another interesting
discovery was positive words.

1131
00:55:06,886 --> 00:55:11,356
I made a positive word list,
and all of a sudden in '97,

1132
00:55:12,116 --> 00:55:14,836
there was a huge uptick
in positive words.

1133
00:55:15,896 --> 00:55:18,456
Maybe somebody sent out a
memo that asked everyone

1134
00:55:18,456 --> 00:55:20,906
to be more upbeat
in their reporting.

1135
00:55:21,716 --> 00:55:25,466
Finally, whatever view you're
in, there's a sidebar over here

1136
00:55:25,466 --> 00:55:27,046
that provides a lot
more details.

1137
00:55:27,046 --> 00:55:31,276
>> So I think this is
a good example of first

1138
00:55:31,276 --> 00:55:32,366
of all, collecting data.

1139
00:55:32,366 --> 00:55:34,966
He mentioned in the beginning
he collected 2 million articles,

1140
00:55:34,966 --> 00:55:36,656
which wasn't a small
feat, actually.

1141
00:55:36,656 --> 00:55:39,486
He had to do a lot of web
scraping to get that data.

1142
00:55:39,826 --> 00:55:41,316
Then he had to process the data.

1143
00:55:41,316 --> 00:55:44,116
Usually when you collect the
data it's not in a usable form,

1144
00:55:44,516 --> 00:55:47,736
so he wrote some Python scripts
to clean up the data to get rid

1145
00:55:47,736 --> 00:55:50,226
of filler words, of
articles, et cetera,

1146
00:55:50,646 --> 00:55:53,006
and finally he created
this visualization.

1147
00:55:53,386 --> 00:55:57,046
And I think it's a nice mix
of interactivity and sort

1148
00:55:57,046 --> 00:55:58,546
of insights in filtering.

1149
00:55:59,126 --> 00:56:02,896
So CS 171 is online.

1150
00:56:02,896 --> 00:56:05,616
You can go to CS171
dot org to see more,

1151
00:56:05,726 --> 00:56:07,286
and you find a syllabus there

1152
00:56:07,746 --> 00:56:10,066
and the last year's
schedule of lectures.

1153
00:56:10,676 --> 00:56:12,596
In the spring it
will be very similar

1154
00:56:12,596 --> 00:56:13,876
to what you're finding
there now.

1155
00:56:14,626 --> 00:56:18,076
Just to give you a list of the
topics here, so we're starting

1156
00:56:18,076 --> 00:56:19,486
out with some fundamentals,

1157
00:56:19,566 --> 00:56:23,046
talking about the perceptual
system and what makes good

1158
00:56:23,046 --> 00:56:24,456
and bad visual encodings.

1159
00:56:25,016 --> 00:56:27,316
Then we talk a little bit
about interaction techniques.

1160
00:56:28,086 --> 00:56:30,316
And then we start to talk
about different methods

1161
00:56:30,696 --> 00:56:35,006
to visualize statistical graphs,
maps, trees and networks,

1162
00:56:35,496 --> 00:56:37,376
and of course,
higher-dimensional data.

1163
00:56:38,326 --> 00:56:39,966
I should actually
mention to Harry,

1164
00:56:39,966 --> 00:56:43,166
the Facebook data you showed,
which is a wonderful example

1165
00:56:43,166 --> 00:56:46,636
of network visualization,
didn't actually have any plot

1166
00:56:46,636 --> 00:56:47,526
of the countries on it.

1167
00:56:47,886 --> 00:56:50,466
This was just the raw Facebook
data, and the amazing thing is

1168
00:56:50,466 --> 00:56:53,576
that the countries pop out
visually because of course,

1169
00:56:53,576 --> 00:56:55,596
the data is distributed
on the land masses.

1170
00:56:56,546 --> 00:56:59,436
So we end up with a
few guest lectures.

1171
00:56:59,436 --> 00:57:01,976
I usually have a great
line-up of guest lectures.

