[MUSIC] So, let's go through a few more 
examples. 
So, if you were asked to decide how 
important a particular scientific paper 
was relative to other papers, how might 
you go about doing that? 
One way to decide between say these two 
papers here, I'll mark one with a blue 
circle and one with a red circle is to 
wait until other papers start to site 
these, and count up the number of 
citations. 
So, here you know, the paper marked with 
the blue circle has had four other papers 
bother, you know authors have bothered to 
read this paper. 
And some therefore must have had more 
impact inside of the community than the 
ones marked with a red circle. 
But if we wait any longer and that's 
indicated by the one in the darker blue 
color, if we wait even longer you might 
get even more people citing this 
intermediate paper. 
And so maybe we can conclude that well, 
over time, ultimately this one had more 
impact because it, this, this paper was 
influenced by this paper and all of these 
papers were influenced by this one. 
So, therefore perhaps we change our 
answer and that this one is more 
important. 
So, how do we decide between these two 
interpretations? 
Well this problem looks a lot like the 
problem of judging the relative 
importance of pages on the web. 
And so one thing you can do is say, well 
look, you know, a particular website is 
important, if a lot of other important 
websites point to it. 
And so here you can say a scientific 
paper is important, if a lot of other 
important papers point to it. 
And this method that Google proposed and 
implemented and ultimately led to a 
pretty significant part of their success, 
was this algorithm page rank. 
And so page rank did exactly this. 
You add up all the weights of your 
neighbors. 
And give them to yourself, and then, pass 
on that weight to everybody that you link 
to. 
And you keep going with this, until 
you've reached some convergence 
condition, and you end up with the 
relative importance of these these pages. 
Okay, and so, this is a pre, this is a 
method that comes up quite a bit. 
Whenever you have a graph, it makes sense 
to potentially run paging algorithm on 
it. 
Even though it had nothing to do with, 
ranking things on the web, which, for 
which it was originally designed. 
Okay. 
Using the same data set, you can, Carl 
Bergstrom and Martin Rosvall created this 
visualization. 
So, ignore the importance question, just 
think about the graph of the citation 
network and doing analytics on it. 
One kind of analysis you can do is 
judging importance. 
Another kind of analysis you can do is 
this, and so what this is, is over time, 
run a clustering algorithm, which I 
haven't described what that is. 
But it groups, clusters of similar 
documents to, and then, map those 
clusters into the rel, these fields. 
And so, you can determine that with some 
probab, with, with some confidence, that 
this cluster represents medicine. 
Because it has the lancet and has other 
kinds of medical journals in it. 
And you can conclude that this cluster 
has, is, represents molecular and cell 
biology. 
But what's pretty striking is that if you 
lay this out on this timeline like this, 
you can see that some fraction of the 
molecular and cell biology community. 
And the n, neurology community, started 
to combine to form a brand new field of 
science called Neuroscience. 
And so just by doing this kind of 
analytics on this graph, right, to do 
this data science on this graph, you can 
uncover the emergence of new fields of 
science. 
And so I find that pretty striking. 
And they've gone on to do many other 
kinds of analysis on this a same data 
set. 
And the overall field of this kind of 
meta field of studying the scientific 
literature in order to draw inferences is 
called bibliometrics. 
This pen is, aq little wonky, 
bibliometrics, sorry for the bad b there. 
Okay, so what can't, what is not ameanble 
to data science? 
Well, you know, you might think food, and 
this is a paper in a fairly respectable 
journal that apply, applied some 
date-driven techniques to analyzing food 
paring. 
Okay, so what they did here is pretty 
interesting, right? 
So, induce a graph on the ingredients by 
saying that if two ingredients appear 
together in a recipe, you draw an edge 
between them. 
You're graphing vertices and edges. 
And if you've never really heard, if 
you're not familiar with graphs, you will 
be by the end of the course. 
But bear with me right now. 
so connect two ingredients if they appear 
together in some recipe okay? 
Build that big graph and now you can 
analyze it in ways that are similar to 
what we just talked about. 
You can look at the community structure. 
You can find the clusters within this 
graph and see if these clusters 
correspond to the well-known methods of, 
of food pairings, okay? 
And in some cases they do, and in some 
cases they don't. 
And then, the authors sort of show that 
they've uncovered things that were not 
necessarily known, but appear to be there 
in the data, right? 
And so, this data-driven approach, you 
know, the fact that, that there's web 
sites full of recipes online. 
Has allowed us to put things on, on a 
more quantitative basis that were 
previously simply, you know old wives' 
tales, essentially, okay. 
So, I thought that was kind of a fun 
example of, of an, an unusual use of data 
science, again, in a respectable journal. 
All right? 
So another example, just from the Last FM 
blog, they looked at the tags associated 
with the songs in Last F, Last FM. 
And used it to do some simple analysis of 
the emergence of genres over time, but 
you know, based on the popularity of the 
tags for songs coming from that time. 
And so you can see things like well post 
punk in red here came after punk in 
purple, which you'd hope the graph shows 
you'd expect. 
He also uses a kind of rise in here. 
Rock and roll over time, and them maybe a 
bit of a, a, a, a dip more recently. 
And so this is kind of interesting. 
But the, the other theme here we have is, 
that we mentioned in the previous segment 
is repurposing data, right. 
