-: All right, before we start talking about data analytics,
let's talk a little bit about data literacy,
because in order to do data analytics,
you need to be data literate.
So what does data literacy mean?
Before I define it in explicit terms,
this requires data literacy to even understand.
This is not marks on a page and a burnt match.
I mean, it is, but this means something, right?
And you have to be data literate
in order to understand that this is,
this looks like a chart, this is a chart-like thing, okay?
The match is not just a match,
it represents data, a line.
Only a data literate person
would be even aware of that idea.
So what is a specific definition of data literacy?
It's the ability to read, work with,
analyze, and argue with data.
Okay, that's fine, this is a good definition,
it's from MIT.
And another way of thinking about this
a little bit simpler, I would argue,
is it's the ability to think about and do stuff with data.
Think about, look at data, hear data,
understand data, think about what it means,
and then do stuff,
meaning the ability to actually do data analytics
and all that other stuff.
And we all have varying degrees of data literacy, right?
Data scientists are up here
and a first grader is down here, okay?
So we wanna live somewhere in that spectrum,
hopefully closer to the top than the bottom.
But you don't have to be a data scientist to work with data.
Data literacy essentially requires some data fluency, right?
You speak the language of data.
You also have to have some statistics to analytical skills.
Again, not all the way up here,
but you know, somewhere in there.
And data literacy actually does require
visualization as well,
which is one of the reasons we're talking about it here.
Now, data literacy on an organizational level
also requires the democratization and ubiquity of data,
and the tools to access data, right?
It implies that an organization
has made data central to what it does,
and therefore everybody has access to it.
Now, one thing that's interesting is this,
this survey came out,
maybe three or four years ago, from Qlik.
They make data analytics, data visualization,
essentially business intelligence software.
And it found that 24% of business decision makers
consider themselves data literate.
Okay.
My data literacy hat
essentially makes my body tingle when I read this,
because this, even, this simple thing,
this one number I would argue,
from a data literacy standpoint,
is not the way to talk about this.
It's not that 24% are data literate.
The shocking statistic here
is that three out of four business decision makers
are not data literate.
They're data illiterate, right?
So how are they making data-driven decisions
if they're not data literate?
It makes absolutely no sense.
So data literacy, yes, is practical,
tangible, real skills and outcomes, yes,
but it's more than that, okay?
There's other stuff involved in data literacy.
It's understanding things like,
what do these numbers actually mean?
So this website dollarstreet.org
is a website where you can go up and look up,
in this case income, and get a very visceral understanding
of what different levels of income mean by looking at,
in this case, toothbrushes.
What does dental care, brushing your teeth look like
in Burundi versus Ukraine
based on, essentially, income level,
or at least correlated with income level?
You can look at other things as well.
Transportation, homes, all kinds of different,
using photography, a way of understanding
what do these numbers mean?
That's data literacy.
There's also this example from the book,
"Factfulness" by Hans Rosling.
You'll hear his name again.
And so another thing about data literacy is,
do the numbers make sense, okay?
So as an example here,
and by the way only 9% of Norwegian teachers
get this question correct
and only one in four World Economic Forum participants.
So this is kinda shocking, I would argue.
But here's the thing.
Look at the visual here.
World population from 8,000 BC to today.
So thousands and thousands of years ago,
there were essentially like zero people on Earth.
Very, very few, especially could compared to today,
you know, a few million, right?
And over time, thousands of years,
that remained static, okay?
And then eventually, the industrial revolution,
and then boom, skyrocketing population.
Okay, now if you zoom in on that timeline,
not looking at thousands of years
but maybe just the last, you know, several hundred years,
it's fairly steady,
and then it, you know, goes up, certainly,
not quite the same hockey stick but pretty dramatic.
And the question that Norwegian teachers get wrong is,
what's the world population gonna look like
in the year 2100?
And so this is an example where,
of course, we assume the line
is just gonna continue to go up the way it's been going.
We tend to do that.
That's one of the mistakes that we make as humans.
We extrapolate numbers and assume
it's gonna continue to be the same.
Turns out, based on demography
and statistical analysis and all kinds of good stuff,
it looks like the population is actually gonna peak
by 2100 at around 11 billion people.
So essentially it's gonna keep going up,
but it's already gonna be curving,
and then eventually start coming back down soon after that.
You need data literacy to understand that.
You need to literally know the data
to really know that number.
But we just make mistakes and assumptions.
A good example of that is this.
Yesterday I had zero spouses.
Today, I got married.
Today I have one spouse.
How many spouses will I have by the end of the year?
Oh my goodness, right?
Doesn't work that way.
So in some cases it's obvious.
