[MUSIC]. 
Welcome back. 
So I want to talk a little bit about how 
the term "data science" relates to other 
fields of science. 
And in particular, I want to introduce 
this term eScience, which through first 
approximation you can think of as 
equivalent to Data Science. 
So while the eScience is associated with 
Astronomy and Oceanography and Biology 
data science has been adopted more in 
business. 
But they involve a lot of the same 
concepts. 
So let me tell you what's going on in 
Science. 
So for thousands of years, you know, 
scientific inquiry has been empirical, 
right. 
You observe the natural world, or maybe 
in some cases replicate the natural world 
in a controlled environment in a 
laboratory. 
And make observations about that. 
In the last few hundred years science has 
accepted theoretical models as a valid 
method of inquiry. 
One that is reinforcing empirical methods 
so you know new theories suggests new 
experiments and the theories help explain 
the observed data that you get from the 
experiments. 
In the last 50 years or so high speed 
computation has emitted an entirely new 
method of scientific inquiry. 
Soon you can simulate in the computer 
phenomena on that otherwise couldn't be 
re, you couldn't, you can't observe 
directly. 
And you can't reproduce in the lab and 
even the theoretical models become too 
complex to solve analytically using, you 
know essentially paper and pencil. 
Right, but you can actually start from 
initial conditions and run the simulation 
to get a result. 
So this is maybe what goes on in the 
interior stars or the shift of tectonic 
plates or the evolution of the universe 
or the effects of the ecology on some 
species dying out, and so on. 
So that's fine. 
That's three methods of inquiry. 
But in the last ten years or so, there's 
been arguably a fourth method of 
scientific Inquiry, which is to acquire 
massive data sets from instruments or 
from simulations. 
And then explore these data sets using 
new algorithms and infrastructure. 
And so eScience is really about massive 
and complex data, data large enough to 
require you know, automated or 
semi-automated analysis. 
You can't look at it, you can't inspect 
it directly, okay. 
And so the relevant tools here are the 
same as those for data science, you know, 
databases, visualization scale out 
computing. 
Maybe the new sequel systems. 
Machine learning techniques. 
Web services and so on. 
Okay. 
So the way, that, this, this, idea of the 
fourth paradigm, there's a book that's in 
the reading list that you can refer to 
here. 
And there's a lot of, just some other 
articles in your reading list you can 
also refer to. 
The story has been told lots of ways. 
The way I like to talk about this story 
is that science has always been about 
asking questions but conventionally it 
was really about querying the world, 
right? 
You would, sort of, have data acquisition 
or activities, experiments or field 
studies. 
They will couple to very specific 
hypothesis where you have the question in 
mind first and we click the data. 
But eScience has really shifted a bit 
where now you're kind of downloading data 
on mass, you're downloading the world 
first putting some sort of representation 
in the computer. 
And then creating that database to test 
your hypothesis and so it's the, the data 
can be acquired independent of any 
specific hypothesis in some case. 
Okay. 
And this is due in part to the cost of 
data acquisition dropping precipitously, 
thanks for advances in technology, right. 
So the telescopes you can build now, that 
we'll talk about in the next couple of 
slides, can acquire at enormous amounts 
of data at very high resolution. 
And in the life sciences, you have sort 
of laboratory automation and sort of 
high-throughput sequencing. 
In oceanography the sensors are getting 
cheaper. 
The models thanks to advances and thanks 
to more's laws. 
And thanks to advances in computing. 
The simulations you can run are getting 
bigger and higher resolution. 
And producing, and therefore producing 
larger, and larger amounts of data. 
And so on, and so you know, the rate at 
which data can be produced has far out 
paced the rate that we can analyze it or 
come up with the questions we need to ask 
about it. 
Okay. 
And this suggested a new approach to 
science. 
So let me give some examples, so we said 
that eScience is driven by data more than 
by the computation. 
Alright so some examples on the size of 
the data is that's coming on. 
The Apache point telescope that was the 
primary instrument for the Sloan Digital 
Sky Survey that we might refer to 
multiple times in this course. 
Produced 80 terabytes of raw new data 
over, a seven year period. 
You know, at the time, this is a pretty 
significant data size, and even by many 
standards is still today. 
The next generation of this the next 
generation project that's in the same 
sort of spirit as Sloan Digital Sky 
Survey is the large synoptic survey 
telescope. 
So this guy can produce 40 terabytes per 
day. 
it will do so for over a ten year period. 
So in total 100 plus petabytes and 
producing a single amount of data that's 
soon the skies will be produced in over a 
total entire lifetime. 
It can be produce that over every two 
days. 
Okay. 
And so this is a pretty staggering amount 
of data, and requires a, a pretty 
different approach. 
One thing I want to mention maybe about 
Sloan Digital Sky Survey, what they 
actually did here, was to take the 
images, cook them, right, extract the 
relevant objects from it, put all those 
objects into a, an off-the-shelf or a 
relational database. 
In fact, it was Microsoft SQL server, and 
critically host this database online and 
serve it out over the web, and this 
require a pretty significant investment 
in infrastructure. 
But as a result f doing this, of making 
all the data public and queryable, it 
became the most productive astronomy 
facility in history. 
Right, so the number of papers that have 
been produced on this data is on the 
order thousands. 
In the original, you know, PIs of the 
project, the principal investigators of 
the project had, sort of, maybe only 100 
papers in mind for the data. 
And the other 4900 papers that have been 
written all came from external partners 
writing queries against this database. 
So it's just a wild, wild success. 
Now, the problem is, is that the same 
technology stack in, into some extent 
even the same approach. 
