[MUSIC]. 
Welcome back. 
So I want to talk about in this segment 
what this term data science actually 
means actually means. 
So, you know, you'll see these quotes 
around the web so we in Fortune Magazine 
about Data Science being the hot new gig 
and Tech and Hal Varian who was chief, 
Google's chief economist in the New York 
Times in 2009, which was a while ago now 
talked about statistics being the next 
sexy job and described it as the you 
know, the ability to take data to 
understand it, process it, and extract 
value from it and communicate it. 
That's going to be hugely important. 
Another person who's prolific In thinking 
and writing in this space is Mike 
Driscoll whose the CEO of a company 
called Metamarkets and he talks about 
there the data science there as it's 
practiced is as a cloacal view of it is a 
blend of Red-Bull-fueled hacking and 
espresso-inspired statistics. 
And maybe another quote of his is the 
Data sciences is the civil engineering of 
data. 
Who, you know, whose accolades persist as 
practical knowledge, as well as a 
theoretical understanding. 
And so there's balance between pragmatism 
and theory is something we'll come back 
to. 
So another perspective on this that you 
should be familiar with is this Venn 
diagram that made the rounds several 
years ago by Drew Conway. 
And what he, his point was that Data 
Science is probably the mix of three 
different sort of areas. 
One is hacking skills you know, 
programming expertise. 
Another is the academic view you know, 
the math and statistics knowledge. 
And then the third that he added is this 
notion of substantive expertise. 
And what he meant by this is kind of a 
deep investment with the data. 
So if you think about Your typical IT 
shop. 
They're typically building tools, for 
other people to use, to actually analyze 
the data, but they don't necessarily do 
the analysis themselves. 
And, in, you know, a data scientist, in 
contrast, may be a participant in tool 
building, but they're going to also, sort 
of, dive deeply into the data and do the 
analysis themselves. 
And then, that's one of the ways that I 
like to interpret Drew's blue bullet here 
of, of substituted expertise. 
Okay, and then he also sort of fills in 
the gaps between these, talking about 
that I'm not sure that I'd agree that 
this is necessarily traditional research, 
but perhaps it is, is applying the 
statistical knowledge to a particular 
domain is where research Comes in and at 
some deep theory plus some pragmatic 
programming makes you a machine learning 
expert. 
And then he sort of refers to this as the 
danger zone. 
Where you sort of know enough about the 
domain to be dangerous. 
And you know enough hacking to be 
dangerous but you don't know how to 
ground your analysis in. 
Proper theorem. 
And you know, some of my colleagues like 
to joke, the computer scientists don't 
understand error bars, right. 
And that's, that's maybe what they're 
referring to here, okay. 
So fine. 
So what do data scientists actually do? 
Well, some more quotes here from, you 
know, from EMC who is a company who 
acquired a company called Green Plum and 
have a Reason we have a pretty 
significant initiative in data science, 
both related to chronicle green plumb as 
well as other products. 
So we need to find the nuggets in data 
and then explain it to business leaders. 
And I like this, you know, and then 
explain it to business leaders. 
Something else we'll come back to that I 
mentioned previously is that 
communicating results is a critical part 
of this. 
process, it's not just getting the 
result. 
Okay, and then another, view of this, 
which is sort of interesting, is that 
data scie-, DJ Patil, talked about, data 
scientist tend to be hard scientist. 
Maybe coming from a physics background, 
who have a strong mathematical background 
and computing skills, and, you know, come 
from a discipline, in which survival 
depends on getting the most from the 
data, they're, really, used to, sort of, 
torturing the data, to extract every last 
ounce of value out of it. 
Out of it. 
This, you know, DJ has an applied math 
background so he's maybe coming from that 
perspective. 
So Mike Driscoll also talks about the 
three skills of data geeks which are 
statistics, this thing data munging and 
we'll kind of, you know, this funny word 
munging, we'll kind of return to this. 
You'll see a lot of this sort of 
colloquial Language and I'll give my 
perspective on what I think that tells us 
about the, the state of the world in Data 
Science. 
And but, what he means by this, as you 
can imagine, is sort of parsing data, and 
scraping data from the web, and 
converting to, between different file 
formats effeciently, and not getting hung 
up on these the kind of friction that you 
deal with when you are working with large 
and, and, and heterogeneous data sets. 
Alright? 
So a data scientist is someone who is 
very comfortable in that environment and 
is able to sort of work nimbly even when 
things aren't very clean. 
Okay. 
And then finally, visualization is 
another sexy skill, right? 