In other cases, it isn't.
You have to be data literate to,
even if you can't predict the actual answer,
to know the questions to ask
so you don't make silly mistakes, okay?
Data literacy also requires understanding things
like data, quality issues, sample quality.
Is it a big enough sample?
What is the makeup of the sample?
Methodology?
What method did you use a Twitter poll pool, excuse me,
Twitter poll to make your decisions?
Or did you actually do a real statistical analysis
in a randomized, you know, double blind study,
et cetera, et cetera, et cetera.
And by the way, is this a useful analysis, right?
A national poll to do predictions
of U.S. presidential elections?
Not so useful, right?
Data literacy also requires understanding the difference
between correlation and causation, right?
You've probably heard the phrase
correlation doesn't equal causation.
All right, well, what does that exactly mean?
Well, let's talk about what correlation is.
There's something called the correlation coefficient, or R,
which is measured on a scale from negative one to one.
That is a statistical measure
of whether variables are correlated,
meaning they're moving in the same direction,
at the same time with each other.
Closer to a one or a negative one,
based on that statistical measure
means it's a stronger correlation, okay?
Either positive or negative.
As one number goes up, the other one goes up.
Or as one number goes up, the other one comes down.
Maybe a little bit too much detail.
Don't worry too much about that except to understand this.
You see a scatter plot?
A bunch of random dots?
Clearly, these are not correlated.
I don't need the R value to understand are these correlated?
No, they're not.
It looks like a random collection of dots.
These look pretty nicely correlated.
As one number goes up to the right,
another one goes up, meaning up on the Y-axis.
Clearly they're sort of clustered and moving in tandem.
This data set is very correlated
with a couple of outliers maybe, right?
So I imagine that the R value is gonna be much higher,
much closer to one on this one
than on the random dots, right?
But the thing is, correlation does not equal causation.
even in that previous example.
It's very heavily correlated,
but it doesn't prove that one causes the other.
And this being a classic example.
The divorce rate in Maine is statistically correlated
with the consumption of margarine.
It's kind of weird one, it's true.
But I don't think we can prove
that the consumption of margarine increases divorce
or vice versa, okay?
That might be true,
ut we haven't proven that in the data.
Another thing, data literate people understand
is the difference between signal and noise, right?
What is, you know, a useful information
and what's just a distraction?
So what's important versus maybe interesting?
And so this comes in all kinds of different forms.
Essentially, the only way to detect this,
to really think about it
is to understand what is the question
you're really asking of your data?
Data literate people know
what they're asking of their data.
Data illiterate people just sort of look at the numbers
and evaluate them without that context,
which isn't very helpful.
It's very, very, very important also to leave bias behind.
And we're gonna talk more about bias in a moment.
But before we do that,
it's also worth knowing
that data literacy is also about context.
Numbers are meaningless without the context.
So this example,
if we look at this guy, he has good form,
he looks like he knows what he's doing.
But once we have the context,
we see that things aren't quite what they seem, right?
The numbers themselves may be meaningless
or may mean the opposite
of what they seem to appear once we introduce context.
So what kinds of context do we need for data analytics?
Answers to questions like,
are these numbers good or bad, right?
And good or bad, compared to what, by the way, okay?
Should we show a rate instead of the actual values, right?
I'm gonna throw a number out there.
87, is that good or bad?
And out of how many, right?
So 87 on its own means nothing.
But 87 out of 10 million, that's a tiny number.
87 out of 90 is a huge number, right?
So understanding that as a rate,
whether it's a percentage, or it's per capita,
or it's a per 1,000, or per 100,000,
that's one of the most important things
we do in data analytics,
is we convert values into rates and we evaluate the rates.
Instead of, sometimes, or at least in addition to
the actual values themselves.
Other questions we ask ourselves as data literate people is
compared to what, which I already said.
Does time matter?
Does geography matter?
Questions like, okay, someone's showing me the average,
but maybe I should be looking at the median,
or the mode?
Anyone remember what the mode is?
Most people forget that one.
It's a good one. (chuckles)
Something to keep in mind.
We almost never refer to it by that name.
Long story short, you have to be data literate
to even know to ask that question.
So rates.
Using ratios, using indexing,
other ways of converting the raw values into stuff
by essentially dividing them by other numbers.
An example of that is that in 2015,
there were 9.9 million people
living in extreme poverty around the world.
That's a big number, right?
Until you learn that it was 35.9 people,
million people rather, in 1990.
In other words, it it decreased by 72% in those,
what, 25 years or so?
That's pretty interesting.
That's a big deal.
Now, we didn't factor in population, okay?