It is difficult to apply in this case of 
large synoptic survey telescope. 
The reason why this guy is producing so 
much more data is not just because it's 
much higher resolution and it can 
perceive a much deeper field in the sky. 
But also because it's returning to the 
same point in the sky frequently, every 
three days, and so this allows you to 
look at things change over time. 
So Asteroids, comets, you might catch, 
super novas and so forth. 
Okay, and by comparing these images in 
the time series, there, there's all sorts 
of new questions you can ask. 
Okay. 
So, both because of the science that 
they're going to do. 
And because of the sheer scale. 
and because of some of the complexity of 
the, details of how the data is acquired. 
The Existing, the previous solution won't 
work. 
And so, this is motivated in a whole new 
area of research to study, data 
management techniques and data analysis 
techniques to support this project. 
So in Life Sciences, these high 
throughput sequencers are capable of 
producing, you know, terabytes per day 
when run continuously. 
And you know, major labs that do this 
work, such as the Joint Genome Institute, 
have 25 to 100 of these machines running 
all the time, alright. 
So this is spitting out an enormous 
amount of data for, well, I was going to 
say a variety of samples. 
So it, so it may be individual organisms 
or it could even be samples from the 
environment where there's no particular 
one organism in there but there's an 
entire population. 
Alright, so for a variety of uses these 
guys are are able to spit out the data. 
In oceanography, the regional scale nodes 
of the NSF Ocean Observatories Initiative 
is a project led here at UW. 
Ocean Observatories Initiative is a 
multi-institutional partnership. 
the regional scale nodes part is run at 
the University of Washington. 
So these, this project does involve 
laying, you know, a thousand kilometers 
of fiber optic cable on the seafloor. 
Connecting thousands of instruments in 
chemical, physical and biological 
thousands of chemical, biological and 
physical sensors. 
Including live video from the seafloor to 
measure to monitor volcanic activity. 
Okay, so again, the database the, the if 
not a relational database. 
[LAUGH]. 
The data sets and data infrastructure 
required to support this effort is 
significant and has motivated a lot of 
new research. 
Alright. 
in the information space there is a lot 
of science to be done on the web itself. 
And so, just the web, a single computer 
can read 30 to 35 megabytes per second 
from one disk, and so it would take about 
four months just to read the entire web. 
So new clusters of machines. 
So summing up a little bit eScience is 
about the analysis of data. 
So the automated or semi-automated 
extraction of knowledge from massive 
volumes of data. 
And so your main instrument for looking 
for answers is the, or the algorithms and 
the technology as oppose to direct 
inspection. 
There's just too much of it to look at it 
yourself. 
But it's not just a matter of volume as 
we'll talk about in the next segment. 
this is another link back to what's going 
in business. 
Right, there's these, there's this 
concept of big data and there's the three 
V's of big data that we'll talk about a 
little bit more next time. 
But let me just mention them here. 
Where volume, sort of the three V's are 
volume, variety, and velocity. 
They will deal about and I will give you 
the, where this stuffs came from in the 
next segment. 
Involving first just into the number of 
rows and the number of bytes at the 
serious scale. 
Variety is perhaps a number of columns 
and dimensions but you know in science 
for example lot of in allied sciences 
particularly you will have experiments 
that involve accessing multiple databases 
as well as multiple sensors. 
you're own data that you've collected and 
that of your colleagues and the 
integration task of putting all these 
data together is a significant model 
like. 
Even when the actual scale on the data is 
not necessarily all that bad, okay? 
So those are the complexity of the data. 
And then the velocity, you know, we saw 
what the large survey telescope that. 
You know although the, the, the scale 
itself is enormous, that fact that 40, 40 
terabytes are being collected every two 
days means that the infrastructure needs 
to keep up with that pace. 
And just transferring that data from the 
telescope facility to the data analysis 
facility is an engineering challenge. 
Okay? 
And you'll also see other V's here, with 
things like veracity. 
You know, can we actually trust this 
data? 
Okay, so a bit more of that next time. 
So to summarize here, science is in the 
midst of a generational shift from a data 
poor enterprise, where you can never, you 
know there's never enough data. 
To a data rich enterprise where there's 
so much of it you don't know what to do 
with it. 
And as a result, you know data analysis 
has replaced data acquisition as the new 
bottleneck to discovery. 
Right? 
So it's not the cost of going out and 
getting the data, it's the cost of 
actually analyzing the data you might 
already have. 
So this is fine, but what does this have 
to do with business, which is probably 
where a lot of you are coming from, and 
where your interests lie. 
Well, what we see is that business is 
beginning to look a lot like what's 
always been happening in science. 
So though, you know businesses of 
requiring data aggressively and keeping 
it around indefinitely, in case it 
becomes useful. 
They're beginning to hire people that 
have training and skill sets that look a 
lot like whats been important in science 
for a long time, especially in 
mathematical depth. 
And they're beginning to make decisions 
with this data that are very empirical, 
so we're always wanting to make up every 
decision with a a clear case based on 
data. 
And so for these reasons, I think you can 
take the lessons learned in science and 
apply them in business, and actually vice 
versa as well. 
The one thing where science is lagging 
behind business is in the adoption of 
technology. 
There's been, there's been 
proportionately a lot less spent on IT 
infrastructure in science than there has 
in business. 
And so there's, it's a great time for 
this, crosspollination of ideas between 
both fields. 
Okay, and so you know going back to this 
first slide I gave, e-science and 
data-science have essentially everything 
in common. 
So we might use examples interchangeably 
between the two. 
Okay.