The ability to communicate the results 
through visualization, alright. 
So another quote now from Jeffrey Stanton 
who teaches a course in data science at 
Syracuse and has, was involved in one of 
the earlier programs in data science. 
Talks about the emerging area of work 
concerned with the collection, 
preparation, analysis, visualization, 
management and preservation of large 
collections of information. 
And I think one thing interesting about 
His perspective includes the word 
preservation. 
While the preparation the analytics and 
visualization are the three tenants that 
you see quite often is something that we 
like in this course as well. 
Jeffrey goes one step further and talks 
about preservations even after you're 
done communicating the results. 
What do you do with the data long term? 
Part of the reason is he's got a 
background in library science and 
information studies where they're very 
concerned with the curiation of data and 
so that's something we're probably not 
going to emphasize in this course but its 
definately part of the overall data life 
cycle if you will. 
Okay. 
So another quote from thinker in this 
space is Hillary Mason, the chief 
scientist at bit.ly, and so she says a 
data scientist is someone who can obtain, 
scrub explore, model and interpret data, 
you know, blending hacking, statistics 
and machine learning. 
So we saw that before in Drew Conway's 
diagram. 
There's blending. 
and data scientists are not only adept at 
working with data, but can appreciate 
data itself as a first-class product. 
And I think she's talking about there 
maybe is being able to organize the data 
and actually produce something that's 
usable by other people. 
Right? 
Okay. 
So having a quality data Resource a data 
asset that others can use to answer 
questions is one of the outputs of data 
science and then you know she talks about 
let's see scrubbing and we saw munging on 
the previous slide you'll see data jiu 
jitsu people will say you know this, 
this. 
The data scientist that I want to hire is 
really good at data jujitsu. 
And we don't, I, I, I, on, on, in a 
couple of segments I'll talk about, or 
the next segment I think I'll talk about, 
what I think this means. 
Okay, so to summarize what we talked 
about, there's perhaps three overarching 
tasks involved in data science. 
And those are, you know, preparing to run 
some sort of statistical analysis, 
actually running that statistical 
analysis, and then interpreting and 
communicating the results. 
And this phase here, this preparing to 
run a model, is where we see all that 
data munging, cleaning, manipulating, 
integrating, and so on. 
All right. 
So another view that I like to take is 
that data science is really about data 
products, Producing data products that 
you may use yourself or that others may 
use. 
Okay, and so, what do I mean by data 
products? 
Well this could be data driven 
applications. 
So if you think about a spell checker, 
alright this is not just a piece of code 
This is not just a piece of software that 
does something. 
This is only enabled by a dictionary of 
words and a dictionary of misspelled word 
actually. 
Okay and similarly a machine translation 
where you can type a sentence in French 
and have it automatically translated into 
Arabic. 
Relies on not just clever algorithms. 
But rather an enormous corpus of French 
texts and Arabic texts, okay. 
A second kind of data product is 
interactive visualizations. 
we saw this example with the Google flu 
application, and there's a suite of 
visualizations on the web that I'm not 
going to show right now called that are 
associate with the global burden of 
disease. 
And these are visualizations produced by 
the institute for. 
Health measures here at the University of 
Washington. 
And what I like about it is, it's an 
example of where they've done a lot of 
research. 
But then the output was not just a 
research paper, although they wrote 
plenty of those as well. 
It was these interactive visualizations 
that allowed you to explore the data as 
well. 
And so this, I think, captures this 
notion of producing, not just the answer 
Or not just a paper in, in perhaps this 
case, but a data product that is usable 
by others. 
Okay. 
And then finally, another kind of data 
product might be an online data, database 
of some kind that others can actually use 
to can query and answer their own 
questions. 
And so maybe there's not necessarily a 
visualization component, but the work 
that goes into producing these things, I 
would argue is part of data science. 
And so this. 
Captures some of the Enterprise data 
warehouse work and software and effort, 
of which there's a lot. 
And including business intelligence work 
and I'll, and in a couple of segments, 
I'll try to differentiate those two. 
Another example of an online database 
from science as opposed to business is 
the Sloan Digital Sky Survey, which I'll 
talk about in more detail in a couple of 
segments. 
So, again just to summarize here whats in 
red. 
Data science is not just about building 
data products. 
Rather it's about building data products, 
not just answering the questions once. 
And what data products are our assets, 
visual assets that empower others to use 
the data in new ways, and so they may 
help communicate results. 
For example, Nate Silver built these maps 
or they may empower others to do their 
own kind of analysis, say with a data 
warehouse, or with a visualization. 
Alright.