In 2015, the population was 7.34 billion people
versus 1990, it was 5.28 billion.
So it wasn't a 72% drop.
If we look at the per capita rate,
it's actually more than an 80% decrease.
That's a big deal.
Only a data literate person could have expressed that,
that that difference, essentially.
So let's talk about bias for a moment.
It's a very important topic.
We're not gonna talk about it much at all on this course,
but I want to touch on it briefly here today.
There are a lot of cognitive biases, okay?
Humans have deeply flawed psychologies,
brains, all kinds of things.
We make mistakes all the time.
And there are a bunch of them that apply to data.
Just gonna talk about a few of them.
Number one is something called selection bias.
I make a mistake in who I select to study, okay?
As an example, I did a poll,
asking, do you love ice hockey?
Yes or no?
And I asked five people,
sample size, that's not enough people.
And I asked them,
people who are coming out of a hockey game,
that's my selection bias, right? (chuckles)
So it turns out I tell the world
100% of people love ice hockey
because I asked five people coming outta a hockey game
maybe not quite so accurate, okay?
Recall bias.
Okay, recall bias,
we think we remember things a certain way,
turns out we're wrong.
There was a study that was done.
This is a real result.
Women with breast cancer
ate higher fat diets when they were young,
was the result of this study.
Okay, maybe that's true.
Turned out it's not true.
Or at least they couldn't prove it was true.
What they realized was happening
is that women with breast cancer
answering a survey question
about what they did when they were younger,
their recall was bad.
Essentially, they assumed, because they had cancer,
they must have done something wrong.
So they were much more likely to say,
yes, I ate a lot of high fat diet,
high fat food when I was young,
compared to women who didn't have breast cancer.
Purely a recall bias problem, okay?
My personal example,
when I was in college many, many, many years ago,
I distinctly remember watching Seinfeld
in my freshman dorm room with my roommates.
I was wrong.
Seinfeld didn't launch
until I was a senior in college, okay?
So recall bias is a big one.
Another one is called survivor bias.
This is the most famous visual
always used to describe survivor bias.
So if you look at this visual, this is a plane,
World War II plane.
And the story is, and I think it's a true story,
that the planes would come back
and the maintenance guys would look at the planes,
and the planes had bullet holes all over them,
as you can see.
And this was like the overall pattern of bullet holes,
looking at however many planes they looked at.
And so they said, hey, you know what we should do
is we should add armor to the tips of the wings,
and to that place behind the cockpit,
and the tail where all those bullet holes are,
and hey, we'll be in good shape, right?
Exactly wrong.
Why?
This is survivor bias,
meaning these are the planes that made it back.
These are the planes that essentially proving the point
that bullets can hit the tips of the wings
and the tail, et cetera,
and you can still fly,
otherwise they wouldn't have made it back.
So the essentially, the answer here is,
don't put 'em there,
Put 'em where you don't see the bullet holes,
'cause those are the planes that didn't make it home.
Cockpit, engines, et cetera, okay?
Key biases also include confirmation bias.
And my favorite quote, I use this one all the time,
if you torture your data long enough, it will confess.
Classic problem in data analytics.
You know, I didn't find anything interesting in this data,
so I kept going until I found something interesting.
Now that one isn't necessarily a bad thing
because if you find nothing in your data,
sometimes the answer is there's nothing going on here.
And you gotta be ready for that and be okay with that.
Sometimes you just, you do have to keep digging.
You have to sort of go down that rabbit hole and keep going.
And eventually maybe you'll find something.
But you gotta find a balance there.
More importantly,
I found the opposite of what I was looking for.
Well, I'm gonna keep looking
until I find what my boss asked me to find, right?
You always wanna avoid that.
And last, but not least,
not because this is the most important bias
or because these are the only biases that affect data,
but I just like this one.
There's one called Simpson's Paradox.
I love this one.
It's a very good one to be aware of.
And of course, it's not The Simpsons, it's this.
Basic idea here is that some data sets,
the overall trend, pattern,
whatever in the data may look one way.
So in this case, the trend, the overall trend line
goes down into the right for this entire dataset.
But if you look at the dataset in segments,
you may find that the segments
can clearly go in very different directions.
And in this case, both of the segments
go in the opposite direction.
They have completely opposite trends
from that overall combined trend.
Something to be very thoughtful of.
You can find interesting insights in your data
if you segment it in certain ways.
Not to mention, you may find your segments
are literally the opposite story
of what you might see overall.
Okay, data literacy,
it's a mindset as much as anything else.
It's about critical thinking.
It's about being a little bit paranoid,
being aware of certain things,
and just asking a million questions, okay?
If you do that, you'll find stories in your data.