1172
00:57:02,406 --> 00:57:05,796
Ben Fry, the creator of
Processing, Martin Wattenberg,

1173
00:57:05,796 --> 00:57:08,056
whose baby name wizard you
just saw, and his partner,

1174
00:57:08,056 --> 00:57:10,016
Fernanda Viegas, they're not

1175
00:57:10,016 --> 00:57:12,156
at Google doing great
visualization work there,

1176
00:57:12,546 --> 00:57:15,176
Bang Wong from the Broad
Institute and Janet Iwasa,

1177
00:57:15,216 --> 00:57:18,436
talking about biological
visualization, et cetera.

1178
00:57:18,486 --> 00:57:22,526
So we end up with a
really nice overview

1179
00:57:22,526 --> 00:57:25,096
of what visualization
practitioners do currently

1180
00:57:25,286 --> 00:57:26,116
in their daily work.

1181
00:57:26,636 --> 00:57:28,696
And I believe that's it.

1182
00:57:28,696 --> 00:57:31,666
Oh, I should mention the
course takes place Tuesday

1183
00:57:31,666 --> 00:57:36,806
and Thursday, 2:30 to 4:00 p.m.,
and we have sections on Fridays

1184
00:57:37,276 --> 00:57:39,326
that you are highly
encouraged to attend; however,

1185
00:57:39,326 --> 00:57:41,696
they're not mandatory,
and during sections

1186
00:57:41,696 --> 00:57:43,636
on Fridays we usually
talk about some

1187
00:57:43,636 --> 00:57:45,206
of the implementation details.

1188
00:57:45,656 --> 00:57:47,276
For example, if you
don't know Python,

1189
00:57:47,276 --> 00:57:51,146
we try to give introductions
to Python or Processing

1190
00:57:51,146 --> 00:57:53,736
or other tools that you
might use through the course.

1191
00:57:54,706 --> 00:57:55,446
Thank you very much.

1192
00:57:56,516 --> 00:58:01,706
[Applause]

1193
00:58:02,206 --> 00:58:04,096
>> So it really is quite
remarkable, to be honest,

1194
00:58:04,096 --> 00:58:05,566
how much the CS Department
has evolved,

1195
00:58:05,566 --> 00:58:06,546
even since when I was here.

1196
00:58:06,546 --> 00:58:10,596
Back in my day you took CS50
then 51 then 121 then 124 then

1197
00:58:10,596 --> 00:58:13,826
141 -- there was very
much a linear path

1198
00:58:13,826 --> 00:58:15,146
through the department,
but as you saw

1199
00:58:15,146 --> 00:58:16,876
at the beginning today
there's so many directions

1200
00:58:16,876 --> 00:58:19,206
in which you can go, even if
you're not looking to major

1201
00:58:19,206 --> 00:58:21,096
or minor in computer science.

1202
00:58:21,096 --> 00:58:23,336
As our three presentations
today here suggest,

1203
00:58:23,556 --> 00:58:25,866
it really is applicable
to all sorts of domains.

1204
00:58:25,866 --> 00:58:27,746
And for all of those
other courses,

1205
00:58:27,746 --> 00:58:29,416
realize that we will
post later today

1206
00:58:29,416 --> 00:58:32,716
at CS50 dot net slash lectures,
five or ten-minute clips

1207
00:58:32,716 --> 00:58:34,616
from a whole bunch of other
CS courses, if you would

1208
00:58:34,616 --> 00:58:35,926
like to shop those virtually.

1209
00:58:36,346 --> 00:58:38,156
In the meantime, tonight
we have office hours;

1210
00:58:38,156 --> 00:58:39,006
tomorrow night as well.

1211
00:58:39,006 --> 00:58:42,016
Wednesday is quiz 1 and next
Monday is your very last CS50

1212
00:58:42,016 --> 00:58:43,166
lecture ever.

1213
00:58:43,486 --> 00:58:45,456
We will see you think.

1214
00:58:45,456 --> 00:58:47,546
Ah, this so sweet!

1215
00:58:47,866 --> 00:58:50,606
See you then!