So, this data was collected simply to 
help with search, you know find similar 
music, and it's not being reused to sort 
of draw inferences about the emergence 
of, of entire genres. 
Okay. 
So, another example a few of you may be 
familiar with. 
Google was able to show that, by 
analyzing the search logs, the frequency 
of search terms, it could do a better job 
predicting the severity and the, scope of 
flu outbreaks. 
Than the Centers for Disease Control, and 
by ,better, here we mean essentially 
earlier. 
Right? 
I was able to, give more a head start to 
the, the, health community. 
Okay. 
And also, it was, it was sort of more 
accurate. 
So, how do they do this? 
Well, you know when you're getting the 
flu, it turns out that you want to search 
for flu symptoms, terms associated with 
flu symptoms more often. 
And by watching that uptick, you can 
predict that there's, that the flu 
outbreak is coming. 
Okay. 
So, that's great and they put in a this 
work, and they sort of published a paper 
about it and then they put up this 
interactive visualization. 
A lot of people just sort of analyzed 
[UNKNOWN] going forward. 
But just this year, you know some folks 
showed that he didn't do a very good job 
in this last year. 
So, scientific hindsight shows that the 
Google flu trends far overstated this 
years flu season. 
And the reason they think explains this 
is that there was lots of media attention 
associated with this year's flu season, 
because it was a, was a bit of an uptick, 
and so it got amplified. 
And so this caused people to search more 
flu, flu-related terms more often, 
perhaps because their worried about 
experiencing the symptoms perhaps. 
Because they are trying to understand 
more about the flu outbreak, perhaps 
because they are searching for articles. 
Perhaps because they are worried about 
their kids more. 
But it was a second order effect, based 
on the media attention on the problem 
which lead to skewed results and 
ultimately wrong answer. 
And so the point here is this is, great 
when their re-purposing data from the 
search engine to try and make predictions 
about something else. 
But it's, it's biased its [INAUDIBLE] it 
is biased data so you have to be careful 
with, what you conclude from it. 
Okay? 
So there are limits here. 
All right. 
So, another example also, also, analyzing 
web search traffic. 
That was done with perhaps a little more 
of scientific rigor, was done by some 
folks with the Microsoft research. 
And so, here what you are looking for is 
side effects associated with particular 
drugs. 
And so these results are pretty striking. 
So, what this graph is showing that is 
over time, a set users was around about a 
million that had, that had a permission 
to sort of monitor there there web search 
traffic. 
That when you searched for this drug, in 
green, what percentage of the time did 
you also search for terms associated with 
hyperglycemia symptoms? 
And the answer is somewhere around 5%. 
For this other drug it was somewhere 
around 4%. 
For the, in the background, for the 
average case it was pretty close to 0%. 
If you searched for both of these drugs, 
the odds that you also searched for 
hyperglycemia symptoms went up to 10%. 
Okay, so what's striking about this, is 
that hyperglycemia is not a known side 
effect of these drugs. 
But it seems impossible to ignore from 
the web search data. 
Alright, there's just no reason to 
believe that this could be explained by 
coincidence. 
And they develop this argument more in 
the, in the paper then I, then I just 
have there. 
But, so fine. 
So, re-purposing data, this is another 
example, a large data sets, that had to 
use web search, 
And those are probably the two points I 
want to make about that, but a pretty fun 
one. 
Okay, so the last example I'll give is a 
different take. 
It's more about prediction then data. 
But this from last October, If you recall 
there were six Italian seismologists who 
were convicted of manslaughter, for 
failing to predict a magnitude 6.0 
earthquake in April 2009. 
And while the locals were concerned about 
the seismic activity, the researchers 
were deemed to be just too reassuring 
about the verdict. 
And so the point I want to make here is 
that, there is liability I mean, so this, 
this, this, this scientific community was 
completely aghast that this happened. 
And I'm completely aghast this happened, 
and pretty much everybody is. 
That you can imagine to hold researchers 
responsible for failing to predict 
something that is demonstrably and known 
to be impossible to predict, right? 
So, there's no seismologist on the 
planet, that would argue the earthquakes 
are maybe even remotely predictable. 
and yet the courts sort of decided that 
somehow they, you know, because they got 
the wrong answer, it's bad. 
But it does sort of bring up the issue 
that when you make a prediction, there's 
a certain amount of weight you're, you're 
going to put behind it. 
Whether, whether intentionally or not. 
And so understanding how confident you 
are about that prediction, is sort of an 
important part of the game plan. 
Okay. 
So, that was the last example. 
The, the, the themes that we saw come out 
here. 
We gave a couple examples of graph 
analytics. 
We say that databases were sort of useful 
in the Obama grounding case. 
So, a lot of examples of visualization 
and communicating these results, 
interpreting these results. 
we saw some examples of using very large 
data sets, other examples of using very 
small data sets, and not everything is 
about big data. 
A couple of bullets that aren't on here: 
We talked about, y'know, ad hoc 
interactive analysis. 
It's sort of, not just faster but 
different. 
Also supporting that is important. 
And then we talked about re-purposing 
data. 
Right. 
So, data collected by, perhaps by someone 
else for some other purpose, reusing that 
as a drawing for something else, that's a 
pretty common theme here. 
Okay. 
In the next couple of segments, we'll 
talk about how we organized this course 
and some of the design decisions we made 
in, in creating the material.